fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f71fd916fcc0b6fd5407ff0c3b8ac492320273d7

26,028

py

Python

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

40

2016-03-09T03:03:08.000Z

2021-09-11T21:44:12.000Z

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

1

2018-05-08T13:45:43.000Z

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

17

2016-03-17T14:57:11.000Z

2021-06-04T16:24:48.000Z

# Copyright 2016 Mandiant, A FireEye Company # Authors: Brian Jones # License: Apache 2.0 ''' Model classes for "Relational Learning with TensorFlow" tutorial ''' import numpy as np import tensorflow as tf from .util import ContrastiveTrainingProvider def least_squares_objective(output, target, add_bias=True): ''' Creates final model output and loss for least squares objective Args: output: Model output target: Training target placeholder add_bias: If True, a bias Variable will be added to the output Returns: tuple (final output, loss) ''' y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias loss = tf.reduce_sum(tf.square(y - target)) return y, loss def logistic_objective(output, target, add_bias=True): ''' Creates final model output and loss for logistic objective Args: output: Model output target: Training target placeholder add_bias: If True, a bias Variable will be added to the output Returns: tuple (final output, loss) ''' y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias sig_y = tf.clip_by_value(tf.sigmoid(y), 0.001, 0.999) # avoid NaNs loss = -tf.reduce_sum(target*tf.log(sig_y) + (1-target)*tf.log(1-sig_y)) return sig_y, loss def ranking_margin_objective(output, margin=1.0): ''' Create final model output and loss for pairwise ranking margin objective Loss for single pair (f(p), f(n)) = [margin - f(p) + f(n)]+ This only works when given model output on alternating positive/negative pairs: [pos,neg,pos,neg,...]. TODO: check target placeholder at runtime to make sure this is the case? Args: output: Model output margin: The margin value for the pairwise hinge loss Returns: tuple (final output, loss) ''' y_pairs = tf.reshape(output, [-1,2]) # fold: 1 x n -> [n/2 x 2] pos_scores, neg_scores = tf.split(1, 2, y_pairs) # separate pairs hinge_losses = tf.nn.relu(margin - pos_scores + neg_scores) total_hinge_loss = tf.reduce_sum(hinge_losses) return output, total_hinge_loss def sparse_maxnorm_update(var_matrix, indices, maxnorm=1.0): '''Sparse update operation that ensures selected rows in var_matrix do not have a Euclidean norm greater than maxnorm. Rows that exceed it are scaled to length. Args: var_matrix: 2D mutable tensor (Variable) to operate on indices: 1D tensor with the row indices to constrain maxnorm: the maximum Euclidean norm Returns: An operation that will update var_matrix when run in a Session ''' selected_rows = tf.nn.embedding_lookup(var_matrix, indices) row_norms = tf.sqrt(tf.reduce_sum(tf.square(selected_rows), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = selected_rows * tf.expand_dims(scaling, 1) return tf.scatter_update(var_matrix, indices, scaled) def dense_maxnorm_update(var_matrix, maxnorm=1.0): '''Dense update operation that ensures all rows in var_matrix do not have a Euclidean norm greater than maxnorm. Rows that exceed it are scaled to length. Args: var_matrix: 2D mutable tensor (Variable) to operate on maxnorm: the maximum Euclidean norm Returns: An operation that will update var_matrix when run in a Session ''' row_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = var_matrix * tf.expand_dims(scaling, 1) return tf.assign(var_matrix, scaled) def dense_maxnorm(var_matrix, maxnorm=1.0): '''Similar to dense_maxnorm_update(), except this returns a new Tensor instead of an operation that modifies var_matrix. Args: var_matrix: 2D tensor (Variable) maxnorm: the maximum Euclidean norm Returns: A new tensor where all rows have been scaled as necessary ''' axis_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(axis_norms, maxnorm) return var_matrix * tf.expand_dims(scaling, 1) class BaseModel(object): ''' Base class for embedding-based relational learning models that use maxnorm regularization. Subclasses must implement _create_model() and populate self.train_step, and can optionally populate self.post_step for post-processing. Note: When model_type is 'ranking_margin', the mini-batch provider returned by _create_batch_provider() must provide instances in alternating pos/neg pairs: [pos, neg, pos, neg, ...]. This is satisfied when using ContrastiveTrainingProvider; be careful if you use a different one. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) TODO: add support for other regularizers like L2 ''' def __init__(self, embedding_size, maxnorm=1.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): self.embedding_size = embedding_size self.maxnorm = maxnorm self.batch_pos_cnt = batch_pos_cnt self.max_iter = max_iter self.model_type = model_type self.add_bias = add_bias if opt is None: opt = tf.train.AdagradOptimizer(1.0) self.opt = opt self.sess = None self.train_step = None self.post_step = None self.graph = tf.Graph() with self.graph.as_default(): self.head_input = tf.placeholder(tf.int32, shape=[None]) self.rel_input = tf.placeholder(tf.int32, shape=[None]) self.tail_input = tf.placeholder(tf.int32, shape=[None]) self.target = tf.placeholder(tf.float32, shape=[None]) def _create_model(self, train_triples): ''' Subclasses must build Graph and set self.train_step ''' raise Exception('subclass must implement') def _create_batch_provider(self, train_triples): ''' Default implementation ''' return ContrastiveTrainingProvider(train_triples, self.batch_pos_cnt) def _create_output_and_loss(self, raw_output): if self.model_type == 'least_squares': return least_squares_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'logistic': return logistic_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'ranking_margin': return ranking_margin_objective(raw_output, 1.0) else: raise Exception('Unknown model_type') def _norm_constraint_op(self, var_matrix, row_indices, maxnorm): ''' Args: var_matrix: A 2D Tensor holding the vectors to constrain (in rows) row_indices: The rows in var_tensor that are being considered for constraint application (typically embedding vectors for entities observed for a minibatch of training data). These will be used for a sparse variable update operation if the chosen optimizer only modified these entries. Otherwise a dense operation is used and row_indices are ignored. maxnorm: The maximum Euclidean norm for the rows in var_tensor Returns: An operation which will apply the constraints when run in a Session ''' # Currently, TF optimizers do not update variables with zero gradient # except AdamOptimizer if isinstance(self.opt, tf.train.AdamOptimizer): return dense_maxnorm_update(var_matrix, maxnorm) else: return sparse_maxnorm_update(var_matrix, row_indices, maxnorm) def embeddings(self): ''' Subclass should override this if it uses different embedding variables Returns: A list of pairs: [(embedding name, embedding 2D Tensor)] ''' return [('entity', self.entity_embedding_vars), ('rel', self.rel_embedding_vars)] def create_feed_dict(self, triples, labels=None, training=False): ''' Create a TensorFlow feed dict for relationship triples Args: triples: A numpy integer array of relationship triples, where each row contains [head idx, relationship idx, tail idx] labels: (optional) A label array for triples training: (optional) A flag indicating whether the feed dict is for training or test purposes. Useful for things like dropout where a dropout_probability variable is set differently in the two contexts. ''' feed_dict = {self.head_input: triples[:, 0], self.rel_input: triples[:, 1], self.tail_input: triples[:, 2]} if labels is not None: feed_dict[self.target] = labels return feed_dict def close(self): ''' Closes the TensorFlow Session object ''' self.sess.close(); def fit(self, train_triples, step_callback=None): ''' Trains the model on relationship triples Args: train_triples: A numpy integer array of relationship triples, where each row of contains [head idx, relationship idx, tail idx] step_callback: (optional) A function that will be called before each optimization step, step_callback(iteration, feed_dict) ''' if self.sess is not None: self.sess.close() self.sess = tf.Session(graph=self.graph) with self.graph.as_default(): self._create_model(train_triples) self.sess.run(tf.initialize_all_variables()) batch_provider = self._create_batch_provider(train_triples) for i in range(self.max_iter): batch_triples, batch_labels = batch_provider.next_batch() feed_dict = self.create_feed_dict(batch_triples, batch_labels, training=True) if step_callback: keep_going = step_callback(i, feed_dict) if not keep_going: break self.sess.run(self.train_step, feed_dict) if self.post_step is not None: self.sess.run(self.post_step, feed_dict) def predict(self, triples): ''' Runs a trained model on the supplied relationship triples. fit() must be called before calling this function. Args: triples: A numpy integer array of relationship triples, where each row of contains [head idx, relationship idx, tail idx] ''' feed_dict = self.create_feed_dict(triples, training=False) return self.sess.run(self.output, feed_dict=feed_dict) class Contrastive_CP(BaseModel): ''' Model with a scoring function based on CANDECOMP/PARAFAC tensor decomposition. Optimization differs, however, in the use of maxnorm regularization and contrastive negative sampling. Score for (head i, rel k, tail j) triple is: h_i^T * diag(r_k) * t_j, where h_i and t_j are embedding vectors for the head and tail entities, and r_k is an embedding vector for the relationship type. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Kolda, Tamara G., and Brett W. Bader. "Tensor decompositions and applications." SIAM review 51.3 (2009): 455-500. ''' def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes head_cnt = len(set(train_triples[:,0])) rel_cnt = len(set(train_triples[:,1])) tail_cnt = len(set(train_triples[:,2])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding matrices for entities and relationship types head_init = tf.truncated_normal([head_cnt, self.embedding_size], stddev=init_sd) rel_init = tf.truncated_normal([rel_cnt, self.embedding_size], stddev=init_sd) tail_init = tf.truncated_normal([tail_cnt, self.embedding_size], stddev=init_sd) if self.maxnorm is not None: # Ensure maxnorm constraints are initially satisfied head_init = dense_maxnorm(head_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) tail_init = dense_maxnorm(tail_init, self.maxnorm) self.head_embedding_vars = tf.Variable(head_init) self.rel_embedding_vars = tf.Variable(rel_init) self.tail_embedding_vars = tf.Variable(tail_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.head_embedding_vars, self.head_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) tail_embed = tf.nn.embedding_lookup(self.tail_embedding_vars, self.tail_input) # Model output raw_output = tf.reduce_sum(tf.mul(tf.mul(head_embed, rel_embed), tail_embed), 1) self.output, self.loss = self._create_output_and_loss(raw_output) # Optimization self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: # Post-processing to limit embedding vars to L2 ball head_constraint = self._norm_constraint_op(self.head_embedding_vars, tf.unique(self.head_input)[0], self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, tf.unique(self.rel_input)[0], self.maxnorm) tail_constraint = self._norm_constraint_op(self.tail_embedding_vars, tf.unique(self.tail_input)[0], self.maxnorm) self.post_step = [head_constraint, rel_constraint, tail_constraint] def _create_batch_provider(self, train): # CP treats head and tail entities separately return ContrastiveTrainingProvider(train, self.batch_pos_cnt, separate_head_tail=True) def embeddings(self): ''' Returns: A list of pairs: [(embedding name, embedding 2D Tensor)] ''' return [('head', self.head_embedding_vars), ('tail', self.head_embedding_vars), ('rel', self.rel_embedding_vars)] class Bilinear(BaseModel): ''' Model with a scoring function based on the bilinear formulation of RESCAL. Optimization differs, however, in the use of maxnorm regularization and contrastive negative sampling. Score for (head i, rel k, tail j) triple is: e_i^T * R_k * e_j where e_i and e_j are D-dimensional embedding vectors for the head and tail entities, and R_k is a (D x D) matrix for the relationship type acting as a bilinear operator. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors rel_maxnorm_mult: Multiplier for the maxnorm threshold used for relationship embeddings. Example: If maxnorm=2.0 and rel_maxnorm_mult=4.0, then the maxnorm constrain for relationships will be 2.0 * 4.0 = 8.0. batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Nickel, Maximilian, Volker Tresp, and Hans-Peter Kriegel. "A three-way model for collective learning on multi-relational data." Proceedings of the 28th international conference on machine learning (ICML-11). 2011. ''' def __init__(self, embedding_size, maxnorm=1.0, rel_maxnorm_mult=3.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): super(Bilinear, self).__init__( embedding_size=embedding_size, maxnorm=maxnorm, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type=model_type, opt=opt) self.rel_maxnorm_mult = rel_maxnorm_mult def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding variables for all entities and relationship types entity_embedding_shape = [entity_cnt, self.embedding_size] # Relationship embeddings will be stored in flattened format to make # applying maxnorm constraints easier rel_embedding_shape = [rel_cnt, self.embedding_size * self.embedding_size] entity_init = tf.truncated_normal(entity_embedding_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_embedding_shape, stddev=init_sd) if self.maxnorm is not None: # Ensure maxnorm constraints are initially satisfied entity_init = dense_maxnorm(entity_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) # Reshape rel_embed into square D x D matrices rel_embed_square = tf.reshape(rel_embed, (-1, self.embedding_size, self.embedding_size)) # Reshape head_embed and tail_embed to be suitable for the matrix multiplication head_embed_row = tf.expand_dims(head_embed, 1) # embeddings as row vectors tail_embed_col = tf.expand_dims(tail_embed, 2) # embeddings as column vectors head_rel_mult = tf.batch_matmul(head_embed_row, rel_embed_square) # Output needs a squeeze into a 1d vector raw_output = tf.squeeze(tf.batch_matmul(head_rel_mult, tail_embed_col)) self.output, self.loss = self._create_output_and_loss(raw_output) # Optimization self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: # Post-processing to limit embedding vars to L2 ball rel_maxnorm = self.maxnorm * self.rel_maxnorm_mult unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] unique_rel_indices = tf.unique(self.rel_input)[0] entity_constraint = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, unique_rel_indices, rel_maxnorm) self.post_step = [entity_constraint, rel_constraint] class TransE(BaseModel): ''' TransE: Translational Embeddings Model Score for (head i, rel k, tail j) triple is: d(e_i + t_k, e_i) where e_i and e_j are D-dimensional embedding vectors for the head and tail entities, t_k is a another D-dimensional vector acting as a translation, and d() is a dissimilarity function like Euclidean distance. Optimization is performed uing SGD on ranking margin loss between contrastive training pairs. Entity embeddings are contrained to lie within the unit L2 ball, relationship vectors are left unconstrained. Args: embedding_size: Embedding vector length batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform dist: Distance function used in loss: 'euclidean': sqrt(sum((x - y)^2)) 'sqeuclidean': squared Euclidean, sum((x - y)^2) 'manhattan': sum of absolute differences, sum(|x - y|) margin: Margin parameter for parwise ranking hinge loss opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Bordes, Antoine, et al. "Translating embeddings for modeling multi-relational data." Advances in Neural Information Processing Systems. 2013. ''' def __init__(self, embedding_size, batch_pos_cnt=100, max_iter=1000, dist='euclidean', margin=1.0, opt=None): super(TransE, self).__init__(embedding_size=embedding_size, maxnorm=1.0, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type='ranking_margin', opt=opt) self.dist = dist self.margin = margin self.EPS = 1e-3 # for sqrt gradient when dist='euclidean' def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding variables entity_var_shape = [entity_cnt, self.embedding_size] rel_var_shape = [rel_cnt, self.embedding_size] entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd) # Ensure maxnorm constraints are initially satisfied entity_init = dense_maxnorm(entity_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) # Relationship vector acts as a translation in entity embedding space diff_vec = tail_embed - (head_embed + rel_embed) # negative dist so higher scores are better (important for pairwise loss) if self.dist == 'manhattan': raw_output = -tf.reduce_sum(tf.abs(diff_vec), 1) elif self.dist == 'euclidean': # +eps because gradients can misbehave for small values in sqrt raw_output = -tf.sqrt(tf.reduce_sum(tf.square(diff_vec), 1) + self.EPS) elif self.dist == 'sqeuclidean': raw_output = -tf.reduce_sum(tf.square(diff_vec), 1) else: raise Exception('Unknown distance type') # Model output self.output, self.loss = ranking_margin_objective(raw_output, self.margin) # Optimization with postprocessing to limit embedding vars to L2 ball self.train_step = self.opt.minimize(self.loss) unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] self.post_step = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm)

47.67033

96

0.643807

import numpy as np import tensorflow as tf from .util import ContrastiveTrainingProvider def least_squares_objective(output, target, add_bias=True): y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias loss = tf.reduce_sum(tf.square(y - target)) return y, loss def logistic_objective(output, target, add_bias=True): y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias sig_y = tf.clip_by_value(tf.sigmoid(y), 0.001, 0.999) loss = -tf.reduce_sum(target*tf.log(sig_y) + (1-target)*tf.log(1-sig_y)) return sig_y, loss def ranking_margin_objective(output, margin=1.0): y_pairs = tf.reshape(output, [-1,2]) pos_scores, neg_scores = tf.split(1, 2, y_pairs) hinge_losses = tf.nn.relu(margin - pos_scores + neg_scores) total_hinge_loss = tf.reduce_sum(hinge_losses) return output, total_hinge_loss def sparse_maxnorm_update(var_matrix, indices, maxnorm=1.0): selected_rows = tf.nn.embedding_lookup(var_matrix, indices) row_norms = tf.sqrt(tf.reduce_sum(tf.square(selected_rows), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = selected_rows * tf.expand_dims(scaling, 1) return tf.scatter_update(var_matrix, indices, scaled) def dense_maxnorm_update(var_matrix, maxnorm=1.0): row_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = var_matrix * tf.expand_dims(scaling, 1) return tf.assign(var_matrix, scaled) def dense_maxnorm(var_matrix, maxnorm=1.0): axis_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(axis_norms, maxnorm) return var_matrix * tf.expand_dims(scaling, 1) class BaseModel(object): def __init__(self, embedding_size, maxnorm=1.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): self.embedding_size = embedding_size self.maxnorm = maxnorm self.batch_pos_cnt = batch_pos_cnt self.max_iter = max_iter self.model_type = model_type self.add_bias = add_bias if opt is None: opt = tf.train.AdagradOptimizer(1.0) self.opt = opt self.sess = None self.train_step = None self.post_step = None self.graph = tf.Graph() with self.graph.as_default(): self.head_input = tf.placeholder(tf.int32, shape=[None]) self.rel_input = tf.placeholder(tf.int32, shape=[None]) self.tail_input = tf.placeholder(tf.int32, shape=[None]) self.target = tf.placeholder(tf.float32, shape=[None]) def _create_model(self, train_triples): raise Exception('subclass must implement') def _create_batch_provider(self, train_triples): return ContrastiveTrainingProvider(train_triples, self.batch_pos_cnt) def _create_output_and_loss(self, raw_output): if self.model_type == 'least_squares': return least_squares_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'logistic': return logistic_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'ranking_margin': return ranking_margin_objective(raw_output, 1.0) else: raise Exception('Unknown model_type') def _norm_constraint_op(self, var_matrix, row_indices, maxnorm): if isinstance(self.opt, tf.train.AdamOptimizer): return dense_maxnorm_update(var_matrix, maxnorm) else: return sparse_maxnorm_update(var_matrix, row_indices, maxnorm) def embeddings(self): return [('entity', self.entity_embedding_vars), ('rel', self.rel_embedding_vars)] def create_feed_dict(self, triples, labels=None, training=False): feed_dict = {self.head_input: triples[:, 0], self.rel_input: triples[:, 1], self.tail_input: triples[:, 2]} if labels is not None: feed_dict[self.target] = labels return feed_dict def close(self): self.sess.close(); def fit(self, train_triples, step_callback=None): if self.sess is not None: self.sess.close() self.sess = tf.Session(graph=self.graph) with self.graph.as_default(): self._create_model(train_triples) self.sess.run(tf.initialize_all_variables()) batch_provider = self._create_batch_provider(train_triples) for i in range(self.max_iter): batch_triples, batch_labels = batch_provider.next_batch() feed_dict = self.create_feed_dict(batch_triples, batch_labels, training=True) if step_callback: keep_going = step_callback(i, feed_dict) if not keep_going: break self.sess.run(self.train_step, feed_dict) if self.post_step is not None: self.sess.run(self.post_step, feed_dict) def predict(self, triples): feed_dict = self.create_feed_dict(triples, training=False) return self.sess.run(self.output, feed_dict=feed_dict) class Contrastive_CP(BaseModel): def _create_model(self, train_triples): head_cnt = len(set(train_triples[:,0])) rel_cnt = len(set(train_triples[:,1])) tail_cnt = len(set(train_triples[:,2])) init_sd = 1.0 / np.sqrt(self.embedding_size) head_init = tf.truncated_normal([head_cnt, self.embedding_size], stddev=init_sd) rel_init = tf.truncated_normal([rel_cnt, self.embedding_size], stddev=init_sd) tail_init = tf.truncated_normal([tail_cnt, self.embedding_size], stddev=init_sd) if self.maxnorm is not None: head_init = dense_maxnorm(head_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) tail_init = dense_maxnorm(tail_init, self.maxnorm) self.head_embedding_vars = tf.Variable(head_init) self.rel_embedding_vars = tf.Variable(rel_init) self.tail_embedding_vars = tf.Variable(tail_init) head_embed = tf.nn.embedding_lookup(self.head_embedding_vars, self.head_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) tail_embed = tf.nn.embedding_lookup(self.tail_embedding_vars, self.tail_input) raw_output = tf.reduce_sum(tf.mul(tf.mul(head_embed, rel_embed), tail_embed), 1) self.output, self.loss = self._create_output_and_loss(raw_output) self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: head_constraint = self._norm_constraint_op(self.head_embedding_vars, tf.unique(self.head_input)[0], self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, tf.unique(self.rel_input)[0], self.maxnorm) tail_constraint = self._norm_constraint_op(self.tail_embedding_vars, tf.unique(self.tail_input)[0], self.maxnorm) self.post_step = [head_constraint, rel_constraint, tail_constraint] def _create_batch_provider(self, train): return ContrastiveTrainingProvider(train, self.batch_pos_cnt, separate_head_tail=True) def embeddings(self): return [('head', self.head_embedding_vars), ('tail', self.head_embedding_vars), ('rel', self.rel_embedding_vars)] class Bilinear(BaseModel): def __init__(self, embedding_size, maxnorm=1.0, rel_maxnorm_mult=3.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): super(Bilinear, self).__init__( embedding_size=embedding_size, maxnorm=maxnorm, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type=model_type, opt=opt) self.rel_maxnorm_mult = rel_maxnorm_mult def _create_model(self, train_triples): entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) entity_embedding_shape = [entity_cnt, self.embedding_size] rel_embedding_shape = [rel_cnt, self.embedding_size * self.embedding_size] entity_init = tf.truncated_normal(entity_embedding_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_embedding_shape, stddev=init_sd) if self.maxnorm is not None: entity_init = dense_maxnorm(entity_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) rel_embed_square = tf.reshape(rel_embed, (-1, self.embedding_size, self.embedding_size)) head_embed_row = tf.expand_dims(head_embed, 1) tail_embed_col = tf.expand_dims(tail_embed, 2) head_rel_mult = tf.batch_matmul(head_embed_row, rel_embed_square) raw_output = tf.squeeze(tf.batch_matmul(head_rel_mult, tail_embed_col)) self.output, self.loss = self._create_output_and_loss(raw_output) self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: rel_maxnorm = self.maxnorm * self.rel_maxnorm_mult unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] unique_rel_indices = tf.unique(self.rel_input)[0] entity_constraint = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, unique_rel_indices, rel_maxnorm) self.post_step = [entity_constraint, rel_constraint] class TransE(BaseModel): def __init__(self, embedding_size, batch_pos_cnt=100, max_iter=1000, dist='euclidean', margin=1.0, opt=None): super(TransE, self).__init__(embedding_size=embedding_size, maxnorm=1.0, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type='ranking_margin', opt=opt) self.dist = dist self.margin = margin self.EPS = 1e-3 def _create_model(self, train_triples): entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) entity_var_shape = [entity_cnt, self.embedding_size] rel_var_shape = [rel_cnt, self.embedding_size] entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd) entity_init = dense_maxnorm(entity_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) diff_vec = tail_embed - (head_embed + rel_embed) if self.dist == 'manhattan': raw_output = -tf.reduce_sum(tf.abs(diff_vec), 1) elif self.dist == 'euclidean': raw_output = -tf.sqrt(tf.reduce_sum(tf.square(diff_vec), 1) + self.EPS) elif self.dist == 'sqeuclidean': raw_output = -tf.reduce_sum(tf.square(diff_vec), 1) else: raise Exception('Unknown distance type') self.output, self.loss = ranking_margin_objective(raw_output, self.margin) self.train_step = self.opt.minimize(self.loss) unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] self.post_step = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm)

true

f71fd969d3ac6dc91ff8442595549e245b3a9430

1,059

py

Python

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

6

2021-09-13T17:20:49.000Z

2022-02-09T04:31:47.000Z

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

21

2021-09-20T00:56:54.000Z

2022-03-22T01:12:12.000Z

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

9

2021-09-13T17:54:04.000Z

2022-03-15T08:38:35.000Z

from dataclasses import dataclass from typing import List from salvia.types.blockchain_format.sized_bytes import bytes32 from salvia.types.blockchain_format.program import Program from salvia.util.ints import uint64 # This class is supposed to correspond to a CREATE_COIN condition @dataclass(frozen=True) class Payment: puzzle_hash: bytes32 amount: uint64 memos: List[bytes] def as_condition_args(self) -> List: return [self.puzzle_hash, self.amount, self.memos] def as_condition(self) -> Program: return Program.to([51, *self.as_condition_args()]) def name(self) -> bytes32: return self.as_condition().get_tree_hash() @classmethod def from_condition(cls, condition: Program) -> "Payment": python_condition: List = condition.as_python() puzzle_hash, amount = python_condition[1:3] memos: List[bytes] = [] if len(python_condition) > 3: memos = python_condition[3] return cls(bytes32(puzzle_hash), uint64(int.from_bytes(amount, "big")), memos)

31.147059

86

0.705382

from dataclasses import dataclass from typing import List from salvia.types.blockchain_format.sized_bytes import bytes32 from salvia.types.blockchain_format.program import Program from salvia.util.ints import uint64 @dataclass(frozen=True) class Payment: puzzle_hash: bytes32 amount: uint64 memos: List[bytes] def as_condition_args(self) -> List: return [self.puzzle_hash, self.amount, self.memos] def as_condition(self) -> Program: return Program.to([51, *self.as_condition_args()]) def name(self) -> bytes32: return self.as_condition().get_tree_hash() @classmethod def from_condition(cls, condition: Program) -> "Payment": python_condition: List = condition.as_python() puzzle_hash, amount = python_condition[1:3] memos: List[bytes] = [] if len(python_condition) > 3: memos = python_condition[3] return cls(bytes32(puzzle_hash), uint64(int.from_bytes(amount, "big")), memos)

true

f71fd999a54a3748a94533f9d632879d0495dbcd

1,271

py

Python

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

""" Text Parsers to find url from content. Every url item should contain: - url - location(`filepath:row:column`) """ from abc import abstractmethod from typing import List class Link: def __init__(self, url: str, path: str, row: int, column: int): """init link object :param str url: link's href :param str path: where found this link, file path :param int row: where found this link, line number :param int column: where found this link, chars after line beginning """ self.__url = url self.__path = path self.__row = row self.__column = column @property def url(self) -> str: return self.__url @property def path(self) -> str: return self.__path @property def row(self) -> int: return self.__row @property def column(self) -> int: return self.__column @property def location(self) -> str: return f"{self.path}:{self.row}:{self.column}" @path.setter def path(self, other: str): self.__path = other class Parser: @abstractmethod def parse(self, text: str) -> List[Link]: pass @abstractmethod def parse_file(self, path: str) -> List[Link]: pass

21.183333

76

0.601888

from abc import abstractmethod from typing import List class Link: def __init__(self, url: str, path: str, row: int, column: int): self.__url = url self.__path = path self.__row = row self.__column = column @property def url(self) -> str: return self.__url @property def path(self) -> str: return self.__path @property def row(self) -> int: return self.__row @property def column(self) -> int: return self.__column @property def location(self) -> str: return f"{self.path}:{self.row}:{self.column}" @path.setter def path(self, other: str): self.__path = other class Parser: @abstractmethod def parse(self, text: str) -> List[Link]: pass @abstractmethod def parse_file(self, path: str) -> List[Link]: pass

true

f71fd9b490090c7c03e0f828032d2a989edaca88

3,415

py

Python

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

import base64 from django.db import models from django.contrib.auth.models import User from django.utils.text import slugify import string import random DEFAULT_ENVIRONMENT_ID = 1 class Flavor(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512) cpu = models.TextField(blank=True, null=True) mem = models.TextField(blank=True, null=True) gpu = models.TextField(blank=True, null=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class Environment(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512, blank=True, null=True) image = models.CharField(max_length=512) dockerfile = models.TextField(default='FROM jupyter/base-notebook') startup = models.TextField(null=True, blank=True) teardown = models.TextField(null=True, blank=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class ProjectManager(models.Manager): def generate_passkey(self, length=20): import secrets import string alphabet = string.ascii_letters + string.digits password = ''.join(secrets.choice(alphabet) for _ in range(length)) # Encrypt the key password = password.encode('ascii') base64_bytes = base64.b64encode(password) password = base64_bytes.decode('ascii') return password def create_project(self, name, owner, description, repository): letters = string.ascii_lowercase slug = name.replace(" ","-").replace("_","-") from .helpers import urlify slug = urlify(slug) slug_extension = ''.join(random.choice(letters) for i in range(3)) slug = '{}-{}'.format(slugify(slug), slug_extension) key = self.generate_passkey() secret = self.generate_passkey(40) project = self.create(name=name, owner=owner, slug=slug, project_key=key, project_secret=secret, description=description, repository=repository, repository_imported=False) return project class Project(models.Model): objects = ProjectManager() name = models.CharField(max_length=512, unique=True) description = models.TextField(null=True, blank=True) slug = models.CharField(max_length=512, unique=True) owner = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name='owner') authorized = models.ManyToManyField(User, blank=True) image = models.CharField(max_length=2048, blank=True, null=True) project_key = models.CharField(max_length=512) project_secret = models.CharField(max_length=512) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) repository = models.CharField(max_length=512, null=True, blank=True) repository_imported = models.BooleanField(default=False) def __str__(self): return "Name: {} Description: {}".format(self.name, self.description) environment = models.ForeignKey('projects.Environment', on_delete=models.DO_NOTHING, default=DEFAULT_ENVIRONMENT_ID) clone_url = models.CharField(max_length=512, null=True, blank=True)

34.846939

120

0.703075

import base64 from django.db import models from django.contrib.auth.models import User from django.utils.text import slugify import string import random DEFAULT_ENVIRONMENT_ID = 1 class Flavor(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512) cpu = models.TextField(blank=True, null=True) mem = models.TextField(blank=True, null=True) gpu = models.TextField(blank=True, null=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class Environment(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512, blank=True, null=True) image = models.CharField(max_length=512) dockerfile = models.TextField(default='FROM jupyter/base-notebook') startup = models.TextField(null=True, blank=True) teardown = models.TextField(null=True, blank=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class ProjectManager(models.Manager): def generate_passkey(self, length=20): import secrets import string alphabet = string.ascii_letters + string.digits password = ''.join(secrets.choice(alphabet) for _ in range(length)) password = password.encode('ascii') base64_bytes = base64.b64encode(password) password = base64_bytes.decode('ascii') return password def create_project(self, name, owner, description, repository): letters = string.ascii_lowercase slug = name.replace(" ","-").replace("_","-") from .helpers import urlify slug = urlify(slug) slug_extension = ''.join(random.choice(letters) for i in range(3)) slug = '{}-{}'.format(slugify(slug), slug_extension) key = self.generate_passkey() secret = self.generate_passkey(40) project = self.create(name=name, owner=owner, slug=slug, project_key=key, project_secret=secret, description=description, repository=repository, repository_imported=False) return project class Project(models.Model): objects = ProjectManager() name = models.CharField(max_length=512, unique=True) description = models.TextField(null=True, blank=True) slug = models.CharField(max_length=512, unique=True) owner = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name='owner') authorized = models.ManyToManyField(User, blank=True) image = models.CharField(max_length=2048, blank=True, null=True) project_key = models.CharField(max_length=512) project_secret = models.CharField(max_length=512) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) repository = models.CharField(max_length=512, null=True, blank=True) repository_imported = models.BooleanField(default=False) def __str__(self): return "Name: {} Description: {}".format(self.name, self.description) environment = models.ForeignKey('projects.Environment', on_delete=models.DO_NOTHING, default=DEFAULT_ENVIRONMENT_ID) clone_url = models.CharField(max_length=512, null=True, blank=True)

true

f71fdbd179d815f56f9c409701685cd66a7005c3

23,154

py

Python

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

import hashlib import hmac import json import logging import os import re import struct import tempfile import time import uuid from base64 import urlsafe_b64encode from binascii import unhexlify import m3u8 from Crypto.Cipher import AES from tqdm import tqdm def is_channel(url): url = re.findall('(slot)', url) if url: return True return False yuu_log = logging.getLogger('yuu.abematv') class AbemaTVDownloader: def __init__(self, url, session): self.key = None self.iv = None self.url = url self.session = session self.merge = True if os.name == "nt": self.yuu_folder = os.path.join(os.getenv('LOCALAPPDATA'), 'yuu_data') sffx = '\\' else: self.yuu_folder = os.path.join(os.getenv('HOME'), '.yuu_data') sffx = '/' if not os.path.isdir(self.yuu_folder): os.mkdir(self.yuu_folder) self.temporary_folder = tempfile.mkdtemp(dir=self.yuu_folder) self.temporary_folder = self.temporary_folder + sffx self._aes = None def setup_decryptor(self): self.iv = unhexlify(self.iv) self._aes = AES.new(self.key, AES.MODE_CBC, IV=self.iv) def download_chunk(self, files, key, iv): if iv.startswith('0x'): self.iv = iv[2:] else: self.iv = iv self.key = key self.downloaded_files = [] self.setup_decryptor() # Initialize a new decryptor try: with tqdm(total=len(files), desc='Downloading', ascii=True, unit='file') as pbar: for tsf in files: outputtemp = self.temporary_folder + os.path.basename(tsf) if outputtemp.find('?tver') != -1: outputtemp = outputtemp[:outputtemp.find('?tver')] with open(outputtemp, 'wb') as outf: try: vid = self.session.get(tsf) vid = self._aes.decrypt(vid.content) outf.write(vid) except Exception as err: yuu_log.error('Problem occured\nreason: {}'.format(err)) return None pbar.update() self.downloaded_files.append(outputtemp) except KeyboardInterrupt: yuu_log.warn('User pressed CTRL+C, cleaning up...') return None return self.downloaded_files class AbemaTV: def __init__(self, url, session): self.session = session self.type = 'AbemaTV' self.yuu_logger = logging.getLogger('yuu.abematv.AbemaTV') self.url = url self.m3u8_url = None self.resolution = None self.resolution_o = None self.device_id = None self.is_m3u8 = False self.est_filesize = None # In MiB self.resolution_data = { "1080p": ["4000kb/s", "AAC 192kb/s 2ch"], "720p": ["2000kb/s", "AAC 160kb/s 2ch"], "480p": ["900kb/s", "AAC 128kb/s 2ch"], "360p": ["550kb/s", "AAC 128kb/s 2ch"], "240p": ["240kb/s", "AAC 64kb/s 1ch"], "180p": ["120kb/s", "AAC 64kb/s 1ch"] } self.bitrate_calculation = { "1080p": 5175, "720p": 2373, "480p": 1367, "360p": 878, "240p": 292, "180p": 179 } self.authorization_required = False self.authorized = False # Ignore for now #self.authorize = True # Ignore for now self.resumable = True self._STRTABLE = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz" self._HKEY = b"3AF0298C219469522A313570E8583005A642E73EDD58E3EA2FB7339D3DF1597E" self._KEYPARAMS = { "osName": "android", "osVersion": "6.0.1", "osLand": "ja_JP", "osTimezone": "Asia/Tokyo", "appId": "tv.abema", "appVersion": "3.27.1" } self._MEDIATOKEN_API = "https://api.abema.io/v1/media/token" self._LICENSE_API = "https://license.abema.io/abematv-hls" self._USERAPI = "https://api.abema.io/v1/users" self._PROGRAMAPI = 'https://api.abema.io/v1/video/programs/' self._CHANNELAPI = 'https://api.abema.io/v1/media/slots/' self._SERIESAPI = "https://api.abema.io/v1/video/series/" # Use Chrome UA self.session.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}) def __repr__(self): return '<yuu.AbemaTV: URL={}, Resolution={}, Device ID={}, m3u8 URL={}>'.format(self.url, self.resolution, self.device_id, self.m3u8_url) def get_downloader(self): """ Return a :class: of the Downloader """ return AbemaTVDownloader(self.url, self.session) def resume_prepare(self): """ Add support for resuming files, this function will prepare everything to start resuming download. """ return None def authorize(self, username, password): if not self.device_id: self.yuu_logger.info('{}: Fetching temporary token'.format(self.type)) res, reas = self.get_token() # Abema needs authorization header before authenticating if not res: return res, reas _ENDPOINT_MAIL = 'https://api.abema.io/v1/auth/user/email' _ENDPOINT_OTP = 'https://api.abema.io/v1/auth/oneTimePassword' mail_regex = r'^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$' if re.search(mail_regex, username): _ENDPOINT_USE = _ENDPOINT_MAIL _USERNAME_METHOD = 'email' else: _ENDPOINT_USE = _ENDPOINT_OTP _USERNAME_METHOD = 'userId' auth_ = { _USERNAME_METHOD: username, "password": password } res = self.session.post(_ENDPOINT_USE, json=auth_) if res.status_code > 299: res_j = res.json() self.yuu_logger.debug('Abema Response: {}'.format(res_j['message'])) return False, 'Wrong {} and password combination'.format(_USERNAME_METHOD) res_j = res.json() self.yuu_logger.debug('Authentication Token: {}'.format(res_j['token'])) self.session.headers.update({'Authorization': 'bearer ' + res_j['token']}) self.authorized = True return True, 'Authorized' def get_token(self): def key_secret(devid): SECRETKEY = (b"v+Gjs=25Aw5erR!J8ZuvRrCx*rGswhB&qdHd_SYerEWdU&a?3DzN9B" b"Rbp5KwY4hEmcj5#fykMjJ=AuWz5GSMY-d@H7DMEh3M@9n2G552Us$$" b"k9cD=3TxwWe86!x#Zyhe") deviceid = devid.encode("utf-8") ts_1hour = (int(time.time()) + 60 * 60) // 3600 * 3600 time_struct = time.gmtime(ts_1hour) ts_1hour_str = str(ts_1hour).encode("utf-8") h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(SECRETKEY) tmp = h.digest() for _ in range(time_struct.tm_mon): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + deviceid) tmp = h.digest() for _ in range(time_struct.tm_mday % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + ts_1hour_str) tmp = h.digest() for _ in range(time_struct.tm_hour % 5): # utc hour h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() finalize = urlsafe_b64encode(tmp).rstrip(b"=").decode("utf-8") self.yuu_logger.debug('Secret Key: {}'.format(finalize)) return finalize if self.authorized: # Ignore this if already login return True, 'Success' deviceid = str(uuid.uuid4()) self.yuu_logger.debug('Generated Device UUID: {}'.format(deviceid)) json_data = {"deviceId": deviceid, "applicationKeySecret": key_secret(deviceid)} self.yuu_logger.debug('Generated applicationKeySecret: {}'.format(json_data['applicationKeySecret'])) self.yuu_logger.debug('Sending json data') res = self.session.post(self._USERAPI, json=json_data).json() try: self.yuu_logger.debug('Data sent, getting token') token = res['token'] self.yuu_logger.debug('User token: {}'.format(token)) except: return None, 'Failed to get user token.' self.device_id = deviceid self.session.headers.update({'Authorization': 'bearer ' + token}) return 'Success', 'Success' def parse(self, resolution=None, check_only=False): """ Function to parse abema url """ res_list = [ '180p', '240p', '360p', '480p', '720p', '1080p', 'best', 'worst' ] if resolution not in res_list: if not check_only: return None, 'Unknown resolution: {}. (Check it with `-R`)'.format(resolution) if resolution == 'best': resolution = '1080p' self.resolution_o = 'best' if resolution == 'worst': resolution = '180p' # https://abema.tv/video/title/26-55 (series/playlists) # https://api.abema.io/v1/video/series/26-55 # https://api.abema.io/v1/video/series/26-55/programs?seriesVersion=1577436473958778090&seasonId=26-55_s1&offset=0&order=seq&limit=40 series = re.search(r"(?P<series>title)/(?P<video_id>.*[^-_])", self.url) if series: video_id = series.group(2) self.yuu_logger.info('Series url format detected, fetching all links...') self.yuu_logger.debug('Requesting data to Abema API.') req = self.session.get(self._SERIESAPI + video_id) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') m3u8_url_list = [] output_list = [] jsdata = req.json() to_be_requested = "{api}{vid}/programs?seriesVersion={sv}&seasonId={si}&offset=0&order={od}" season_data = jsdata['seasons'] if not season_data: season_data = [{'id': ''}] # Assume film or some shit version = jsdata['version'] prog_order = jsdata['programOrder'] for ns, season in enumerate(season_data, 1): self.yuu_logger.info('Processing season ' + str(ns)) self.yuu_logger.debug('Requesting data to Abema API.') req_season = self.session.get(to_be_requested.format(api=self._SERIESAPI, vid=video_id, sv=version, si=season['id'], od=prog_order)) if req_season.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_season.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_season.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') season_jsdata = req_season.json() self.yuu_logger.debug('Processing total of {ep} episode for season {se}'.format(ep=len(season_jsdata['programs']), se=ns)) for nep, episode in enumerate(season_jsdata['programs'], 1): free_episode = False if 'label' in episode: if 'free' in episode['label']: free_episode = True elif 'freeEndAt' in episode: free_episode = True if 'episode' in episode: try: episode_name = episode['episode']['title'] if not episode_name: episode_name = episode_name['title']['number'] except KeyError: episode_name = episode_name['title']['number'] else: episode_name = nep if not free_episode and not self.authorized: self.yuu_logger.warn('Skipping episode {} (Not authorized and premium video)'.format(episode_name)) continue self.yuu_logger.info('Processing episode {}'.format(episode_name)) req_ep = self.session.get(self._PROGRAMAPI + episode['id']) if req_ep.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_ep.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_ep.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') ep_json = req_ep.json() title = ep_json['series']['title'] epnum = ep_json['episode']['title'] hls = ep_json['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) m3u8_url_list.append(m3u8_url) output_list.append(output_name) self.resolution = resolution self.m3u8_url = m3u8_url_list if not output_list: err_msg = "All video are for premium only, please provide login details." else: err_msg = "Success" return output_list, err_msg if '.m3u8' in self.url[-5:]: reg = re.compile(r'(program|slot)\/[\w+-]+') self.url = re.search(reg, m3u8)[0] self.is_m3u8 = True ep_link = self.url[self.url.rfind('/')+1:] self.yuu_logger.debug('Requesting data to Abema API') if is_channel(self.url): req = self.session.get(self._CHANNELAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() output_name = jsdata['slot']['title'] if 'playback' in jsdata['slot']: hls = jsdata['slot']['playback']['hls'] else: hls = jsdata['slot']['chasePlayback']['hls'] # Compat m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Title: {}'.format(output_name)) else: req = self.session.get(self._PROGRAMAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() if jsdata['mediaStatus']: if 'drm' in jsdata['mediaStatus']: if jsdata['mediaStatus']['drm']: return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' title = jsdata['series']['title'] epnum = jsdata['episode']['title'] hls = jsdata['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) self.yuu_logger.debug('Episode number: {}'.format(epnum)) self.resolution = resolution self.m3u8_url = m3u8_url return output_name, 'Success' def parse_m3u8(self, m3u8_url): self.yuu_logger.debug('Requesting m3u8') r = self.session.get(m3u8_url) self.yuu_logger.debug('Data requested') if 'timeshift forbidden' in r.text: return None, None, None, 'This video can\'t be downloaded for now.' if r.status_code == 403: return None, None, None, 'This video is geo-locked for Japan only.' self.yuu_logger.debug('Parsing m3u8') x = m3u8.loads(r.text) files = x.files[1:] if not files[0]: files = files[1:] if 'tsda' in files[5]: # Assume DRMed return None, None, None, 'This video has a different DRM method and cannot be decrypted by yuu for now' resgex = re.findall(r'(\d*)(?:\/\w+.ts)', files[0])[0] keys_data = x.keys[0] iv = x.keys[0].iv ticket = x.keys[0].uri[18:] parsed_files = [] for f in files: if f.startswith('/tsvpg') or f.startswith('/tspg'): f = 'https://ds-vod-abematv.akamaized.net' + f parsed_files.append(f) if self.resolution[:-1] != resgex: if not self.resolution_o: self.yuu_logger.warn('Changing resolution, from {} to {}p'.format(self.resolution, resgex)) self.resolution = resgex + 'p' self.yuu_logger.debug('Total files: {}'.format(len(files))) self.yuu_logger.debug('IV: {}'.format(iv)) self.yuu_logger.debug('Ticket key: {}'.format(ticket)) n = 0.0 for seg in x.segments: n += seg.duration self.est_filesize = round((round(n) * self.bitrate_calculation[self.resolution]) / 1024 / 6, 2) return parsed_files, iv[2:], ticket, 'Success' def get_video_key(self, ticket): self.yuu_logger.debug('Sending parameter to API') restoken = self.session.get(self._MEDIATOKEN_API, params=self._KEYPARAMS).json() mediatoken = restoken['token'] self.yuu_logger.debug('Media token: {}'.format(mediatoken)) self.yuu_logger.debug('Sending ticket and media token to License API') rgl = self.session.post(self._LICENSE_API, params={"t": mediatoken}, json={"kv": "a", "lt": ticket}) if rgl.status_code == 403: return None, 'Access to this video are not allowed\nProbably a premium video or geo-locked.' gl = rgl.json() cid = gl['cid'] k = gl['k'] self.yuu_logger.debug('CID: {}'.format(cid)) self.yuu_logger.debug('K: {}'.format(k)) self.yuu_logger.debug('Summing up data with STRTABLE') res = sum([self._STRTABLE.find(k[i]) * (58 ** (len(k) - 1 - i)) for i in range(len(k))]) self.yuu_logger.debug('Result: {}'.format(res)) self.yuu_logger.debug('Intepreting data') encvk = struct.pack('>QQ', res >> 64, res & 0xffffffffffffffff) self.yuu_logger.debug('Encoded video key: {}'.format(encvk)) self.yuu_logger.debug('Hashing data') h = hmac.new(unhexlify(self._HKEY), (cid + self.device_id).encode("utf-8"), digestmod=hashlib.sha256) enckey = h.digest() self.yuu_logger.debug('Second Encoded video key: {}'.format(enckey)) self.yuu_logger.debug('Decrypting result') aes = AES.new(enckey, AES.MODE_ECB) vkey = aes.decrypt(encvk) self.yuu_logger.debug('Decrypted, Result: {}'.format(vkey)) return vkey, 'Success getting video key' def resolutions(self, m3u8_uri): self.yuu_logger.debug('Requesting data to API') m3u8_ = m3u8_uri[:m3u8_uri.rfind('/')] base_url = m3u8_[:m3u8_.rfind('/')] + '/' m3u8_1080 = m3u8_[:m3u8_.rfind('/')] + '/1080/playlist.m3u8' m3u8_720 = m3u8_[:m3u8_.rfind('/')] + '/720/playlist.m3u8' m3u8_480 = m3u8_[:m3u8_.rfind('/')] + '/480/playlist.m3u8' m3u8_360 = m3u8_[:m3u8_.rfind('/')] + '/360/playlist.m3u8' m3u8_240 = m3u8_[:m3u8_.rfind('/')] + '/240/playlist.m3u8' m3u8_180 = m3u8_[:m3u8_.rfind('/')] + '/180/playlist.m3u8' rr_all = self.session.get(base_url + 'playlist.m3u8') if 'timeshift forbidden' in rr_all.text: return None, 'This video can\'t be downloaded for now.' r_all = m3u8.loads(rr_all.text) play_res = [] for r_p in r_all.playlists: temp = [] temp.append(r_p.stream_info.resolution) temp.append(base_url + r_p.uri) play_res.append(temp) resgex = re.compile(r'(\d*)(?:\/\w+.ts)') ava_reso = [] for resdata in play_res: reswh, m3u8_uri = resdata resw, resh = reswh self.yuu_logger.debug('Validating {}p resolution'.format(resh)) rres = m3u8.loads(self.session.get(m3u8_uri).text) m3f = rres.files[1:] if not m3f: return None, 'This video can\'t be downloaded for now.' self.yuu_logger.debug('Sample link: ' + m3f[5]) if 'tsda' in files[5]: # Assume DRMed return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' if str(resh) in re.findall(resgex, m3f[5]): ava_reso.append( [ '{h}p'.format(h=resh), '{w}x{h}'.format(w=resw, h=resh) ] ) if ava_reso: reso = [r[0] for r in ava_reso] self.yuu_logger.debug('Resolution list: {}'.format(', '.join(reso))) return ava_reso, 'Success' def check_output(self, output=None, output_name=None): if output: fn_, ext_ = os.path.splitext(output) if ext_ != 'ts': output = fn_ + '.ts' else: output = '{x} ({m} {r}).ts'.format(x=output_name, m=self.type, r=self.resolution) return output

38.914286

170

0.556319

import hashlib import hmac import json import logging import os import re import struct import tempfile import time import uuid from base64 import urlsafe_b64encode from binascii import unhexlify import m3u8 from Crypto.Cipher import AES from tqdm import tqdm def is_channel(url): url = re.findall('(slot)', url) if url: return True return False yuu_log = logging.getLogger('yuu.abematv') class AbemaTVDownloader: def __init__(self, url, session): self.key = None self.iv = None self.url = url self.session = session self.merge = True if os.name == "nt": self.yuu_folder = os.path.join(os.getenv('LOCALAPPDATA'), 'yuu_data') sffx = '\\' else: self.yuu_folder = os.path.join(os.getenv('HOME'), '.yuu_data') sffx = '/' if not os.path.isdir(self.yuu_folder): os.mkdir(self.yuu_folder) self.temporary_folder = tempfile.mkdtemp(dir=self.yuu_folder) self.temporary_folder = self.temporary_folder + sffx self._aes = None def setup_decryptor(self): self.iv = unhexlify(self.iv) self._aes = AES.new(self.key, AES.MODE_CBC, IV=self.iv) def download_chunk(self, files, key, iv): if iv.startswith('0x'): self.iv = iv[2:] else: self.iv = iv self.key = key self.downloaded_files = [] self.setup_decryptor() try: with tqdm(total=len(files), desc='Downloading', ascii=True, unit='file') as pbar: for tsf in files: outputtemp = self.temporary_folder + os.path.basename(tsf) if outputtemp.find('?tver') != -1: outputtemp = outputtemp[:outputtemp.find('?tver')] with open(outputtemp, 'wb') as outf: try: vid = self.session.get(tsf) vid = self._aes.decrypt(vid.content) outf.write(vid) except Exception as err: yuu_log.error('Problem occured\nreason: {}'.format(err)) return None pbar.update() self.downloaded_files.append(outputtemp) except KeyboardInterrupt: yuu_log.warn('User pressed CTRL+C, cleaning up...') return None return self.downloaded_files class AbemaTV: def __init__(self, url, session): self.session = session self.type = 'AbemaTV' self.yuu_logger = logging.getLogger('yuu.abematv.AbemaTV') self.url = url self.m3u8_url = None self.resolution = None self.resolution_o = None self.device_id = None self.is_m3u8 = False self.est_filesize = None self.resolution_data = { "1080p": ["4000kb/s", "AAC 192kb/s 2ch"], "720p": ["2000kb/s", "AAC 160kb/s 2ch"], "480p": ["900kb/s", "AAC 128kb/s 2ch"], "360p": ["550kb/s", "AAC 128kb/s 2ch"], "240p": ["240kb/s", "AAC 64kb/s 1ch"], "180p": ["120kb/s", "AAC 64kb/s 1ch"] } self.bitrate_calculation = { "1080p": 5175, "720p": 2373, "480p": 1367, "360p": 878, "240p": 292, "180p": 179 } self.authorization_required = False self.authorized = False esumable = True self._STRTABLE = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz" self._HKEY = b"3AF0298C219469522A313570E8583005A642E73EDD58E3EA2FB7339D3DF1597E" self._KEYPARAMS = { "osName": "android", "osVersion": "6.0.1", "osLand": "ja_JP", "osTimezone": "Asia/Tokyo", "appId": "tv.abema", "appVersion": "3.27.1" } self._MEDIATOKEN_API = "https://api.abema.io/v1/media/token" self._LICENSE_API = "https://license.abema.io/abematv-hls" self._USERAPI = "https://api.abema.io/v1/users" self._PROGRAMAPI = 'https://api.abema.io/v1/video/programs/' self._CHANNELAPI = 'https://api.abema.io/v1/media/slots/' self._SERIESAPI = "https://api.abema.io/v1/video/series/" self.session.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}) def __repr__(self): return '<yuu.AbemaTV: URL={}, Resolution={}, Device ID={}, m3u8 URL={}>'.format(self.url, self.resolution, self.device_id, self.m3u8_url) def get_downloader(self): return AbemaTVDownloader(self.url, self.session) def resume_prepare(self): return None def authorize(self, username, password): if not self.device_id: self.yuu_logger.info('{}: Fetching temporary token'.format(self.type)) res, reas = self.get_token() if not res: return res, reas _ENDPOINT_MAIL = 'https://api.abema.io/v1/auth/user/email' _ENDPOINT_OTP = 'https://api.abema.io/v1/auth/oneTimePassword' mail_regex = r'^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$' if re.search(mail_regex, username): _ENDPOINT_USE = _ENDPOINT_MAIL _USERNAME_METHOD = 'email' else: _ENDPOINT_USE = _ENDPOINT_OTP _USERNAME_METHOD = 'userId' auth_ = { _USERNAME_METHOD: username, "password": password } res = self.session.post(_ENDPOINT_USE, json=auth_) if res.status_code > 299: res_j = res.json() self.yuu_logger.debug('Abema Response: {}'.format(res_j['message'])) return False, 'Wrong {} and password combination'.format(_USERNAME_METHOD) res_j = res.json() self.yuu_logger.debug('Authentication Token: {}'.format(res_j['token'])) self.session.headers.update({'Authorization': 'bearer ' + res_j['token']}) self.authorized = True return True, 'Authorized' def get_token(self): def key_secret(devid): SECRETKEY = (b"v+Gjs=25Aw5erR!J8ZuvRrCx*rGswhB&qdHd_SYerEWdU&a?3DzN9B" b"Rbp5KwY4hEmcj5#fykMjJ=AuWz5GSMY-d@H7DMEh3M@9n2G552Us$$" b"k9cD=3TxwWe86!x#Zyhe") deviceid = devid.encode("utf-8") ts_1hour = (int(time.time()) + 60 * 60) // 3600 * 3600 time_struct = time.gmtime(ts_1hour) ts_1hour_str = str(ts_1hour).encode("utf-8") h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(SECRETKEY) tmp = h.digest() for _ in range(time_struct.tm_mon): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + deviceid) tmp = h.digest() for _ in range(time_struct.tm_mday % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + ts_1hour_str) tmp = h.digest() for _ in range(time_struct.tm_hour % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() finalize = urlsafe_b64encode(tmp).rstrip(b"=").decode("utf-8") self.yuu_logger.debug('Secret Key: {}'.format(finalize)) return finalize if self.authorized: return True, 'Success' deviceid = str(uuid.uuid4()) self.yuu_logger.debug('Generated Device UUID: {}'.format(deviceid)) json_data = {"deviceId": deviceid, "applicationKeySecret": key_secret(deviceid)} self.yuu_logger.debug('Generated applicationKeySecret: {}'.format(json_data['applicationKeySecret'])) self.yuu_logger.debug('Sending json data') res = self.session.post(self._USERAPI, json=json_data).json() try: self.yuu_logger.debug('Data sent, getting token') token = res['token'] self.yuu_logger.debug('User token: {}'.format(token)) except: return None, 'Failed to get user token.' self.device_id = deviceid self.session.headers.update({'Authorization': 'bearer ' + token}) return 'Success', 'Success' def parse(self, resolution=None, check_only=False): res_list = [ '180p', '240p', '360p', '480p', '720p', '1080p', 'best', 'worst' ] if resolution not in res_list: if not check_only: return None, 'Unknown resolution: {}. (Check it with `-R`)'.format(resolution) if resolution == 'best': resolution = '1080p' self.resolution_o = 'best' if resolution == 'worst': resolution = '180p' series = re.search(r"(?P<series>title)/(?P<video_id>.*[^-_])", self.url) if series: video_id = series.group(2) self.yuu_logger.info('Series url format detected, fetching all links...') self.yuu_logger.debug('Requesting data to Abema API.') req = self.session.get(self._SERIESAPI + video_id) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') m3u8_url_list = [] output_list = [] jsdata = req.json() to_be_requested = "{api}{vid}/programs?seriesVersion={sv}&seasonId={si}&offset=0&order={od}" season_data = jsdata['seasons'] if not season_data: season_data = [{'id': ''}] version = jsdata['version'] prog_order = jsdata['programOrder'] for ns, season in enumerate(season_data, 1): self.yuu_logger.info('Processing season ' + str(ns)) self.yuu_logger.debug('Requesting data to Abema API.') req_season = self.session.get(to_be_requested.format(api=self._SERIESAPI, vid=video_id, sv=version, si=season['id'], od=prog_order)) if req_season.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_season.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_season.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') season_jsdata = req_season.json() self.yuu_logger.debug('Processing total of {ep} episode for season {se}'.format(ep=len(season_jsdata['programs']), se=ns)) for nep, episode in enumerate(season_jsdata['programs'], 1): free_episode = False if 'label' in episode: if 'free' in episode['label']: free_episode = True elif 'freeEndAt' in episode: free_episode = True if 'episode' in episode: try: episode_name = episode['episode']['title'] if not episode_name: episode_name = episode_name['title']['number'] except KeyError: episode_name = episode_name['title']['number'] else: episode_name = nep if not free_episode and not self.authorized: self.yuu_logger.warn('Skipping episode {} (Not authorized and premium video)'.format(episode_name)) continue self.yuu_logger.info('Processing episode {}'.format(episode_name)) req_ep = self.session.get(self._PROGRAMAPI + episode['id']) if req_ep.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_ep.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_ep.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') ep_json = req_ep.json() title = ep_json['series']['title'] epnum = ep_json['episode']['title'] hls = ep_json['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) m3u8_url_list.append(m3u8_url) output_list.append(output_name) self.resolution = resolution self.m3u8_url = m3u8_url_list if not output_list: err_msg = "All video are for premium only, please provide login details." else: err_msg = "Success" return output_list, err_msg if '.m3u8' in self.url[-5:]: reg = re.compile(r'(program|slot)\/[\w+-]+') self.url = re.search(reg, m3u8)[0] self.is_m3u8 = True ep_link = self.url[self.url.rfind('/')+1:] self.yuu_logger.debug('Requesting data to Abema API') if is_channel(self.url): req = self.session.get(self._CHANNELAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() output_name = jsdata['slot']['title'] if 'playback' in jsdata['slot']: hls = jsdata['slot']['playback']['hls'] else: hls = jsdata['slot']['chasePlayback']['hls'] m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Title: {}'.format(output_name)) else: req = self.session.get(self._PROGRAMAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() if jsdata['mediaStatus']: if 'drm' in jsdata['mediaStatus']: if jsdata['mediaStatus']['drm']: return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' title = jsdata['series']['title'] epnum = jsdata['episode']['title'] hls = jsdata['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) self.yuu_logger.debug('Episode number: {}'.format(epnum)) self.resolution = resolution self.m3u8_url = m3u8_url return output_name, 'Success' def parse_m3u8(self, m3u8_url): self.yuu_logger.debug('Requesting m3u8') r = self.session.get(m3u8_url) self.yuu_logger.debug('Data requested') if 'timeshift forbidden' in r.text: return None, None, None, 'This video can\'t be downloaded for now.' if r.status_code == 403: return None, None, None, 'This video is geo-locked for Japan only.' self.yuu_logger.debug('Parsing m3u8') x = m3u8.loads(r.text) files = x.files[1:] if not files[0]: files = files[1:] if 'tsda' in files[5]: # Assume DRMed return None, None, None, 'This video has a different DRM method and cannot be decrypted by yuu for now' resgex = re.findall(r'(\d*)(?:\/\w+.ts)', files[0])[0] keys_data = x.keys[0] iv = x.keys[0].iv ticket = x.keys[0].uri[18:] parsed_files = [] for f in files: if f.startswith('/tsvpg') or f.startswith('/tspg'): f = 'https://ds-vod-abematv.akamaized.net' + f parsed_files.append(f) if self.resolution[:-1] != resgex: if not self.resolution_o: self.yuu_logger.warn('Changing resolution, from {} to {}p'.format(self.resolution, resgex)) self.resolution = resgex + 'p' self.yuu_logger.debug('Total files: {}'.format(len(files))) self.yuu_logger.debug('IV: {}'.format(iv)) self.yuu_logger.debug('Ticket key: {}'.format(ticket)) n = 0.0 for seg in x.segments: n += seg.duration self.est_filesize = round((round(n) * self.bitrate_calculation[self.resolution]) / 1024 / 6, 2) return parsed_files, iv[2:], ticket, 'Success' def get_video_key(self, ticket): self.yuu_logger.debug('Sending parameter to API') restoken = self.session.get(self._MEDIATOKEN_API, params=self._KEYPARAMS).json() mediatoken = restoken['token'] self.yuu_logger.debug('Media token: {}'.format(mediatoken)) self.yuu_logger.debug('Sending ticket and media token to License API') rgl = self.session.post(self._LICENSE_API, params={"t": mediatoken}, json={"kv": "a", "lt": ticket}) if rgl.status_code == 403: return None, 'Access to this video are not allowed\nProbably a premium video or geo-locked.' gl = rgl.json() cid = gl['cid'] k = gl['k'] self.yuu_logger.debug('CID: {}'.format(cid)) self.yuu_logger.debug('K: {}'.format(k)) self.yuu_logger.debug('Summing up data with STRTABLE') res = sum([self._STRTABLE.find(k[i]) * (58 ** (len(k) - 1 - i)) for i in range(len(k))]) self.yuu_logger.debug('Result: {}'.format(res)) self.yuu_logger.debug('Intepreting data') encvk = struct.pack('>QQ', res >> 64, res & 0xffffffffffffffff) self.yuu_logger.debug('Encoded video key: {}'.format(encvk)) self.yuu_logger.debug('Hashing data') h = hmac.new(unhexlify(self._HKEY), (cid + self.device_id).encode("utf-8"), digestmod=hashlib.sha256) enckey = h.digest() self.yuu_logger.debug('Second Encoded video key: {}'.format(enckey)) self.yuu_logger.debug('Decrypting result') aes = AES.new(enckey, AES.MODE_ECB) vkey = aes.decrypt(encvk) self.yuu_logger.debug('Decrypted, Result: {}'.format(vkey)) return vkey, 'Success getting video key' def resolutions(self, m3u8_uri): self.yuu_logger.debug('Requesting data to API') m3u8_ = m3u8_uri[:m3u8_uri.rfind('/')] base_url = m3u8_[:m3u8_.rfind('/')] + '/' m3u8_1080 = m3u8_[:m3u8_.rfind('/')] + '/1080/playlist.m3u8' m3u8_720 = m3u8_[:m3u8_.rfind('/')] + '/720/playlist.m3u8' m3u8_480 = m3u8_[:m3u8_.rfind('/')] + '/480/playlist.m3u8' m3u8_360 = m3u8_[:m3u8_.rfind('/')] + '/360/playlist.m3u8' m3u8_240 = m3u8_[:m3u8_.rfind('/')] + '/240/playlist.m3u8' m3u8_180 = m3u8_[:m3u8_.rfind('/')] + '/180/playlist.m3u8' rr_all = self.session.get(base_url + 'playlist.m3u8') if 'timeshift forbidden' in rr_all.text: return None, 'This video can\'t be downloaded for now.' r_all = m3u8.loads(rr_all.text) play_res = [] for r_p in r_all.playlists: temp = [] temp.append(r_p.stream_info.resolution) temp.append(base_url + r_p.uri) play_res.append(temp) resgex = re.compile(r'(\d*)(?:\/\w+.ts)') ava_reso = [] for resdata in play_res: reswh, m3u8_uri = resdata resw, resh = reswh self.yuu_logger.debug('Validating {}p resolution'.format(resh)) rres = m3u8.loads(self.session.get(m3u8_uri).text) m3f = rres.files[1:] if not m3f: return None, 'This video can\'t be downloaded for now.' self.yuu_logger.debug('Sample link: ' + m3f[5]) if 'tsda' in files[5]: # Assume DRMed return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' if str(resh) in re.findall(resgex, m3f[5]): ava_reso.append( [ '{h}p'.format(h=resh), '{w}x{h}'.format(w=resw, h=resh) ] ) if ava_reso: reso = [r[0] for r in ava_reso] self.yuu_logger.debug('Resolution list: {}'.format(', '.join(reso))) return ava_reso, 'Success' def check_output(self, output=None, output_name=None): if output: fn_, ext_ = os.path.splitext(output) if ext_ != 'ts': output = fn_ + '.ts' else: output = '{x} ({m} {r}).ts'.format(x=output_name, m=self.type, r=self.resolution) return output

true

f71fde1bd02fcc1f714d372b9d638e5ed8bbe7be

4,468

py

Python

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

16

2017-06-30T20:05:05.000Z

2022-03-08T21:03:19.000Z

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

342

2017-06-23T21:37:40.000Z

2022-03-30T16:44:16.000Z

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

33

2017-07-01T00:12:20.000Z

2022-01-26T18:06:53.000Z

""" The basic date shifting rule.. Original Issue: DC-1005 This is an abstract class and cannot be directly instantiated. It must be extended to be used. """ # Python Imports import logging from abc import abstractmethod # Project imports from cdr_cleaner.cleaning_rules.base_cleaning_rule import BaseCleaningRule from common import JINJA_ENV LOGGER = logging.getLogger(__name__) SHIFT_EXP = JINJA_ENV.from_string(""" {{field_type}}_SUB( CAST({{field}} AS {{field_type}}), INTERVAL ( SELECT shift FROM `{{project}}.{{mapping_dataset_id}}.{{mapping_table_id}}` AS map WHERE map.research_id = remodel.person_id) DAY) AS {{field}} """) SELECT_STATEMENT = JINJA_ENV.from_string(""" CREATE OR REPLACE TABLE `{{project}}.{{dataset}}.{{table}}` AS ( SELECT {{fields}} FROM `{{project}}.{{dataset}}.{{table}}` AS remodel) """) class DateShiftRule(BaseCleaningRule): """ Date shift fields from 1 - 365 days in the past. Performs a "day" shift for any field in the provided table names and schemas. Uses the field type to determine the shift function to use. Currently works for the DATE, DATETIME, and TIMESTAMP type fields. """ def __init__(self, project_id, dataset_id, sandbox_dataset_id, issue_numbers, description, affected_datasets, affected_tables, mapping_dataset_id, mapping_table_id, depends_on=None): """ Initialize the class. Set the issue numbers, description and affected datasets. As other tickets may affect this SQL, append them to the list of Jira Issues. DO NOT REMOVE ORIGINAL JIRA ISSUE NUMBERS! """ if depends_on is None: depends_on = [] desc = (f'Date shift date and timestamp fields by the date shift ' f'calculated in the static mapping table.') self.mapping_dataset_id = mapping_dataset_id self.mapping_table_id = mapping_table_id super().__init__(issue_numbers=issue_numbers, description=description, affected_datasets=affected_datasets, project_id=project_id, dataset_id=dataset_id, sandbox_dataset_id=sandbox_dataset_id, affected_tables=affected_tables, depends_on=depends_on) @abstractmethod def get_tables_and_schemas(self): """ Provide dictionary of table names and schemas. :returns: a dictionary whose key, value patterns are in the form of {"tablename": "json schema",}. """ pass def get_query_specs(self): """ Return a list of dictionary query specifications. :return: A list of dictionaries. Each dictionary contains a single query and a specification for how to execute that query. The specifications are optional but the query is required. """ date_shift_queries = [] for table, schema in self.get_tables_and_schemas().items(): LOGGER.info(f"Building Date Shifting query for {self.dataset_id}." f"{table}") fields = [] for field in schema: field_type = field.get('type').lower() field_name = field.get('name') if field_type in ['date', 'datetime', 'timestamp']: shift_string = SHIFT_EXP.render( project=self.project_id, mapping_dataset_id=self.mapping_dataset_id, mapping_table_id=self.mapping_table_id, field_type=field_type.upper(), field=field_name, table=table) fields.append(shift_string) else: fields.append(field_name) fields_string = ',\n'.join(fields) query = SELECT_STATEMENT.render(project=self.project_id, dataset=self.dataset_id, table=table, fields=fields_string) date_shift_queries.append({'query': query}) return date_shift_queries

34.90625

78

0.576321

import logging from abc import abstractmethod from cdr_cleaner.cleaning_rules.base_cleaning_rule import BaseCleaningRule from common import JINJA_ENV LOGGER = logging.getLogger(__name__) SHIFT_EXP = JINJA_ENV.from_string(""" {{field_type}}_SUB( CAST({{field}} AS {{field_type}}), INTERVAL ( SELECT shift FROM `{{project}}.{{mapping_dataset_id}}.{{mapping_table_id}}` AS map WHERE map.research_id = remodel.person_id) DAY) AS {{field}} """) SELECT_STATEMENT = JINJA_ENV.from_string(""" CREATE OR REPLACE TABLE `{{project}}.{{dataset}}.{{table}}` AS ( SELECT {{fields}} FROM `{{project}}.{{dataset}}.{{table}}` AS remodel) """) class DateShiftRule(BaseCleaningRule): def __init__(self, project_id, dataset_id, sandbox_dataset_id, issue_numbers, description, affected_datasets, affected_tables, mapping_dataset_id, mapping_table_id, depends_on=None): if depends_on is None: depends_on = [] desc = (f'Date shift date and timestamp fields by the date shift ' f'calculated in the static mapping table.') self.mapping_dataset_id = mapping_dataset_id self.mapping_table_id = mapping_table_id super().__init__(issue_numbers=issue_numbers, description=description, affected_datasets=affected_datasets, project_id=project_id, dataset_id=dataset_id, sandbox_dataset_id=sandbox_dataset_id, affected_tables=affected_tables, depends_on=depends_on) @abstractmethod def get_tables_and_schemas(self): pass def get_query_specs(self): date_shift_queries = [] for table, schema in self.get_tables_and_schemas().items(): LOGGER.info(f"Building Date Shifting query for {self.dataset_id}." f"{table}") fields = [] for field in schema: field_type = field.get('type').lower() field_name = field.get('name') if field_type in ['date', 'datetime', 'timestamp']: shift_string = SHIFT_EXP.render( project=self.project_id, mapping_dataset_id=self.mapping_dataset_id, mapping_table_id=self.mapping_table_id, field_type=field_type.upper(), field=field_name, table=table) fields.append(shift_string) else: fields.append(field_name) fields_string = ',\n'.join(fields) query = SELECT_STATEMENT.render(project=self.project_id, dataset=self.dataset_id, table=table, fields=fields_string) date_shift_queries.append({'query': query}) return date_shift_queries

true

f71fde73faca108579365cdbb13033f096a89b4b

37,146

py

Python

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

3

2022-01-13T12:39:54.000Z

2022-03-30T00:10:52.000Z

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

null

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

null

# 201005: rename/restructure .yml files for consistency with xtb-level data # 201006: in read_conformer() fix error message when log files are missing import os,re,itertools,time #import pybel #from openbabel import pybel import numpy as np import pandas as pd import pathlib as pl cwd = pl.Path.cwd() import yaml from yaml import CLoader as Loader from yaml import CDumper as Dumper from rdkit import Chem,Geometry from rdkit.Chem import rdmolfiles, AllChem, rdMolAlign,rdmolops from multiprocessing import Pool import morfeus # Kjell Jorner from PL_split_logs_201006 import split_log # TG from PL_conformer_selection_200411 import mirror_mol, delete_element_from_rdkitmol, delete_haloalkane_halides # TG #changed from PL_conformer_selection_201019 5/17/21 by EP import PL_gaussian_properties_201021 as gp # TG import vmin4 as vmin # TG/Iris Guo import P_int_200916 as P_int # Robert Pollice (,TG(,ML)) # import PL_visvol as visvol # Ellyn Peters # covalent radii, from Pyykko and Atsumi, Chem. Eur. J. 15, 2009, 188-197 # values for metals decreased by 10% according to Robert Paton's Sterimol implementation rcov = { "H": 0.32,"He": 0.46,"Li": 1.2,"Be": 0.94,"B": 0.77,"C": 0.75,"N": 0.71,"O": 0.63,"F": 0.64,"Ne": 0.67,"Na": 1.4,"Mg": 1.25,"Al": 1.13,"Si": 1.04,"P": 1.1,"S": 1.02,"Cl": 0.99,"Ar": 0.96,"K": 1.76,"Ca": 1.54,"Sc": 1.33,"Ti": 1.22,"V": 1.21,"Cr": 1.1,"Mn": 1.07,"Fe": 1.04,"Co": 1.0,"Ni": 0.99,"Cu": 1.01,"Zn": 1.09,"Ga": 1.12,"Ge": 1.09,"As": 1.15,"Se": 1.1,"Br": 1.14,"Kr": 1.17,"Rb": 1.89,"Sr": 1.67,"Y": 1.47,"Zr": 1.39,"Nb": 1.32,"Mo": 1.24,"Tc": 1.15,"Ru": 1.13,"Rh": 1.13,"Pd": 1.08,"Ag": 1.15,"Cd": 1.23,"In": 1.28,"Sn": 1.26,"Sb": 1.26,"Te": 1.23,"I": 1.32,"Xe": 1.31,"Cs": 2.09,"Ba": 1.76,"La": 1.62,"Ce": 1.47,"Pr": 1.58,"Nd": 1.57,"Pm": 1.56,"Sm": 1.55,"Eu": 1.51,"Gd": 1.52,"Tb": 1.51,"Dy": 1.5,"Ho": 1.49,"Er": 1.49,"Tm": 1.48,"Yb": 1.53,"Lu": 1.46,"Hf": 1.37,"Ta": 1.31,"W": 1.23,"Re": 1.18,"Os": 1.16,"Ir": 1.11,"Pt": 1.12,"Au": 1.13,"Hg": 1.32,"Tl": 1.3,"Pb": 1.3,"Bi": 1.36,"Po": 1.31,"At": 1.38,"Rn": 1.42,"Fr": 2.01,"Ra": 1.81,"Ac": 1.67,"Th": 1.58,"Pa": 1.52,"U": 1.53,"Np": 1.54,"Pu": 1.55 } # some constants R = 0.0019872036 #kcal mol^-1 K^-1 T = 298.15 #K hartree_kcalmol = 627.50947 periodictable = ["Bq","H","He","Li","Be","B","C","N","O","F","Ne","Na","Mg","Al","Si","P","S","Cl","Ar","K","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge","As","Se","Br","Kr","Rb","Sr","Y","Zr","Nb","Mo","Tc","Ru","Rh","Pd","Ag","Cd","In","Sn","Sb","Te","I","Xe","Cs","Ba","La","Ce","Pr","Nd","Pm","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu","Hf","Ta","W","Re","Os","Ir","Pt","Au","Hg","Tl","Pb","Bi","Po","At","Rn","Fr","Ra","Ac","Th","Pa","U","Np","Pu","Am","Cm","Bk","Cf","Es","Fm","Md","No","Lr","Rf","Db","Sg","Bh","Hs","Mt","Ds","Rg","Uub","Uut","Uuq","Uup","Uuh","Uus","Uuo","X"] def get_conmat(elements, coords): # partially based on code from Robert Paton's Sterimol script, which based this part on Grimme's D3 code # elements is a list of strings, coords is a numpy array or nested list of shape N_atoms x 3 if type(coords) == list: coords = np.asarray(coords) natom = len(elements) #max_elem = 94 k1 = 16.0 k2 = 4.0/3.0 conmat = np.zeros((natom,natom)) for i in range(0,natom): if elements[i] not in rcov.keys(): continue for iat in range(0,natom): if elements[iat] not in rcov.keys(): continue if iat != i: dxyz = coords[iat]-coords[i] r = np.linalg.norm(dxyz) rco = rcov[elements[i]]+rcov[elements[iat]] rco = rco*k2 rr=rco/r damp=1.0/(1.0+np.math.exp(-k1*(rr-1.0))) if damp > 0.85: #check if threshold is good enough for general purpose conmat[i,iat],conmat[iat,i] = 1,1 return(conmat) def add_valence(elements,coords,conmat,base_idx,add_element="Pd"): # Adds a valence to base so that the angle to the previous substituents is maximized and reorders the coordinate output for convenience # add_element: add any of the following elements: distpx = {"O":1.5,"Se":2.12,"Pd":2.28,"X":1.8} # typical bond distances to P if type(coords) == list: coords = np.asarray(coords) num_atoms = len(elements) coord_base = coords[base_idx] base_element = elements[base_idx] vec = np.array([0.0,0.0,0.0]) bonded = [] for atom in range(num_atoms): if conmat[base_idx][atom]: bonded.append(atom) vec += coord_base - coords[atom] coordox = distpx[add_element]*vec/np.linalg.norm(vec) + coord_base atoms = [x for x in range(num_atoms+1)] coords_temp = np.vstack((coords,coordox)) if sum(get_conmat(elements+[add_element],coords_temp)[-1]) != 1.0: print(" Warning: possible collision!") # sort coordinates so that base is first, add_element is second, and the other atoms bonded to base are next elements_new = [base_element,add_element]+[elements[a] for a in bonded] + [a for i,a in enumerate(elements) if i not in [base_idx]+bonded] coords_new = np.vstack((coord_base, coordox, coords[bonded], coords[[i for i,a in enumerate(elements) if i not in [base_idx]+bonded]])) return(elements_new, coords_new) def write_xyz(elements,coords,filename): with open(filename,"w") as f: f.write(f"{len(elements)}\n\n") for i,a in enumerate(elements): f.write(f"{a.title():>3} " + " ".join([f"{coords[i][j]:15f}" for j in range(3)]) + "\n") def rmsd_matrix(conformers): molobjects = [rdmolfiles.MolFromMolFile(str(cwd/conformer/f"{conformer}_opt.sdf"),removeHs=False,strictParsing=False) for conformer in conformers] molobjects = [Chem.RemoveHs(mol) for mol in molobjects] # Remove all H: optional but speeds up RMSD calculation molobjects = [delete_haloalkane_halides(mol) for mol in molobjects] # Remove halides in perhaloalkyl moieties. Improves RMSD matching and timing molobjects_inv = [mirror_mol(mol) for mol in molobjects] # create mirror images of each conformer rmsd_mat = np.zeros((len(conformers),len(conformers))) for i,j in itertools.product(range(len(conformers)),range(len(conformers))): if i<j: continue if i==j: rmsd_mat[i,j] = 1 else: rmsd_mat[i,j] = min((rdMolAlign.GetBestRMS(molobjects[i],molobjects[j]),rdMolAlign.GetBestRMS(molobjects[i],molobjects_inv[j]))) rmsd_mat[j,i] = rmsd_mat[i,j] return(rmsd_mat) def dict_key_rmsd(candidate_pair): return float(rmsd_matrix(candidate_pair)[0,1]) # which energies to read from which log-file energylogs = { "e_dz":"freq", "e_tz_gas":"nbo", "e_tz_gas":"sp", "e_tz_solv":"solv", "e_tz_ra":"ra", "e_tz_rc":"rc", } # which properties to read from which log-file proplogs = { "freq":["nimag","g","t"], "sp" :["dipole","homo","qpole","t"], "ra" :["homo","nbo","t"], "rc" :["homo","nbo","t"], "nbo" :["nbo","nborbsP","t"], "nmr" :["nmr","t"], "efg" :["efg","nuesp","t"], "solv":["ecds","t"], } # assign names to each descriptor propoutput = { "freq_g": ["","g"], "freq_nimag": ["nimag"], "sp_dipole": ["dipolemoment",], "sp_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "ra_homo":["somo_ra","","","",""], "rc_homo":["somo_rc","","","",""], "sp_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], "nbo_nbo": ["nbo_P"], "ra_nbo": ["nbo_P_ra","spindens_P_ra"], "rc_nbo": ["nbo_P_rc","spindens_P_rc"], "nmr_nmr": ["nmr_P","nmrtens_sxx_P","nmrtens_syy_P","nmrtens_szz_P",], "efg_efg": ["efg_amp_P","efgtens_xx_P","efgtens_yy_P","efgtens_zz_P"], "efg_nuesp": ["nuesp_P",], "solv_ecds": ["E_solv_cds"], "nbo_dipole": ["dipolemoment",], "nbo_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "nbo_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], } boltzproperties = ['vmin_vmin','vmin_r','dipolemoment', 'fmo_e_homo', 'fmo_e_lumo', 'fmo_mu', 'fmo_eta', 'fmo_omega', 'somo_ra', 'somo_rc', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'nbo_P', 'nbo_P_ra', 'spindens_P_ra', 'nbo_P_rc', 'spindens_P_rc', 'nmr_P', 'nmrtens_sxx_P', 'nmrtens_syy_P', 'nmrtens_szz_P', 'efg_amp_P', 'efgtens_xx_P', 'efgtens_yy_P', 'efgtens_zz_P', 'nuesp_P', 'E_solv_cds', 'nbo_lp_P_percent_s', 'nbo_lp_P_occ', 'nbo_lp_P_e', 'nbo_bd_e_max', 'nbo_bd_e_avg', 'nbo_bds_e_min', 'nbo_bds_e_avg', 'nbo_bd_occ_min', 'nbo_bd_occ_avg', 'nbo_bds_occ_max', 'nbo_bds_occ_avg', 'E_solv_total', 'E_solv_elstat', 'E_oxidation', 'E_reduction', 'fukui_p', 'fukui_m', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_ratio_vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL',"Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] # "vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", mmproperties = ['dipolemoment', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL'] # ,"vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", Pintresults = ["Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] def morfeus_properties(elements,coordinates,confdata): # Morfeus: Sterimol, Vbur, pyr morfdict = {} if "pyr_P" not in confdata.keys() and confdata["p_val"] == 3: # Pyramidalization - two equivalent measurments P and alpha pyr = morfeus.Pyramidalization(elements=elements,coordinates=coordinates,atom_index=1,excluded_atoms=[2]) # remove Pd morfdict["pyr_P"] = float(pyr.P) morfdict["pyr_alpha"] = float(pyr.alpha) if "vbur_vbur" not in confdata.keys(): #Buried volume - get quadrant volumes and distal volume # iterate through P-substituents, aligning the quadrants paralell to each once (= xz_plane definition) # Metal/point of reference should be 2.28 A away from P # z_axis_atoms: P # xz_plane_atoms: each of the substituents once # keep lowest and highest quadrant and octant volume across all three orientations of the coordinate system # keep highest difference of any neighboring quadrant volume # keep volume in each of the two hemispheres qvbur_all = np.array([]) qvdist_all = np.array([]) qvtot_all = np.array([]) max_delta_qvbur_all = [] max_delta_qvtot_all = [] ovbur_all = np.array([]) ovtot_all = np.array([]) for i in range(3):#confdata["p_val"]): bv = morfeus.BuriedVolume(elements,coordinates,2,excluded_atoms=[2],z_axis_atoms=[1],xz_plane_atoms=[3+i]) bv.octant_analysis() bv.compute_distal_volume(method="buried_volume",octants=True) vbur = bv.buried_volume # these are identical for each iteration vdist = bv.distal_volume # vtot = vbur + vdist # qvbur = np.asarray(list(bv.quadrants["buried_volume"].values())) qvdist = np.asarray(list(bv.quadrants["distal_volume"].values())) qvtot = qvbur + qvdist qvbur_all = np.append(qvbur_all,qvbur) qvtot_all = np.append(qvtot_all,qvtot) max_delta_qvbur_all.append(max([abs(qvbur[j]-qvbur[j-1]) for j in range(4)])) max_delta_qvtot_all.append(max([abs(qvtot[j]-qvtot[j-1]) for j in range(4)])) ovbur = np.asarray(list(bv.octants["buried_volume"].values())) ovdist = np.asarray(list(bv.octants["distal_volume"].values())) ovtot = ovbur + ovdist ovbur_all = np.append(ovbur_all,ovbur) ovtot_all = np.append(ovtot_all,ovtot) near_vbur = ovbur[4:].sum() # these are identical for each iteration far_vbur = ovbur[:4].sum() # near_vtot = ovtot[4:].sum() # far_vtot = ovtot[:4].sum() # morfdict["vbur_vbur"] = vbur morfdict["vbur_vtot"] = float(vtot) morfdict["vbur_ratio_vbur_vtot"] = float(vbur/vtot) morfdict["vbur_qvbur_min"] = float(min(qvbur_all)) morfdict["vbur_qvbur_max"] = float(max(qvbur_all)) morfdict["vbur_qvtot_min"] = float(min(qvtot_all)) morfdict["vbur_qvtot_max"] = float(max(qvtot_all)) morfdict["vbur_max_delta_qvbur"] = float(max(max_delta_qvbur_all)) morfdict["vbur_max_delta_qvtot"] = float(max(max_delta_qvtot_all)) morfdict["vbur_ovbur_min"] = float(min(ovbur_all)) morfdict["vbur_ovbur_max"] = float(max(ovbur_all)) morfdict["vbur_ovtot_min"] = float(min(ovtot_all)) morfdict["vbur_ovtot_max"] = float(max(ovtot_all)) morfdict["vbur_near_vbur"] = float(near_vbur) morfdict["vbur_far_vbur"] = float(far_vbur) morfdict["vbur_near_vtot"] = float(near_vtot) morfdict["vbur_far_vtot"] = float(far_vtot) if "sterimol_B1" not in confdata.keys(): # Sterimol # for Sterimol values matching Rob Paton's implementation: patonradii = morfeus.helpers.get_radii(elements, radii_type="bondi") patonradii = np.array(patonradii) patonradii[patonradii == 1.2] = 1.09 sterimol = morfeus.Sterimol(elements, coordinates, 2, 1, radii=patonradii, n_rot_vectors=3600) morfdict["sterimol_B1"] = float(sterimol.B_1_value) morfdict["sterimol_B5"] = float(sterimol.B_5_value) morfdict["sterimol_L"] = float(sterimol.L_value) # buried Sterimol sterimol_bur = morfeus.Sterimol(elements, coordinates, 2, 1,calculate=False,radii=patonradii, n_rot_vectors=3600) sterimol_bur.bury(sphere_radius=5.5,method="delete",radii_scale=0.5) # sterimol.bury(sphere_radius=4.5,method="delete",radii_scale=1) morfdict["sterimol_burB1"] = float(sterimol_bur.B_1_value) morfdict["sterimol_burB5"] = float(sterimol_bur.B_5_value) morfdict["sterimol_burL"] = float(sterimol_bur.L_value) return(morfdict) def gp_properties(ligand,conformer,p_idx): # reads gaussian log files gpdict = {} gpdict["properties"] = {} contents = { "streams":{}, "filecont":{}, } # read energies for e,log in energylogs.items(): contents["streams"][log] = gp.get_outstreams(cwd/conformer/f"{conformer}_{log}.log") if contents["streams"][log] == "failed or incomplete job": return({"error":True}) else: gpdict[e] = gp.get_e_hf(contents["streams"][log]) gpdict["error"] = False # going through each log file, get the relevant properties for log in proplogs.keys(): contents["filecont"][log] = gp.get_filecont(cwd/conformer/f"{conformer}_{log}.log") for prop in proplogs[log]: gpresults = gp.jobtypes[prop][0](contents[gp.jobtypes[prop][1]][log],p_idx) if prop == "nborbsP": # NBO orbital analysis returns a dictionary with the proper labels gpdict["properties"].update(gpresults) elif prop == "t": # subjob time gpdict[f"{log}_t"] = gpresults elif prop in ["e_dz","g","e_tz_gas","e_tz_solv","e_tz_ra","e_tz_rc","nimag"]: gpdict.update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) else: # all other functions return a list. This is assigned into a dict with proper names here gpdict["properties"].update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) gpdict["g_tz_gas"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_gas"] # in Hartree gpdict["g_tz_solv"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_solv"] # in Hartree gpdict["properties"]["E_solv_total"] = (gpdict["e_tz_solv"] - gpdict["e_tz_gas"]) * hartree_kcalmol # in kcal/mol gpdict["properties"]["E_solv_elstat"] = gpdict["properties"]["E_solv_total"] - gpdict["properties"]["E_solv_cds"] # in kcal/mol gpdict["properties"]["E_oxidation"] = gpdict["e_tz_rc"] - gpdict["e_tz_gas"] # in Hartree gpdict["properties"]["E_reduction"] = gpdict["e_tz_ra"] - gpdict["e_tz_gas"] # in Hartree gpdict["properties"]["fukui_p"] = gpdict["properties"]["nbo_P"]-gpdict["properties"]["nbo_P_ra"] # fukui electrophilicity gpdict["properties"]["fukui_m"] = gpdict["properties"]["nbo_P_rc"]-gpdict["properties"]["nbo_P"] # fukui nucleophilicity gpdict["t_total"] = sum([gpdict[f"{log}_t"] for log in proplogs.keys()]) if "" in gpdict.keys(): del gpdict[""] if "" in gpdict["properties"].keys(): del gpdict["properties"][""] return(gpdict) def read_conformer(cwd, ligand, conformer): # cwd: pathlib path of current working directory. ligand: 0-digit ligand ID. conformer: full name of the conformer (including the ID at the beginnig) confdata = {} errors = [] checklogs = [cwd/conformer/f"{conformer}_{l}.log" for l in proplogs.keys() if not (cwd/conformer/f"{conformer}_{l}.log").exists()] if len(checklogs) != 0: #! log this as a conformer-level error err = f"Missing Gaussian log files, flagged in read_conformer: {','.join([chkl.name for chkl in checklogs])}" errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True return(confdata,errors) if "elements_pd" not in confdata.keys(): # mol = next(pybel.readfile("g09",str(cwd/conformer/f"{conformer}_nbo.log"))) #mol = next(pybel.readfile("g09",str(cwd/conformer/f"{conformer}_opt.log"))) #elements = [periodictable[a.atomicnum] for a in mol.atoms] #coordinates = [list(a.coords) for a in mol.atoms] #coordinates_a = np.array([a.coords for a in mol.atoms]) def read_gaussian_logfile(fn): time0=time.time() read=False for line in open(fn,"r"): if read: if "---" in line and len(elements)>0: read=False if read: if "X" not in line and "---" not in line: atomnum = int(line.split()[1]) #print(line.replace("\n","")) #print(atomnum) el = periodictable[atomnum] elements.append(el) coordinates.append([float(line.split()[3]),float(line.split()[4]), float(line.split()[5])]) if "Coordinates (Angstroms)" in line: coordinates, elements = [], [] read=True time1=time.time() print("gaussian log parser done in %.2f seconds"%(time1-time0)) return(coordinates, elements) coordinates, elements = read_gaussian_logfile(str(cwd/conformer/f"{conformer}_opt.log")) coordinates_a = np.array(coordinates) conmat = get_conmat(elements,coordinates_a) p_idx = [i for i in range(len(elements)) if elements[i] == "P" and sum(conmat[i]) <= 3][0] # this removes quaternary P (phosphonium, phosphate etc) but allows for P with 2 substituents (phosphabenzene, phosphaimine etc). Can we be sure that we never have more than one non-quaternary P(III)? elements_pd, coordinates_pd = add_valence(elements,coordinates,conmat,p_idx,add_element="Pd") # Add "Pd" at the reference position in the P-lone pair region if not (cwd/conformer/f"{conformer}_opt_Pd.xyz").exists(): #out = pybel.Outputfile("xyz",str(cwd/conformer/f"{conformer}_opt.xyz")) #out.write(mol) #out.close() write_xyz(elements, coordinates, cwd/conformer/f"{conformer}_opt.xyz") #out = pybel.Outputfile("sdf",str(cwd/conformer/f"{conformer}_opt.sdf")) #out.write(mol) #out.close() os.system("obabel -ixyz %s -osdf >> %s"%(str(cwd/conformer/f"{conformer}_opt.xyz"), str(cwd/conformer/f"{conformer}_opt.sdf"))) write_xyz(elements_pd,coordinates_pd,cwd/conformer/f"{conformer}_opt_Pd.xyz") confdata["coords"] = coordinates confdata["coords_pd"] = coordinates_pd.tolist() confdata["elements"] = elements confdata["elements_pd"] = elements_pd confdata["conmat"] = conmat.tolist() confdata["p_idx"] = p_idx confdata["p_val"] = int(sum(conmat[p_idx])) # how many substituents at P confdata["properties"] = {} ## get properties # gp_properties: everything that can be read from the Gaussian log files (most electronic properties) confdata.update(gp_properties(ligand,conformer,confdata["p_idx"])) if confdata["error"]: #! log this as a conformer-level error err = "Error in the Gaussian computations, flagged in read_conformer, please check log files." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) if confdata["nimag"] != 0: #! log this as a conformer-level error err = f"Number of imaginary frequencies: {confdata['nimag']}." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) confdata["error"] = True return(confdata,errors) # morfeus: properties that use the geometry/steric properties confdata["properties"].update(morfeus_properties(confdata["elements_pd"],confdata["coords_pd"],confdata)) # # P_int # if "Pint_P_int" not in confdata.keys(): # confdata.update(P_int.P_int_main(name=conformer,directory=cwd/conformer)) # read results disp = "d3" pint_read = P_int.read_dedout(cwd/conformer,conformer,disp)+P_int.read_multiwfnout(cwd/conformer,conformer)+P_int.read_disp(cwd/conformer,conformer,disp) confdata["properties"].update({Pintresults[i]:float(pint_read[i]) for i in range(7)}) # V_min try: if "vmin_vmin" not in confdata.keys(): vminob = vmin.get_vmin(f"{conformer}.fchk",str(cwd/conformer)+"/",True) confdata["properties"]["vmin_vmin"] = float(vminob.v_min) confdata["properties"]["vmin_r"] = float(vminob.r_min) except: err = f"Vmin FileNotFoundError." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True # visvol # if "vv_total_visible_volume" not in confdata.keys(): # confdata.update(visvol.get_vis_vol(cwd/conformer/f"{conformer}_opt_Pd.xyz",radii_type = 'rcov',prox_cutoff = 3.5,ignore_H = 0,write_results = 1, plot = 0)) with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) def read_ligand(cwd, ligand, conformers, liganddata = {}): # cwd is the ligand-level directory status = {"ligandlevel": [],} if len(liganddata.keys()) == 0: if (cwd/f"{ligand}_data.yml").exists(): with open(cwd/f"{ligand}_data.yml","r") as f: liganddata = yaml.load(f,Loader=Loader) if (cwd/f"{ligand}_confdata.yml").exists(): with open(cwd/f"{ligand}_confdata.yml","r") as f: liganddata["confdata"] = yaml.load(f,Loader=Loader) else: liganddata = { "conformers_all": conformers, "conformers": conformers.copy(), # Duplicates and computations with errors (including nimag=1) will be removed from this list "number_of_conformers": len(conformers), "removed_duplicates": [], "confdata": {},#{c:{} for c in conformers}, "boltzmann_averaged_data": {}, "min_data": {}, "max_data": {}, "delta_data": {}, "vburminconf_data": {}, } newconfs = 0 for conformer in conformers: if conformer in liganddata["removed_duplicates"]: continue print(conformer) if conformer in liganddata["confdata"].keys(): pass elif (cwd/conformer/f"{conformer}_data.yml").exists(): with open(cwd/conformer/f"{conformer}_data.yml","r") as f: liganddata["confdata"][conformer] = yaml.load(f,Loader=Loader) newconfs += 1 else: print("read conformer data") liganddata["confdata"][conformer],status[conformer] = read_conformer(cwd, ligand, conformer) # returns the dictionary with the conformer data and a list with errors newconfs += 1 if newconfs > 0: # error, NIMAG removal liganddata["conformers_w_error"] = [conformer for conformer in liganddata["conformers"] if liganddata["confdata"][conformer]["error"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["conformers_w_error"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) energies = ["e_dz","g","e_tz_gas","g_tz_gas","e_tz_solv","g_tz_solv"] liganddata["energies"] = {} liganddata["relative_energies"] = {} for e in energies: liganddata["energies"][e] = {conformer: liganddata["confdata"][conformer][e] for conformer in liganddata["conformers"]} liganddata[e+"_min"] = min(liganddata["energies"][e].values()) liganddata[e+"_minconf"] = list(liganddata["energies"][e].keys())[np.argmin(list(liganddata["energies"][e].values()))] liganddata["relative_energies"][e+"_rel"] = {conformer: (liganddata["energies"][e][conformer]-liganddata[e+"_min"])*hartree_kcalmol for conformer in liganddata["conformers"]} # erel_df = pd.DataFrame(np.array([list(liganddata[e+"_rel"].values()) for e in energies]).T ,columns=energies,index=liganddata["conformers"] ) erel_df = pd.DataFrame([liganddata["relative_energies"][e+"_rel"] for e in energies],index=energies).T #liganddata["relative_energies_df"] = erel_df liganddata["relative_energies_dict"] = erel_df.to_dict() # Find duplicates: # 1) find pairs of conformers that are within E_rel < 0.1 kcal/mol (relative energies seem to be much more reliable than relative free energies) # 2) check these pairs to also have RMSD < 0.2 A # 3) Remove the conformer with higher relative free energy duplicates_candidates = [(i,j) for i,j in itertools.combinations(liganddata["conformers"],2) if abs(erel_df["e_dz"].loc[i] - erel_df["e_dz"].loc[j]) < 0.1] try: # Throw a name error here if you wanna only run the except cores = max(os.cpu_count() - 2, 1) with Pool(cores) as p: values = p.map(dict_key_rmsd, duplicates_candidates) liganddata["rmsd_candidates"] = {key: value for key, value in zip(duplicates_candidates, values)} # The less cool, non-parallel way #liganddata["rmsd_candidates"] = {candidate_pair: float(rmsd_matrix(candidate_pair)[0,1]) for candidate_pair in duplicates_candidates} # keep all RMSD for potential debugging liganddata["duplicates"] = [candidate_pair for candidate_pair in liganddata["rmsd_candidates"] if liganddata["rmsd_candidates"][candidate_pair] < 0.2] except: # RDkit failed to generate Mol objects and thus could not compute RMSD, or some of the internal structures in those mol files are different despite actually being the same. Default to duplicate detection based on dipole moment and chemical shift similarity #! log this on ligand level for double-checking err = "Warning: RDKit error at duplicate RMSD testing. Please double check." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") dipole_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["dipolemoment"] - liganddata["confdata"][j]["properties"]["dipolemoment"]) < 0.025]) nmr_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["nmr_P"] - liganddata["confdata"][j]["properties"]["nmr_P"]) < 0.1]) liganddata["duplicates"] = sorted(dipole_candidates & nmr_candidates) liganddata["removed_duplicates"] = [erel_df.loc[list(pair)]["g_tz_gas"].idxmax() for pair in liganddata["duplicates"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["removed_duplicates"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) # Boltzmann averaging #boltzfacs = {conformer: np.exp(-liganddata["relative_energies_df"]["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} boltzfacs = {conformer: np.exp(-erel_df["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} Q = sum(boltzfacs.values()) liganddata["boltzmann_weights"] = {conformer: float(boltzfacs[conformer]/Q) for conformer in liganddata["conformers"] } # probability for prop in boltzproperties: confsmissingprop = [conf for conf in liganddata["conformers"] if prop not in liganddata["confdata"][conf]["properties"].keys()] if len(confsmissingprop) == 0: liganddata["boltzmann_averaged_data"][prop] = sum([liganddata["boltzmann_weights"][conf] * liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"]]) else: # if a single conformer is missing a property value, set Boltzmann-average to None #! log this as a ligand-level error with prop and confsmissingprop err = f"Warning: {len(confsmissingprop)}/{len(liganddata['conformers'])} conformers missing values for property {prop}: {','.join(confsmissingprop)}." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") liganddata["boltzmann_averaged_data"][prop] = None continue # "Condensed" properties liganddata["vburminconf"] = liganddata["conformers"][np.argmin([liganddata["confdata"][conf]["properties"]["vbur_vbur"] for conf in liganddata["conformers"]])] for prop in mmproperties: proplist = [liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"] if prop in liganddata["confdata"][conf]["properties"].keys()] # if a single conformer is missing a property value, still perform min/max analysis (Boltzmann-average will be None to indicate missing value(s)) # if all confs are missing this prop, set min/max/delta to None if len(proplist) == 0: liganddata["min_data"][prop] = None liganddata["max_data"][prop] = None liganddata["delta_data"][prop] = None liganddata["vburminconf_data"][prop] = None else: liganddata["min_data"][prop] = min(proplist) liganddata["max_data"][prop] = max(proplist) liganddata["delta_data"][prop] = liganddata["max_data"][prop] - liganddata["min_data"][prop] liganddata["vburminconf_data"][prop] = liganddata["confdata"][liganddata["vburminconf"]]["properties"][prop] liganddata["time_all"] = sum([liganddata["confdata"][conf]["t_total"] for conf in liganddata["conformers_all"] if "t_total" in liganddata["confdata"][conf].keys()]) with open(cwd/f"{ligand}_data.yml","w") as f: yaml.dump({k:v for k,v in liganddata.items() if k != "confdata"},f,Dumper=Dumper) with open(cwd/f"{ligand}_confdata.yml","w") as f: yaml.dump(liganddata["confdata"],f,Dumper=Dumper) erel_df.to_csv(cwd/f"{ligand}_relative_energies.csv",sep=";") return(liganddata,status) def main_split_logs(cwd, ligand): if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() # if not (cwd/"done").exists(): # (cwd/"done").mkdir() conformers = [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] conformers_good = [] for conformer in conformers: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand, conformer) if status != "Error": #(cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") conformers_good.append(conformer) return(conformers_good) if __name__ == '__main__': starttime_all = time.time() ligname = re.compile("[0-9]{8}") ligands = sorted([i.name for i in cwd.iterdir() if (ligname.match(i.name) and i.is_dir())]) conformers = {ligand: [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] for ligand in ligands} if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() if not (cwd/"done").exists(): (cwd/"done").mkdir() for ligand in ligands: for conformer in conformers[ligand]: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand,conformer) if status != "Error": (cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") if (cwd/"allligands_data.yml").exists(): with open(cwd/"allligands_data.yml","r") as f: allliganddata = yaml.load(f,Loader=Loader) else: allliganddata = {} for ligand in ligands: print(ligand) print(conformers[ligand]) if ligand in allliganddata.keys(): allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand],allliganddata[ligand]) else: allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand]) with open(cwd/"allligands_data.yml","w") as f: yaml.dump(allliganddata,f,Dumper=Dumper) variants = ["boltz","min","max","delta","vburminconf"] columns = [i+"_boltz" for i in boltzproperties if i not in mmproperties] + [f"{i}_{j}" for i,j in itertools.product(mmproperties,variants)]# + ["t_total","number_of_conformers"] df = pd.DataFrame(columns = columns,index = ligands) for l in ligands: for c in columns: print(allliganddata[l]["properties"]) exit() df.loc[l][c] = allliganddata[l]["properties"][c] df["t_total"] = [allliganddata[l]["t_total"] for l in ligands] df["number_of_conformers"] = [allliganddata[l]["number_of_conformers"] for l in ligands] df.to_csv("allligands_data.csv",sep=";") print(f"All done. Total time: {round((time.time()-starttime_all),2)} sec")

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import os,re,itertools,time import numpy as np import pandas as pd import pathlib as pl cwd = pl.Path.cwd() import yaml from yaml import CLoader as Loader from yaml import CDumper as Dumper from rdkit import Chem,Geometry from rdkit.Chem import rdmolfiles, AllChem, rdMolAlign,rdmolops from multiprocessing import Pool import morfeus from PL_split_logs_201006 import split_log from PL_conformer_selection_200411 import mirror_mol, delete_element_from_rdkitmol, delete_haloalkane_halides as vmin import P_int_200916 as P_int "H": 0.32,"He": 0.46,"Li": 1.2,"Be": 0.94,"B": 0.77,"C": 0.75,"N": 0.71,"O": 0.63,"F": 0.64,"Ne": 0.67,"Na": 1.4,"Mg": 1.25,"Al": 1.13,"Si": 1.04,"P": 1.1,"S": 1.02,"Cl": 0.99,"Ar": 0.96,"K": 1.76,"Ca": 1.54,"Sc": 1.33,"Ti": 1.22,"V": 1.21,"Cr": 1.1,"Mn": 1.07,"Fe": 1.04,"Co": 1.0,"Ni": 0.99,"Cu": 1.01,"Zn": 1.09,"Ga": 1.12,"Ge": 1.09,"As": 1.15,"Se": 1.1,"Br": 1.14,"Kr": 1.17,"Rb": 1.89,"Sr": 1.67,"Y": 1.47,"Zr": 1.39,"Nb": 1.32,"Mo": 1.24,"Tc": 1.15,"Ru": 1.13,"Rh": 1.13,"Pd": 1.08,"Ag": 1.15,"Cd": 1.23,"In": 1.28,"Sn": 1.26,"Sb": 1.26,"Te": 1.23,"I": 1.32,"Xe": 1.31,"Cs": 2.09,"Ba": 1.76,"La": 1.62,"Ce": 1.47,"Pr": 1.58,"Nd": 1.57,"Pm": 1.56,"Sm": 1.55,"Eu": 1.51,"Gd": 1.52,"Tb": 1.51,"Dy": 1.5,"Ho": 1.49,"Er": 1.49,"Tm": 1.48,"Yb": 1.53,"Lu": 1.46,"Hf": 1.37,"Ta": 1.31,"W": 1.23,"Re": 1.18,"Os": 1.16,"Ir": 1.11,"Pt": 1.12,"Au": 1.13,"Hg": 1.32,"Tl": 1.3,"Pb": 1.3,"Bi": 1.36,"Po": 1.31,"At": 1.38,"Rn": 1.42,"Fr": 2.01,"Ra": 1.81,"Ac": 1.67,"Th": 1.58,"Pa": 1.52,"U": 1.53,"Np": 1.54,"Pu": 1.55 } # some constants R = 0.0019872036 #kcal mol^-1 K^-1 T = 298.15 #K hartree_kcalmol = 627.50947 periodictable = ["Bq","H","He","Li","Be","B","C","N","O","F","Ne","Na","Mg","Al","Si","P","S","Cl","Ar","K","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge","As","Se","Br","Kr","Rb","Sr","Y","Zr","Nb","Mo","Tc","Ru","Rh","Pd","Ag","Cd","In","Sn","Sb","Te","I","Xe","Cs","Ba","La","Ce","Pr","Nd","Pm","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu","Hf","Ta","W","Re","Os","Ir","Pt","Au","Hg","Tl","Pb","Bi","Po","At","Rn","Fr","Ra","Ac","Th","Pa","U","Np","Pu","Am","Cm","Bk","Cf","Es","Fm","Md","No","Lr","Rf","Db","Sg","Bh","Hs","Mt","Ds","Rg","Uub","Uut","Uuq","Uup","Uuh","Uus","Uuo","X"] def get_conmat(elements, coords): # partially based on code from Robert Paton's Sterimol script, which based this part on Grimme's D3 code # elements is a list of strings, coords is a numpy array or nested list of shape N_atoms x 3 if type(coords) == list: coords = np.asarray(coords) natom = len(elements) #max_elem = 94 k1 = 16.0 k2 = 4.0/3.0 conmat = np.zeros((natom,natom)) for i in range(0,natom): if elements[i] not in rcov.keys(): continue for iat in range(0,natom): if elements[iat] not in rcov.keys(): continue if iat != i: dxyz = coords[iat]-coords[i] r = np.linalg.norm(dxyz) rco = rcov[elements[i]]+rcov[elements[iat]] rco = rco*k2 rr=rco/r damp=1.0/(1.0+np.math.exp(-k1*(rr-1.0))) if damp > 0.85: #check if threshold is good enough for general purpose conmat[i,iat],conmat[iat,i] = 1,1 return(conmat) def add_valence(elements,coords,conmat,base_idx,add_element="Pd"): # Adds a valence to base so that the angle to the previous substituents is maximized and reorders the coordinate output for convenience # add_element: add any of the following elements: distpx = {"O":1.5,"Se":2.12,"Pd":2.28,"X":1.8} # typical bond distances to P if type(coords) == list: coords = np.asarray(coords) num_atoms = len(elements) coord_base = coords[base_idx] base_element = elements[base_idx] vec = np.array([0.0,0.0,0.0]) bonded = [] for atom in range(num_atoms): if conmat[base_idx][atom]: bonded.append(atom) vec += coord_base - coords[atom] coordox = distpx[add_element]*vec/np.linalg.norm(vec) + coord_base atoms = [x for x in range(num_atoms+1)] coords_temp = np.vstack((coords,coordox)) if sum(get_conmat(elements+[add_element],coords_temp)[-1]) != 1.0: print(" Warning: possible collision!") # sort coordinates so that base is first, add_element is second, and the other atoms bonded to base are next elements_new = [base_element,add_element]+[elements[a] for a in bonded] + [a for i,a in enumerate(elements) if i not in [base_idx]+bonded] coords_new = np.vstack((coord_base, coordox, coords[bonded], coords[[i for i,a in enumerate(elements) if i not in [base_idx]+bonded]])) return(elements_new, coords_new) def write_xyz(elements,coords,filename): with open(filename,"w") as f: f.write(f"{len(elements)}\n\n") for i,a in enumerate(elements): f.write(f"{a.title():>3} " + " ".join([f"{coords[i][j]:15f}" for j in range(3)]) + "\n") def rmsd_matrix(conformers): molobjects = [rdmolfiles.MolFromMolFile(str(cwd/conformer/f"{conformer}_opt.sdf"),removeHs=False,strictParsing=False) for conformer in conformers] molobjects = [Chem.RemoveHs(mol) for mol in molobjects] # Remove all H: optional but speeds up RMSD calculation molobjects = [delete_haloalkane_halides(mol) for mol in molobjects] # Remove halides in perhaloalkyl moieties. Improves RMSD matching and timing molobjects_inv = [mirror_mol(mol) for mol in molobjects] # create mirror images of each conformer rmsd_mat = np.zeros((len(conformers),len(conformers))) for i,j in itertools.product(range(len(conformers)),range(len(conformers))): if i<j: continue if i==j: rmsd_mat[i,j] = 1 else: rmsd_mat[i,j] = min((rdMolAlign.GetBestRMS(molobjects[i],molobjects[j]),rdMolAlign.GetBestRMS(molobjects[i],molobjects_inv[j]))) rmsd_mat[j,i] = rmsd_mat[i,j] return(rmsd_mat) def dict_key_rmsd(candidate_pair): return float(rmsd_matrix(candidate_pair)[0,1]) # which energies to read from which log-file energylogs = { "e_dz":"freq", "e_tz_gas":"nbo", "e_tz_gas":"sp", "e_tz_solv":"solv", "e_tz_ra":"ra", "e_tz_rc":"rc", } # which properties to read from which log-file proplogs = { "freq":["nimag","g","t"], "sp" :["dipole","homo","qpole","t"], "ra" :["homo","nbo","t"], "rc" :["homo","nbo","t"], "nbo" :["nbo","nborbsP","t"], "nmr" :["nmr","t"], "efg" :["efg","nuesp","t"], "solv":["ecds","t"], } # assign names to each descriptor propoutput = { "freq_g": ["","g"], "freq_nimag": ["nimag"], "sp_dipole": ["dipolemoment",], "sp_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "ra_homo":["somo_ra","","","",""], "rc_homo":["somo_rc","","","",""], "sp_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], "nbo_nbo": ["nbo_P"], "ra_nbo": ["nbo_P_ra","spindens_P_ra"], "rc_nbo": ["nbo_P_rc","spindens_P_rc"], "nmr_nmr": ["nmr_P","nmrtens_sxx_P","nmrtens_syy_P","nmrtens_szz_P",], "efg_efg": ["efg_amp_P","efgtens_xx_P","efgtens_yy_P","efgtens_zz_P"], "efg_nuesp": ["nuesp_P",], "solv_ecds": ["E_solv_cds"], "nbo_dipole": ["dipolemoment",], "nbo_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "nbo_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], } boltzproperties = ['vmin_vmin','vmin_r','dipolemoment', 'fmo_e_homo', 'fmo_e_lumo', 'fmo_mu', 'fmo_eta', 'fmo_omega', 'somo_ra', 'somo_rc', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'nbo_P', 'nbo_P_ra', 'spindens_P_ra', 'nbo_P_rc', 'spindens_P_rc', 'nmr_P', 'nmrtens_sxx_P', 'nmrtens_syy_P', 'nmrtens_szz_P', 'efg_amp_P', 'efgtens_xx_P', 'efgtens_yy_P', 'efgtens_zz_P', 'nuesp_P', 'E_solv_cds', 'nbo_lp_P_percent_s', 'nbo_lp_P_occ', 'nbo_lp_P_e', 'nbo_bd_e_max', 'nbo_bd_e_avg', 'nbo_bds_e_min', 'nbo_bds_e_avg', 'nbo_bd_occ_min', 'nbo_bd_occ_avg', 'nbo_bds_occ_max', 'nbo_bds_occ_avg', 'E_solv_total', 'E_solv_elstat', 'E_oxidation', 'E_reduction', 'fukui_p', 'fukui_m', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_ratio_vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL',"Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] # "vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", mmproperties = ['dipolemoment', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL'] # ,"vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", Pintresults = ["Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] def morfeus_properties(elements,coordinates,confdata): # Morfeus: Sterimol, Vbur, pyr morfdict = {} if "pyr_P" not in confdata.keys() and confdata["p_val"] == 3: # Pyramidalization - two equivalent measurments P and alpha pyr = morfeus.Pyramidalization(elements=elements,coordinates=coordinates,atom_index=1,excluded_atoms=[2]) # remove Pd morfdict["pyr_P"] = float(pyr.P) morfdict["pyr_alpha"] = float(pyr.alpha) if "vbur_vbur" not in confdata.keys(): #Buried volume - get quadrant volumes and distal volume # iterate through P-substituents, aligning the quadrants paralell to each once (= xz_plane definition) # Metal/point of reference should be 2.28 A away from P # z_axis_atoms: P # xz_plane_atoms: each of the substituents once # keep lowest and highest quadrant and octant volume across all three orientations of the coordinate system # keep highest difference of any neighboring quadrant volume # keep volume in each of the two hemispheres qvbur_all = np.array([]) qvdist_all = np.array([]) qvtot_all = np.array([]) max_delta_qvbur_all = [] max_delta_qvtot_all = [] ovbur_all = np.array([]) ovtot_all = np.array([]) for i in range(3):#confdata["p_val"]): bv = morfeus.BuriedVolume(elements,coordinates,2,excluded_atoms=[2],z_axis_atoms=[1],xz_plane_atoms=[3+i]) bv.octant_analysis() bv.compute_distal_volume(method="buried_volume",octants=True) vbur = bv.buried_volume # these are identical for each iteration vdist = bv.distal_volume # vtot = vbur + vdist # qvbur = np.asarray(list(bv.quadrants["buried_volume"].values())) qvdist = np.asarray(list(bv.quadrants["distal_volume"].values())) qvtot = qvbur + qvdist qvbur_all = np.append(qvbur_all,qvbur) qvtot_all = np.append(qvtot_all,qvtot) max_delta_qvbur_all.append(max([abs(qvbur[j]-qvbur[j-1]) for j in range(4)])) max_delta_qvtot_all.append(max([abs(qvtot[j]-qvtot[j-1]) for j in range(4)])) ovbur = np.asarray(list(bv.octants["buried_volume"].values())) ovdist = np.asarray(list(bv.octants["distal_volume"].values())) ovtot = ovbur + ovdist ovbur_all = np.append(ovbur_all,ovbur) ovtot_all = np.append(ovtot_all,ovtot) near_vbur = ovbur[4:].sum() # these are identical for each iteration far_vbur = ovbur[:4].sum() # near_vtot = ovtot[4:].sum() # far_vtot = ovtot[:4].sum() # morfdict["vbur_vbur"] = vbur morfdict["vbur_vtot"] = float(vtot) morfdict["vbur_ratio_vbur_vtot"] = float(vbur/vtot) morfdict["vbur_qvbur_min"] = float(min(qvbur_all)) morfdict["vbur_qvbur_max"] = float(max(qvbur_all)) morfdict["vbur_qvtot_min"] = float(min(qvtot_all)) morfdict["vbur_qvtot_max"] = float(max(qvtot_all)) morfdict["vbur_max_delta_qvbur"] = float(max(max_delta_qvbur_all)) morfdict["vbur_max_delta_qvtot"] = float(max(max_delta_qvtot_all)) morfdict["vbur_ovbur_min"] = float(min(ovbur_all)) morfdict["vbur_ovbur_max"] = float(max(ovbur_all)) morfdict["vbur_ovtot_min"] = float(min(ovtot_all)) morfdict["vbur_ovtot_max"] = float(max(ovtot_all)) morfdict["vbur_near_vbur"] = float(near_vbur) morfdict["vbur_far_vbur"] = float(far_vbur) morfdict["vbur_near_vtot"] = float(near_vtot) morfdict["vbur_far_vtot"] = float(far_vtot) if "sterimol_B1" not in confdata.keys(): # Sterimol # for Sterimol values matching Rob Paton's implementation: patonradii = morfeus.helpers.get_radii(elements, radii_type="bondi") patonradii = np.array(patonradii) patonradii[patonradii == 1.2] = 1.09 sterimol = morfeus.Sterimol(elements, coordinates, 2, 1, radii=patonradii, n_rot_vectors=3600) morfdict["sterimol_B1"] = float(sterimol.B_1_value) morfdict["sterimol_B5"] = float(sterimol.B_5_value) morfdict["sterimol_L"] = float(sterimol.L_value) sterimol_bur = morfeus.Sterimol(elements, coordinates, 2, 1,calculate=False,radii=patonradii, n_rot_vectors=3600) sterimol_bur.bury(sphere_radius=5.5,method="delete",radii_scale=0.5) morfdict["sterimol_burB1"] = float(sterimol_bur.B_1_value) morfdict["sterimol_burB5"] = float(sterimol_bur.B_5_value) morfdict["sterimol_burL"] = float(sterimol_bur.L_value) return(morfdict) def gp_properties(ligand,conformer,p_idx): gpdict = {} gpdict["properties"] = {} contents = { "streams":{}, "filecont":{}, } for e,log in energylogs.items(): contents["streams"][log] = gp.get_outstreams(cwd/conformer/f"{conformer}_{log}.log") if contents["streams"][log] == "failed or incomplete job": return({"error":True}) else: gpdict[e] = gp.get_e_hf(contents["streams"][log]) gpdict["error"] = False for log in proplogs.keys(): contents["filecont"][log] = gp.get_filecont(cwd/conformer/f"{conformer}_{log}.log") for prop in proplogs[log]: gpresults = gp.jobtypes[prop][0](contents[gp.jobtypes[prop][1]][log],p_idx) if prop == "nborbsP": gpdict["properties"].update(gpresults) elif prop == "t": gpdict[f"{log}_t"] = gpresults elif prop in ["e_dz","g","e_tz_gas","e_tz_solv","e_tz_ra","e_tz_rc","nimag"]: gpdict.update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) else: gpdict["properties"].update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) gpdict["g_tz_gas"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_gas"] gpdict["g_tz_solv"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_solv"] gpdict["properties"]["E_solv_total"] = (gpdict["e_tz_solv"] - gpdict["e_tz_gas"]) * hartree_kcalmol gpdict["properties"]["E_solv_elstat"] = gpdict["properties"]["E_solv_total"] - gpdict["properties"]["E_solv_cds"] gpdict["properties"]["E_oxidation"] = gpdict["e_tz_rc"] - gpdict["e_tz_gas"] gpdict["properties"]["E_reduction"] = gpdict["e_tz_ra"] - gpdict["e_tz_gas"] gpdict["properties"]["fukui_p"] = gpdict["properties"]["nbo_P"]-gpdict["properties"]["nbo_P_ra"] gpdict["properties"]["fukui_m"] = gpdict["properties"]["nbo_P_rc"]-gpdict["properties"]["nbo_P"] gpdict["t_total"] = sum([gpdict[f"{log}_t"] for log in proplogs.keys()]) if "" in gpdict.keys(): del gpdict[""] if "" in gpdict["properties"].keys(): del gpdict["properties"][""] return(gpdict) def read_conformer(cwd, ligand, conformer): confdata = {} errors = [] checklogs = [cwd/conformer/f"{conformer}_{l}.log" for l in proplogs.keys() if not (cwd/conformer/f"{conformer}_{l}.log").exists()] if len(checklogs) != 0: err = f"Missing Gaussian log files, flagged in read_conformer: {','.join([chkl.name for chkl in checklogs])}" errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True return(confdata,errors) if "elements_pd" not in confdata.keys(): def read_gaussian_logfile(fn): time0=time.time() read=False for line in open(fn,"r"): if read: if "---" in line and len(elements)>0: read=False if read: if "X" not in line and "---" not in line: atomnum = int(line.split()[1]) el = periodictable[atomnum] elements.append(el) coordinates.append([float(line.split()[3]),float(line.split()[4]), float(line.split()[5])]) if "Coordinates (Angstroms)" in line: coordinates, elements = [], [] read=True time1=time.time() print("gaussian log parser done in %.2f seconds"%(time1-time0)) return(coordinates, elements) coordinates, elements = read_gaussian_logfile(str(cwd/conformer/f"{conformer}_opt.log")) coordinates_a = np.array(coordinates) conmat = get_conmat(elements,coordinates_a) p_idx = [i for i in range(len(elements)) if elements[i] == "P" and sum(conmat[i]) <= 3][0] elements_pd, coordinates_pd = add_valence(elements,coordinates,conmat,p_idx,add_element="Pd") if not (cwd/conformer/f"{conformer}_opt_Pd.xyz").exists(): write_xyz(elements, coordinates, cwd/conformer/f"{conformer}_opt.xyz") os.system("obabel -ixyz %s -osdf >> %s"%(str(cwd/conformer/f"{conformer}_opt.xyz"), str(cwd/conformer/f"{conformer}_opt.sdf"))) write_xyz(elements_pd,coordinates_pd,cwd/conformer/f"{conformer}_opt_Pd.xyz") confdata["coords"] = coordinates confdata["coords_pd"] = coordinates_pd.tolist() confdata["elements"] = elements confdata["elements_pd"] = elements_pd confdata["conmat"] = conmat.tolist() confdata["p_idx"] = p_idx confdata["p_val"] = int(sum(conmat[p_idx])) confdata["properties"] = {} a.update(gp_properties(ligand,conformer,confdata["p_idx"])) if confdata["error"]: err = "Error in the Gaussian computations, flagged in read_conformer, please check log files." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) if confdata["nimag"] != 0: err = f"Number of imaginary frequencies: {confdata['nimag']}." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) confdata["error"] = True return(confdata,errors) confdata["properties"].update(morfeus_properties(confdata["elements_pd"],confdata["coords_pd"],confdata)) disp = "d3" pint_read = P_int.read_dedout(cwd/conformer,conformer,disp)+P_int.read_multiwfnout(cwd/conformer,conformer)+P_int.read_disp(cwd/conformer,conformer,disp) confdata["properties"].update({Pintresults[i]:float(pint_read[i]) for i in range(7)}) try: if "vmin_vmin" not in confdata.keys(): vminob = vmin.get_vmin(f"{conformer}.fchk",str(cwd/conformer)+"/",True) confdata["properties"]["vmin_vmin"] = float(vminob.v_min) confdata["properties"]["vmin_r"] = float(vminob.r_min) except: err = f"Vmin FileNotFoundError." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) def read_ligand(cwd, ligand, conformers, liganddata = {}): status = {"ligandlevel": [],} if len(liganddata.keys()) == 0: if (cwd/f"{ligand}_data.yml").exists(): with open(cwd/f"{ligand}_data.yml","r") as f: liganddata = yaml.load(f,Loader=Loader) if (cwd/f"{ligand}_confdata.yml").exists(): with open(cwd/f"{ligand}_confdata.yml","r") as f: liganddata["confdata"] = yaml.load(f,Loader=Loader) else: liganddata = { "conformers_all": conformers, "conformers": conformers.copy(), "number_of_conformers": len(conformers), "removed_duplicates": [], "confdata": {}, "boltzmann_averaged_data": {}, "min_data": {}, "max_data": {}, "delta_data": {}, "vburminconf_data": {}, } newconfs = 0 for conformer in conformers: if conformer in liganddata["removed_duplicates"]: continue print(conformer) if conformer in liganddata["confdata"].keys(): pass elif (cwd/conformer/f"{conformer}_data.yml").exists(): with open(cwd/conformer/f"{conformer}_data.yml","r") as f: liganddata["confdata"][conformer] = yaml.load(f,Loader=Loader) newconfs += 1 else: print("read conformer data") liganddata["confdata"][conformer],status[conformer] = read_conformer(cwd, ligand, conformer) newconfs += 1 if newconfs > 0: liganddata["conformers_w_error"] = [conformer for conformer in liganddata["conformers"] if liganddata["confdata"][conformer]["error"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["conformers_w_error"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) energies = ["e_dz","g","e_tz_gas","g_tz_gas","e_tz_solv","g_tz_solv"] liganddata["energies"] = {} liganddata["relative_energies"] = {} for e in energies: liganddata["energies"][e] = {conformer: liganddata["confdata"][conformer][e] for conformer in liganddata["conformers"]} liganddata[e+"_min"] = min(liganddata["energies"][e].values()) liganddata[e+"_minconf"] = list(liganddata["energies"][e].keys())[np.argmin(list(liganddata["energies"][e].values()))] liganddata["relative_energies"][e+"_rel"] = {conformer: (liganddata["energies"][e][conformer]-liganddata[e+"_min"])*hartree_kcalmol for conformer in liganddata["conformers"]} erel_df = pd.DataFrame([liganddata["relative_energies"][e+"_rel"] for e in energies],index=energies).T liganddata["relative_energies_dict"] = erel_df.to_dict() duplicates_candidates = [(i,j) for i,j in itertools.combinations(liganddata["conformers"],2) if abs(erel_df["e_dz"].loc[i] - erel_df["e_dz"].loc[j]) < 0.1] try: cores = max(os.cpu_count() - 2, 1) with Pool(cores) as p: values = p.map(dict_key_rmsd, duplicates_candidates) liganddata["rmsd_candidates"] = {key: value for key, value in zip(duplicates_candidates, values)} [candidate_pair for candidate_pair in liganddata["rmsd_candidates"] if liganddata["rmsd_candidates"][candidate_pair] < 0.2] except: err = "Warning: RDKit error at duplicate RMSD testing. Please double check." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") dipole_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["dipolemoment"] - liganddata["confdata"][j]["properties"]["dipolemoment"]) < 0.025]) nmr_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["nmr_P"] - liganddata["confdata"][j]["properties"]["nmr_P"]) < 0.1]) liganddata["duplicates"] = sorted(dipole_candidates & nmr_candidates) liganddata["removed_duplicates"] = [erel_df.loc[list(pair)]["g_tz_gas"].idxmax() for pair in liganddata["duplicates"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["removed_duplicates"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) boltzfacs = {conformer: np.exp(-erel_df["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} Q = sum(boltzfacs.values()) liganddata["boltzmann_weights"] = {conformer: float(boltzfacs[conformer]/Q) for conformer in liganddata["conformers"] } for prop in boltzproperties: confsmissingprop = [conf for conf in liganddata["conformers"] if prop not in liganddata["confdata"][conf]["properties"].keys()] if len(confsmissingprop) == 0: liganddata["boltzmann_averaged_data"][prop] = sum([liganddata["boltzmann_weights"][conf] * liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"]]) else: err = f"Warning: {len(confsmissingprop)}/{len(liganddata['conformers'])} conformers missing values for property {prop}: {','.join(confsmissingprop)}." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") liganddata["boltzmann_averaged_data"][prop] = None continue liganddata["vburminconf"] = liganddata["conformers"][np.argmin([liganddata["confdata"][conf]["properties"]["vbur_vbur"] for conf in liganddata["conformers"]])] for prop in mmproperties: proplist = [liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"] if prop in liganddata["confdata"][conf]["properties"].keys()] if len(proplist) == 0: liganddata["min_data"][prop] = None liganddata["max_data"][prop] = None liganddata["delta_data"][prop] = None liganddata["vburminconf_data"][prop] = None else: liganddata["min_data"][prop] = min(proplist) liganddata["max_data"][prop] = max(proplist) liganddata["delta_data"][prop] = liganddata["max_data"][prop] - liganddata["min_data"][prop] liganddata["vburminconf_data"][prop] = liganddata["confdata"][liganddata["vburminconf"]]["properties"][prop] liganddata["time_all"] = sum([liganddata["confdata"][conf]["t_total"] for conf in liganddata["conformers_all"] if "t_total" in liganddata["confdata"][conf].keys()]) with open(cwd/f"{ligand}_data.yml","w") as f: yaml.dump({k:v for k,v in liganddata.items() if k != "confdata"},f,Dumper=Dumper) with open(cwd/f"{ligand}_confdata.yml","w") as f: yaml.dump(liganddata["confdata"],f,Dumper=Dumper) erel_df.to_csv(cwd/f"{ligand}_relative_energies.csv",sep=";") return(liganddata,status) def main_split_logs(cwd, ligand): if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() conformers = [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] conformers_good = [] for conformer in conformers: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand, conformer) if status != "Error": conformers_good.append(conformer) return(conformers_good) if __name__ == '__main__': starttime_all = time.time() ligname = re.compile("[0-9]{8}") ligands = sorted([i.name for i in cwd.iterdir() if (ligname.match(i.name) and i.is_dir())]) conformers = {ligand: [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] for ligand in ligands} if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() if not (cwd/"done").exists(): (cwd/"done").mkdir() for ligand in ligands: for conformer in conformers[ligand]: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand,conformer) if status != "Error": (cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") if (cwd/"allligands_data.yml").exists(): with open(cwd/"allligands_data.yml","r") as f: allliganddata = yaml.load(f,Loader=Loader) else: allliganddata = {} for ligand in ligands: print(ligand) print(conformers[ligand]) if ligand in allliganddata.keys(): allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand],allliganddata[ligand]) else: allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand]) with open(cwd/"allligands_data.yml","w") as f: yaml.dump(allliganddata,f,Dumper=Dumper) variants = ["boltz","min","max","delta","vburminconf"] columns = [i+"_boltz" for i in boltzproperties if i not in mmproperties] + [f"{i}_{j}" for i,j in itertools.product(mmproperties,variants)] df = pd.DataFrame(columns = columns,index = ligands) for l in ligands: for c in columns: print(allliganddata[l]["properties"]) exit() df.loc[l][c] = allliganddata[l]["properties"][c] df["t_total"] = [allliganddata[l]["t_total"] for l in ligands] df["number_of_conformers"] = [allliganddata[l]["number_of_conformers"] for l in ligands] df.to_csv("allligands_data.csv",sep=";") print(f"All done. Total time: {round((time.time()-starttime_all),2)} sec")

true

f71fdf25a6bbbf3c2ecdf90eda58463aa369ca88

52,543

py

Python

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

# Copyright 2021 The T5 Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Classes for data loading and processing. Defines Tasks, TaskRegistry, Mixture, and MixtureRegistry """ import abc import collections import inspect import json import os import re from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Sequence, Tuple, Type, Union from absl import logging import dataclasses import numpy as np from packaging import version from t5.seqio import utils from t5.seqio.feature_converters import FeatureConverter from t5.seqio.vocabularies import Vocabulary import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds import typing_extensions _DEFAULT_FEATURE_KEYS = ["inputs", "targets"] _VALID_TASK_NAME_REGEX = re.compile(r"^[\w\d\._]+$") _MAX_EXAMPLES_TO_MEM_CACHE = 10000 SHUFFLE_BUFFER_SIZE = 1000 @dataclasses.dataclass(frozen=True) class Feature: """A container for attributes of output features of data providers.""" vocabulary: Vocabulary add_eos: bool = True required: bool = True dtype: tf.DType = tf.int32 @dataclasses.dataclass(frozen=True) class ShardInfo: """A container for specifying sharding info.""" index: int num_shards: int class DatasetProviderBase(metaclass=abc.ABCMeta): """Abstract base for classes that provide a tf.data.Dataset.""" @abc.abstractproperty def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractproperty def splits(self) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, sequence_length: int, split: str, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: int = 1 ) -> tf.data.Dataset: """Returns the requested tf.data.Dataset.""" raise NotImplementedError @abc.abstractmethod def num_input_examples(self, split: str) -> int: raise NotImplementedError class DatasetProviderRegistry(object): """Base for registry of data providers. Subclasses must wrap `get` method to override the return type for pytype. TODO(adarob): Remove the need to override `get`. """ # Class variables must be defined in subclasses. _REGISTRY: MutableMapping[str, DatasetProviderBase] _PROVIDER_TYPE: Type[DatasetProviderBase] @classmethod def add_provider(cls, name: str, provider): """Adds a data provider instance to the registry.""" if name in cls._REGISTRY: raise ValueError("Attempting to register duplicate provider: %s" % name) if not isinstance(provider, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, type(provider).__name__)) cls._REGISTRY[name] = provider @classmethod def add( cls, name: str, provider_cls, *provider_args, **provider_kwargs ): """Instantiates and adds provider to the registry.""" if not issubclass(provider_cls, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, provider_cls)) provider = provider_cls(*provider_args, **provider_kwargs) cls.add_provider(name, provider) return provider @classmethod def remove(cls, name): """Remove provider from the registry, if it exists.""" if name in cls._REGISTRY: del cls._REGISTRY[name] @classmethod def get(cls, name): """Returns provider from the registry.""" if name not in cls._REGISTRY: raise ValueError("Provider name not registered: %s" % name) return cls._REGISTRY[name] @classmethod def names(cls): """Returns all provider names in registry.""" return cls._REGISTRY.keys() @classmethod def reset(cls): """Removes all of the registered tasks.""" cls._REGISTRY = {} @classmethod def get_dataset( cls, name, sequence_length, split, use_cached=False, shuffle=True, seed=None, shard_info=None, num_epochs=1): """Returns the requested tf.data.Dataset.""" return cls.get(name).get_dataset( sequence_length=sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) # =============================== DataSources ================================== class DataSource(DatasetProviderBase): """A `DatasetProvider` that provides raw data from an input source. Inherits all abstract methods and properties of `DatasetProviderBase` except those overidden below. """ def __init__( self, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None): self._splits = tuple(splits) self._num_input_examples = ( dict(num_input_examples) if num_input_examples is not None else None) @property def splits(self) -> Sequence[str]: return self._splits @property def output_features(self) -> Mapping[str, Feature]: """Override unused property of `DatasetProviderBase`.""" raise NotImplementedError @abc.abstractmethod def list_shards(self, split: str) -> Sequence[str]: """Returns string identifiers of input shards.""" raise NotImplementedError @abc.abstractmethod def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: """Overrides base class to add shard identifier and remove use_cached. Args: split: string, the split to return. shuffle: bool, whether to shuffle the input source. seed: tf.int64 scalar tf.Tensor (or None) for shuffling input source. shard_info: optional specification for loading a shard of the split. """ raise NotImplementedError def num_input_examples(self, split: str) -> Optional[int]: if self._num_input_examples is None: return None return self._num_input_examples[split] def _validate_args(fn, expected_pos_args): """Ensure function has exactly expected positional args.""" argspec = inspect.getfullargspec(fn) expected_pos_args = tuple(expected_pos_args) actual_args = tuple(argspec.args) if actual_args[:len(expected_pos_args)] != expected_pos_args: raise ValueError( "'%s' must have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_args)) actual_pos_args = tuple( argspec.args[:-len(argspec.defaults)] if argspec.defaults else argspec.args) if actual_pos_args != expected_pos_args[:len(actual_pos_args)]: raise ValueError( "'%s' may only have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_pos_args)) class DatasetFnCallable(typing_extensions.Protocol): def __call__(self, split: str, shuffle_files: bool, seed: Optional[int] = None) -> tf.data.Dataset: ... class FunctionDataSource(DataSource): """A `DataSource` that uses a function to provide the input data.""" def __init__( self, dataset_fn: DatasetFnCallable, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None ): """FunctionDataSource constructor. Args: dataset_fn: a function with the signature `dataset_fn(split, shuffle_files)' (and optionally the variable `seed`) that returns a `tf.data.Dataset`. splits: an iterable of applicable string split names. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ _validate_args(dataset_fn, ["split", "shuffle_files"]) self._dataset_fn = dataset_fn super().__init__(splits=splits, num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: if shard_info and shard_info.num_shards > 1: raise ValueError( "`FunctionDataSource` does not support low-level sharding. Use " "tf.data.Dataset.shard instead.") if seed is None: ds = self._dataset_fn(split=split, shuffle_files=shuffle) else: _validate_args(self._dataset_fn, ["split", "shuffle_files", "seed"]) ds = self._dataset_fn(split=split, shuffle_files=shuffle, seed=seed) return ds def list_shards(self, split: str) -> Sequence[str]: return [split] class TfdsDataSource(DataSource): """A `DataSource` that uses TensorFlow Datasets to provide the input data.""" def __init__( self, tfds_name: str, tfds_data_dir: Optional[str] = None, splits: Optional[Union[Iterable[str], Mapping[str, str]]] = None ): """TfdsTask constructor. Args: tfds_name: string, the name and version number of a TFDS dataset, optionally with a config. tfds_data_dir: string, an optional path to a specific TFDS data directory to use. splits: an iterable of allowable string split names, a dict mapping allowable canonical splits (e.g., 'validation') to TFDS splits or slices (e.g., 'train[':1%']), or None. The default, None, uses all available splits from the TFDS dataset info. """ if ":" not in tfds_name: raise ValueError("TFDS name must contain a version number, got: %s" % tfds_name) self._tfds_dataset = utils.LazyTfdsLoader( tfds_name, data_dir=tfds_data_dir, split_map=splits if isinstance(splits, dict) else None) # If splits are not provided, we pass an empty tuple and use the lazy # lookup in the `splits` property. super().__init__(splits=splits or ()) @property def splits(self): """Overrides since we can't call `info.splits` until after init.""" return self._splits or self._tfds_dataset.info.splits @property def tfds_dataset(self): return self._tfds_dataset def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: return self.tfds_dataset.load( split, shuffle_files=shuffle, seed=seed, shard_info=shard_info) def num_input_examples(self, split: str) -> int: """Overrides since we can't call `info.splits` until after init.""" return self.tfds_dataset.size(split) def list_shards(self, split: str) -> Sequence[str]: return self.tfds_dataset.files(split) class FileDataSource(DataSource): """A `DataSource` that reads a file to provide the input dataset.""" def __init__( self, read_file_fn: Callable[[tf.data.Dataset], tf.data.Dataset], split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], num_input_examples: Optional[Mapping[str, int]] = None, ): """FileDataSource constructor. Args: read_file_fn: a callable for creating a `tf.data.Dataset` from a `tf.data.Dataset` of file paths, e.g., `tf.data.TFRecordDataset`. split_to_filepattern: a mapping from split names to filepatterns to be expanded with glob. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ self._split_to_filepattern = split_to_filepattern self._reader = read_file_fn super().__init__( splits=split_to_filepattern.keys(), num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: files = self.list_shards(split) if not files: raise ValueError( "No file is found for the file pattern: " f"{self._split_to_filepattern[split]}." ) files_ds = tf.data.Dataset.from_tensor_slices(np.array(files, dtype=np.str)) if shard_info: if len(files) < shard_info.num_shards: raise ValueError( f"Dataset has too few files to shard. {len(files)} files vs " f"{shard_info.num_shards} shards requested.") files_ds = files_ds.shard(shard_info.num_shards, shard_info.index) if shuffle: files_ds = files_ds.shuffle(buffer_size=16, seed=seed) return files_ds.interleave( self._reader, cycle_length=16, block_length=16, num_parallel_calls=tf.data.experimental.AUTOTUNE) def list_shards(self, split: str) -> Sequence[str]: return tf.io.gfile.glob(self._split_to_filepattern[split]) class TextLineDataSource(FileDataSource): """A `FileDataSource` that reads lines of text from a file as input.""" def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], skip_header_lines: int = 0, num_input_examples: Optional[Mapping[str, int]] = None, ): """TextLineDataSource constructor. Args: split_to_filepattern: a mapping from split names to filepatterns to be expanded with glob. skip_header_lines: int, number of header lines to skip in each source file. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ # Used during caching. self._skip_header_lines = skip_header_lines def read_file_fn(filepattern): return tf.data.TextLineDataset(filepattern).skip(skip_header_lines) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) class TFExampleDataSource(FileDataSource): """A `FileDataSource` that reads files of tf.train.Example protos as input.""" def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], feature_description: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature]], reader_cls: Type[tf.data.Dataset] = tf.data.TFRecordDataset, num_input_examples: Optional[Mapping[str, int]] = None, ): """TFExampleDataSource constructor. Args: split_to_filepattern: dict of string (split name) to either string (filename or filepattern) or list of strings (filenames or filepatterns). feature_description: dict, a mapping of string feature keys to `tf.io.FixedLenFeature` or `tf.io.VarLenFeature` values. reader_cls: `tf.data.Dataset`, a dataset class to read the input files. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ def read_file_fn(filepattern): return reader_cls(filepattern).map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) # ========================== Offline Caching Helpers =========================== def _rename_plaintext_to_pretokenized( dataset: tf.data.Dataset) -> tf.data.Dataset: """Rename cached _plaintext features to new _pretokenized standard.""" def _rename(inputs): outputs = {} for k, v in inputs.items(): if k.endswith("_plaintext"): k = k[:-len("plaintext")] + "pretokenized" outputs[k] = v return outputs return dataset.map( _rename, num_parallel_calls=tf.data.experimental.AUTOTUNE) class _CachedDataSource(FileDataSource): """A `FileDataSource` for reading datasets cached offline.""" def __init__(self, cache_dir: str, split: str): with tf.io.gfile.GFile(utils.get_cached_info_path(cache_dir, split)) as f: split_info = json.load(f) features = split_info["features"] with tf.io.gfile.GFile(utils.get_cached_stats_path(cache_dir, split)) as f: stats = json.load(f) version_when_cached = version.Version( split_info.get("seqio_version", "0.pre")) version_with_true_dtypes = version.Version("0.0.0") if version_when_cached < version_with_true_dtypes: # Assume that all int64 features are really int32. for name, feat in features.items(): if feat["dtype"] == "int64": logging.info("Casting cached '%s' to int32.", name) feat["dtype"] = "int32" # Use `FixedLenSequenceFeature` for sequences with variable length. def _feature_config(shape, dtype): if dtype in ("int32", "bool"): # int32 and bool are stored as int64 in the tf.train.Example protobuf. # TODO(adarob): Support other conversions. dtype = "int64" if shape and shape[0] is None: return tf.io.FixedLenSequenceFeature( shape[1:], dtype, allow_missing=True) return tf.io.FixedLenFeature(shape, dtype) feature_description = { feat: _feature_config(**desc) for feat, desc in features.items() } def read_file_fn(filepattern): ds = tf.data.TFRecordDataset(filepattern) ds = ds.map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) # Cast features back to the types from the info JSON since some features # must be cast for storage (e.g., in32 is stored as int64). ds = ds.map( lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) # Legacy cached datasets may use old "_plaintext" suffix. Rename to # "_pretokenized". ds = _rename_plaintext_to_pretokenized(ds) return ds split_to_filepattern = { split: "%s-*-of-*%d" % ( utils.get_cached_tfrecord_prefix(cache_dir, split), split_info["num_shards"]) } super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples={split: stats["examples"]} ) class CacheDatasetPlaceholder(object): """A placeholder to signal when in the pipeline offline caching will occur.""" def __init__(self, required=False): """CacheDatasetPlaceholder constructor. Args: required: whether the dataset must be accessed in its cached form, and on-the-fly preprocessing is disallowed. """ self._required = required @property def required(self): return self._required def __call__(self, dataset): raise RuntimeError("`CacheDatasetPlaceholder` should never be called.") # ================================ Tasks ======================================= MetricFnCallable = Callable[..., Mapping[str, float]] class Task(DatasetProviderBase): """A class to manage a dataset and its related metrics.""" def __init__( self, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[MetricFnCallable]] = None, shuffle_buffer_size: Optional[int] = SHUFFLE_BUFFER_SIZE): """Task constructor. Args: name: a unique name for the Task. source: a `DataSource` that provides a raw `tf.data.Dataset`. output_features: dict(str, Feature), output features of the Task to be passed to the model. After preprocessing, examples will be validated to ensure they include features that match this specification. Note that additional features may be included (e.g., for evaluation), but they will not be passed to the model. preprocessors: list(callable), an optional list of functions that receive a tf.data.Dataset and return a tf.data.Dataset. These will be executed sequentually and the final dataset must include features matching `output_features`. postprocess_fn: callable, an optional function that receives decoded model outputs and converts them to a form that is ready for evaluation using the metric functions in `metric_fns`. metric_fns: list(callable), an optional list of metric functions with the signature `metric_fn(targets, predictions)` to use during evaluation. If undefined or empty, no evaluation will occur on the task. shuffle_buffer_size: an optional integer to set the shuffle buffer size. If None, shuffling will be disallowed. """ if not _VALID_TASK_NAME_REGEX.match(name): raise ValueError( "Task name '%s' contains invalid characters. Must match regex: %s" % ( name, _VALID_TASK_NAME_REGEX.pattern)) metric_fns = metric_fns or [] self._predict_metric_fns = [] self._score_metric_fns = [] for metric_fn in metric_fns: pos_args = tuple( key for key, param in inspect.signature(metric_fn).parameters.items() if param.default == inspect.Parameter.empty ) if pos_args == ("targets", "scores"): self._score_metric_fns.append(metric_fn) elif pos_args == ("targets", "predictions"): self._predict_metric_fns.append(metric_fn) else: raise ValueError( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). " f"Got: {pos_args}") self._name = name self._source = source # Find optional CacheDatasetPlaceholder. preprocessors = tuple(preprocessors or []) cache_step_idxs = [ i for i, p in enumerate(preprocessors) if isinstance(p, CacheDatasetPlaceholder) ] if len(cache_step_idxs) > 1: raise ValueError( "`CacheDatasetPlaceholder` can appear at most once in the " f"preprocessing pipeline. Found {len(cache_step_idxs)} in '{name}'.") cache_step_idx = cache_step_idxs[0] if cache_step_idxs else None if cache_step_idx is not None: for prep in preprocessors[:cache_step_idx]: prep_args = inspect.signature(prep).parameters.keys() if "sequence_length" in prep_args: raise ValueError( f"'{prep.__name__}' has a `sequence_length` argument but occurs " f"before `CacheDatasetPlaceholder` in '{name}'. This is not " "allowed since the sequence length is specified at run time.") if "seed" in prep_args or "seeds" in prep_args: raise logging.warning( # pylint:disable=logging-format-interpolation f"'{prep.__name__}' has a `seed(s)` argument but occurs before " f"`CacheDatasetPlaceholder` in '{name}'. This is not recommended " "since the same samples will be used each epoch when reading " "from the cache.") self._cache_step_idx = cache_step_idx self._preprocessors = preprocessors self._metric_fns = tuple(metric_fns) self._postprocess_fn = postprocess_fn self._cache_dir = None self._stats = {} self._shuffle_buffer_size = shuffle_buffer_size self._output_features = collections.OrderedDict( sorted(list(output_features.items())) ) @property def name(self) -> str: return self._name @property def metric_fns(self) -> Sequence[MetricFnCallable]: """List of all metric functions.""" return self._predict_metric_fns + self._score_metric_fns @property def score_metric_fns(self) -> Sequence[MetricFnCallable]: """List of metric functions that use log likelihood scores.""" return self._score_metric_fns @property def predict_metric_fns(self) -> Sequence[MetricFnCallable]: """List of metric functions that use model predictions.""" return self._predict_metric_fns @property def output_features(self) -> Mapping[str, Feature]: return self._output_features @property def splits(self) -> Sequence[str]: s = self.source.splits if not s: raise ValueError(f"Task {self.name} has no splits") return s @property def source(self) -> DataSource: return self._source @property def preprocessors(self) -> Sequence[Callable[..., tf.data.Dataset]]: return self._preprocessors def num_input_examples(self, split: str) -> Optional[int]: return self.source.num_input_examples(split) def _preprocess_dataset( self, dataset: tf.data.Dataset, preprocessors: Sequence[Callable[..., tf.data.Dataset]], sequence_length: Optional[Mapping[str, int]] = None) -> tf.data.Dataset: """Sequentially applies preprocessors.""" for prep_fn in preprocessors: # prep_fn must not rely on variable length keyword args such as **kwargs. fn_args = set(inspect.signature(prep_fn).parameters.keys()) kwargs = {} if "sequence_length" in fn_args: kwargs["sequence_length"] = sequence_length if "output_features" in fn_args: kwargs["output_features"] = self.output_features dataset = prep_fn(dataset, **kwargs) return dataset def _validate_preprocessing( self, dataset: tf.data.Dataset ) -> tf.data.Dataset: """Validates preprocessed dataset, raising Exceptions if needed. Args: dataset: a tf.data.Dataset to validate. Returns: a validated tf.data.Dataset. """ actual_specs = dataset.element_spec for feat, feat_spec in self.output_features.items(): if feat not in actual_specs: if feat_spec.required: raise ValueError( "Task dataset is missing expected output feature after " f"preprocessing: {feat}") else: # It's ok that this feature does not exist. continue actual_spec = actual_specs[feat] if feat_spec.dtype != actual_spec.dtype: raise ValueError( f"Task dataset has incorrect type for feature '{feat}' after " f"preprocessing: Got {actual_spec.dtype.name}, expected " f"{feat_spec.dtype.name}") if actual_spec.shape.rank != 1: raise ValueError( f"Task dataset has incorrect rank for feature '{feat}' after " f"preprocessing: Got {actual_spec.shape.rank}, expected 1") return dataset def _trim_output_features( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]] ) -> tf.data.Dataset: """Trim output features to sequence length.""" def _trim(k: str, v: tf.Tensor) -> tf.Tensor: if k not in self.output_features or not sequence_length: return v return v[:sequence_length[k]] return dataset.map( lambda ex: {k: _trim(k, v) for k, v in ex.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) def preprocess_precache( self, dataset: tf.data.Dataset, seed: Optional[int] = None ) -> tf.data.Dataset: """Runs preprocessing steps before the optional CacheDatasetPlaceholder.""" if not self.supports_caching: return dataset with utils.map_seed_manager(seed): return self._preprocess_dataset( dataset, self._preprocessors[:self._cache_step_idx], ) def preprocess_postcache( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]], seed: Optional[int] = None ) -> tf.data.Dataset: """Runs preprocessing steps after the optional CacheDatasetPlaceholder. Args: dataset: a tf.data.Dataset sequence_length: dict mapping feature key to int length for that feature. If None, the features will not be truncated. seed: an optional random seed for deterministic preprocessing. Returns: a tf.data.Dataset """ start_idx = 0 if self.supports_caching: # Skip a sufficient number of seeds to avoid duplicating any from # pre-cache preprocessing. seed = None if seed is None else seed + 42 * self._cache_step_idx start_idx = self._cache_step_idx + 1 with utils.map_seed_manager(seed): dataset = self._preprocess_dataset( dataset, self._preprocessors[start_idx:], sequence_length=sequence_length, ) return dataset @property def cache_dir(self) -> Optional[str]: """Returns the cache directory (or None), initializing if needed.""" if not self._cache_dir: # See if cached data exists in any of the cache directories. potential_cache_dirs = [ os.path.join(d, self.name) for d in utils.get_global_cache_dirs()] for cache_dir in potential_cache_dirs: try: if tf.io.gfile.exists(os.path.join(cache_dir, "COMPLETED")): self._cache_dir = cache_dir logging.info("'%s' is cached at %s.", self.name, self.cache_dir) break except tf.errors.PermissionDeniedError: logging.warning( "Permission denied for global cache folder: %s", cache_dir) if not self._cache_dir: logging.info( "'%s' does not exist in any task cache directories (searched %s).", self.name, potential_cache_dirs, ) return self._cache_dir @property def supports_caching(self) -> bool: """Whether or not this task supports offline caching.""" return self._cache_step_idx is not None @property def requires_caching(self) -> bool: """Whether or not this task requires offline caching.""" return (self._cache_step_idx is not None and self.preprocessors[self._cache_step_idx].required) def assert_cached(self) -> None: """Raises an assertion error if cached dataset does not exist.""" assert self.cache_dir, ( f"'{self.name}' does not exist in any of the task cache directories.") def get_cached_stats(self, split: str = tfds.Split.TRAIN ) -> Mapping[str, Union[int, float]]: """Returns basic statistics for cached dataset.""" self.assert_cached() if split not in self._stats: stats_path = utils.get_cached_stats_path(self.cache_dir, split) if not tf.io.gfile.exists(stats_path): raise ValueError( "Stats do not exist for '%s' split: %s" % (self.name, split)) with tf.io.gfile.GFile(stats_path) as f: self._stats[split] = json.load(f) return self._stats[split] def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, shuffle_buffer_size: Optional[int] = None, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = 1 ) -> tf.data.Dataset: """Returns a tf.data.Dataset from cache or generated on the fly. Args: sequence_length: dict mapping feature key to maximum int length for that feature. If longer after preprocessing, the feature will be truncated. May be set to None to avoid truncation. split: string, the split to return. use_cached: bool, whether to use the cached dataset instead of processing it on the fly. Defaults to False. shuffle: bool, whether to shuffle the dataset. Only used when generating on the fly (use_cached=False). shuffle_buffer_size: an integer or None to use task-specific buffer size. seed: tf.int64 scalar tf.Tensor (or None) for shuffling tf.data. shard_info: optional specification for loading a shard of the split. If the Task's DataSource contains at least the number of shards in the specification, it will be passed the shard info to avoid loading the full source dataset. Otherwise, the full source dataset will be loaded and sharded at the individual examples. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `1`. Returns: A tf.data.Dataset. """ if use_cached and not self.supports_caching: logging.warning( "Task '%s' does not support caching. Switching to on-the-fly " "preprocessing.", self.name) use_cached = False elif self.requires_caching and not use_cached: raise ValueError( f"Task '{self.name}' requires caching, but was called with " "`use_cached=False`.") if shard_info: # Whether we should shard at source or on the examples from the source. shard_data_source = ( len(self.source.list_shards(split=split)) >= shard_info.num_shards) logging.info("Sharding at the %s: %d of %d", "data source" if shard_data_source else "examples", shard_info.index, shard_info.num_shards) else: # No sharding. shard_data_source = False shard_info = ShardInfo(0, 1) if use_cached: source = self._get_cached_source(split) else: source = self.source if shard_data_source: ds = source.get_dataset( split=split, shuffle=shuffle, seed=seed, shard_info=shard_info) else: ds = source.get_dataset(split=split, shuffle=shuffle, seed=seed) ds = ds.shard(shard_info.num_shards, shard_info.index) if ((use_cached and self.get_cached_stats(split)["examples"] < _MAX_EXAMPLES_TO_MEM_CACHE) or (self.num_input_examples(split) and self.num_input_examples(split) < _MAX_EXAMPLES_TO_MEM_CACHE)): logging.info( "Automatically caching small dataset in memory: '%s:%s'", self.name, split) ds = ds.cache() if not use_cached: ds = self.preprocess_precache(ds, seed=seed) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) # We repeat before calling any (potentially) stochastic post-cache # preprocessing in order to take new samples each epoch. ds = ds.repeat(num_epochs) # Post cache processing. ds = self.preprocess_postcache( ds, sequence_length=sequence_length, seed=seed) ds = self._validate_preprocessing(ds) ds = self._trim_output_features(ds, sequence_length=sequence_length) if shuffle: if self._shuffle_buffer_size is None: raise ValueError( f"Shuffling is disallowed for Task '{self.name}' since its " "`shuffle_buffer_size` was set to `None` on construction.") shuffle_buffer_size = shuffle_buffer_size or self._shuffle_buffer_size # Shuffle before mixing since preprocessor can output multiple # (correlated) examples per input. ds = ds.shuffle(shuffle_buffer_size, seed=seed) return ds.prefetch(tf.data.experimental.AUTOTUNE) def _get_cached_source(self, split) -> _CachedDataSource: """Returns a DataSource to read cached files for split.""" self.assert_cached() return _CachedDataSource(self.cache_dir, split) def postprocess_fn(self, decoded_model_output: Any, **postprocess_kwargs) -> Any: """Returns the model output after applying the postprocess function.""" if self._postprocess_fn: return self._postprocess_fn(decoded_model_output, **postprocess_kwargs) return decoded_model_output class TaskRegistry(DatasetProviderRegistry): """Registry of Tasks.""" _REGISTRY = {} _PROVIDER_TYPE = Task @classmethod def add( cls, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[Callable[..., Mapping[str, float]]]] = None, **kwargs) -> Task: return super().add(name, Task, name, source, output_features, preprocessors, postprocess_fn, metric_fns, **kwargs) @classmethod def get(cls, name) -> Task: return super().get(name) # ================================ Mixtures ==================================== class Mixture(DatasetProviderBase): """Class for mixing multiple tasks.""" def __init__(self, name: str, tasks: Union[Sequence[str], Sequence[Tuple[str, Union[int, float, Callable[[Task], float]]]]], default_rate: Union[float, Callable[[Task], float]] = None): """Mixture constructor. A mixture specifies a set of tasks with associated mixing rates. Mixing happens on preprocessed tokenized examples. The mixing rates represent relative numbers of examples to use from their associated tasks. Setting the mixing rates to be equal to the numbers of examples in the tasks will result in each task going through an epoch in about the same amount of time - i.e. all examples are sampled equally across all tasks. Rates can be expressed either as absolute numbers or as functions that receive the Task as an argument. Args: name: string, a unique name for the Mixture. tasks: a list where each element is either a string (task name) or a pair whose first element is the task name and whose second element is either a float (rate) or a function from Task to float. default_rate: a float or a function from Task to float. This specifies the default rate if rates are not provided in the `tasks` argument. """ self._task_to_rate = {} self._tasks = [] self._sub_mixtures = [] self._name = name for t in tasks: if isinstance(t, str): task_name = t rate = default_rate if default_rate is None: raise ValueError("need a rate for each task") else: task_name, rate = t if task_name in TaskRegistry.names(): self._tasks.append(TaskRegistry.get(task_name)) self._task_to_rate[task_name] = rate else: self._sub_mixtures.append(MixtureRegistry.get(task_name)) # pytype:disable=name-error self._task_to_rate[task_name] = rate if len(set(tuple(t.output_features) for t in self.tasks)) != 1: raise ValueError( "All Tasks in a Mixture must have the same output features." ) @property def name(self) -> str: return self._name @property def tasks(self) -> Sequence[Task]: sub_tasks = (mix.tasks for mix in self._sub_mixtures) return list(sorted(set(sum(sub_tasks, self._tasks)), key=lambda t: t.name)) @property def total_rate(self) -> float: return sum(float(rate(TaskRegistry.get(name)) if callable(rate) else rate) for name, rate in self._task_to_rate.items()) def get_rate(self, task: Task) -> float: """Computes the mixing rate for the given task.""" value = 0.0 for mix in self._sub_mixtures: if task in mix.tasks: rate = self._task_to_rate[mix.name] value += rate * mix.get_rate(task) / mix.total_rate if task.name in self._task_to_rate: rate = self._task_to_rate[task.name] value += float(rate(task) if callable(rate) else rate) return value def num_input_examples(self, split: str) -> int: return sum(t.num_input_examples(split) for t in self.tasks) @property def splits(self) -> Sequence[str]: splits = set() for task in self.tasks: splits.update(task.splits) return tuple(splits) @property def output_features(self) -> Mapping[str, Feature]: # We require all tasks to have the same output_features in __init__ # so we can just get the output_features for the 0th task return self.tasks[0].output_features def _check_compatible_features(self) -> None: """Throw Exception if features across tasks have different vocabs or dtypes. """ for name, feature in self.tasks[0].output_features.items(): for task in self.tasks[1:]: if task.output_features[name].vocabulary != feature.vocabulary: raise ValueError( "Features across tasks in a mixture must use the same vocabulary." ) if task.output_features[name].dtype != feature.dtype: raise ValueError( "Features across tasks in a mixture must use the same dtype." ) def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = None, copy_pretokenized: bool = False, compute_stats_empirically: bool = False, ) -> tf.data.Dataset: """Returns the dataset of mixed tasks using the object-specified rates. Args: sequence_length: dict mapping feature key to maximum int length for that feature. If longer after preprocessing, the feature will be truncated. May be set to None to avoid truncation. split: string, the split to return for all tasks. use_cached: bool, whether to use the cached dataset instead of processing it on the fly. Defaults to False. shuffle: bool, whether to shuffle the dataset. Only used when generating on the fly (use_cached=False). seed: tf.int64 scalar tf.Tensor (or None) for shuffling tf.data. shard_info: optional specification for loading a shard of the split. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `None`. copy_pretokenized: bool, whether to pass through copies of pretokenized features a "_pretokenized" suffix added to the key. compute_stats_empirically: a boolean - does not work on TPU """ self._check_compatible_features() tasks = [] for task in self.tasks: if split not in task.splits: logging.warning( "Task %s has no '%s' split, skipping.", task.name, split ) continue tasks.append(task) if not tasks: raise ValueError("No datasets have a '{}' split".format(split)) output_feature_keys = set(self.output_features.keys()) if copy_pretokenized: output_feature_keys.update( {f + "_pretokenized" for f in output_feature_keys}) def filter_features(ex): return {k: v for k, v in ex.items() if k in output_feature_keys} datasets = [ task.get_dataset( # pylint:disable=g-complex-comprehension sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) .map(filter_features, num_parallel_calls=tf.data.experimental.AUTOTUNE) for task in tasks] rates = [self.get_rate(task) for task in tasks] # Sample from the dataset with the rates rates if seed is not None: sample_seed = seed elif shuffle: sample_seed = None else: sample_seed = 42 dataset = tf.data.experimental.sample_from_datasets( datasets, rates, sample_seed) if (split == "train" and use_cached and all(t.supports_caching for t in tasks)): _log_mixing_proportions(tasks, datasets, rates, dataset, sequence_length, compute_stats_empirically) return dataset def _log_padding_fractions(dataset, sequence_length, num_examples=100): """Empirically compute the fraction of padding - log the results. Args: dataset: a tf.data.Dataset sequence_length: dict from string to int (packed lengths) num_examples: an integer """ logging.info("computing padding fractions") keys = sequence_length.keys() padding_frac = {k: 0 for k in keys} for ex in tfds.as_numpy(dataset.take(num_examples)): for k in keys: padding_frac[k] += 1 - (sequence_length[k] / len(ex[k])) for k in keys: logging.info("%s padding fraction = %g", k, padding_frac[k]) def _log_mixing_proportions( tasks, datasets, rates, mixed_dataset, sequence_length, compute_stats_empirically): """Log information about the mixing proportions. Called from Mixture.get_dataset. Args: tasks: a list of Task datasets: a list of tf.data.Dataset rates: a list of floats mixed_dataset: a tf.data.Dataset sequence_length: dict from string to int (packed lengths) compute_stats_empirically: a boolean - does not work on TPU """ def _normalize(l): denom = sum(l) if not denom: return l return [x / denom for x in l] # compute some stats about the mixture examples_fraction = _normalize(rates) if compute_stats_empirically: stats_examples = 100 mean_inputs_length = [] mean_targets_length = [] for dataset in datasets: inputs_sum = 0 targets_sum = 0 for ex in tfds.as_numpy(dataset.take(stats_examples)): # Some tasks, like LMs, don't have inputs. if "inputs" in ex: inputs_sum += ex["inputs"].size targets_sum += ex["targets"].size mean_inputs_length.append(inputs_sum / float(stats_examples)) mean_targets_length.append(targets_sum / float(stats_examples)) else: def _estimated_mean_length(task, key): if key not in sequence_length: return 0 if (task.supports_caching and task._cache_step_idx < len(task._preprocessors) - 1): # pylint:disable=protected-access # There is processing after caching, so we can't rely on the stats. return sequence_length[key] # Some tasks, like LMs, don't have inputs. if key + "_tokens" in task.get_cached_stats("train"): return min(sequence_length[key], (task.get_cached_stats("train")[key + "_tokens"] / task.get_cached_stats("train")["examples"])) else: return 0 mean_inputs_length = [_estimated_mean_length(task, "inputs") for task in tasks] mean_targets_length = [_estimated_mean_length(task, "targets") for task in tasks] inputs_fraction = _normalize( [l * r for l, r in zip(mean_inputs_length, rates)]) targets_fraction = _normalize( [l * r for l, r in zip(mean_targets_length, rates)]) logging.info("%12s %12s %12s %12s %12s %12s %s", "rate", "ex.frac.", "inp.frac.", "tgt.frac.", "inp.len.", "tgt.len", "task") for i in range(len(rates)): logging.info("%12g %12g %12g %12g %12g %12g %s", rates[i], examples_fraction[i], inputs_fraction[i], targets_fraction[i], mean_inputs_length[i], mean_targets_length[i], tasks[i].name) if compute_stats_empirically: _log_padding_fractions(mixed_dataset, sequence_length) class MixtureRegistry(DatasetProviderRegistry): """Registry of Mixtures.""" _REGISTRY = {} _PROVIDER_TYPE = Mixture @classmethod def add(cls, name, tasks, default_rate=None) -> Mixture: return super().add(name, Mixture, name, tasks, default_rate) @classmethod def get(cls, name) -> Mixture: return super().get(name) def get_mixture_or_task(task_or_mixture_name): """Return the Task or Mixture from the appropriate registry.""" mixtures = MixtureRegistry.names() tasks = TaskRegistry.names() if task_or_mixture_name in mixtures: if task_or_mixture_name in tasks: logging.warning("%s is both a Task and a Mixture, returning Mixture", task_or_mixture_name) return MixtureRegistry.get(task_or_mixture_name) if task_or_mixture_name in tasks: return TaskRegistry.get(task_or_mixture_name) else: raise ValueError("No Task or Mixture found with name: %s" % task_or_mixture_name) def get_subtasks(task_or_mixture): """Returns all the Tasks in a Mixture as a list or the Task itself.""" if isinstance(task_or_mixture, Task): return [task_or_mixture] else: return task_or_mixture.tasks def get_dataset( mixture_or_task_name: str, task_feature_lengths: Mapping[str, int], feature_converter: FeatureConverter, dataset_split: str = "train", use_cached: bool = False, shuffle: bool = False, num_epochs: Optional[int] = 1, shard_info: ShardInfo = None, verbose: bool = True, seed: Optional[int] = None ) -> tf.data.Dataset: """Get processed dataset with the model features. In order to use options specific to a feature converter, e.g., packing, `feature_converter` instance should be instantiated with those options before being pased to this function. Getting sharded datasets is supported. To use this feature, pass in `shard_info`, with shard_index and num_shards information. Sharding is done before the feature converter stage. Therefore, if packing is used it will be done on the sharded dataset. Args: mixture_or_task_name: mixture or task name for the Task API. task_feature_lengths: dict mapping task feature key to its sequence length. This specifies the sequence length of the dataset from the Task API. feature_converter: a feature converter object to use to convert the task features to model features. Must be a subclass of FeatureConverter. dataset_split: the split to use. use_cached: whether to use the cached dataset instead of processing it on the fly. shuffle: whether to shuffle the dataset. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `1`. shard_info: number of shards and shard index information. verbose: if true, log the feature shapes. seed: a random seed to for shuffling tf.data. Returns: ds: the processed dataset. """ if not isinstance(feature_converter, FeatureConverter): raise TypeError( "feature_converter should be an instance of FeatureConverter.") mixture_or_task = get_mixture_or_task(mixture_or_task_name) ds = mixture_or_task.get_dataset( task_feature_lengths, split=dataset_split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) ds = feature_converter(ds, task_feature_lengths=task_feature_lengths) if verbose: logging.info( "The output dataset from seqio.get_dataset has the following features") for feature_name, tensor_spec in ds.element_spec.items(): logging.info("feature: %s \t shape: %s \t dtype: %s", feature_name, tensor_spec.shape.as_list(), tensor_spec.dtype.name) return ds

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import abc import collections import inspect import json import os import re from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Sequence, Tuple, Type, Union from absl import logging import dataclasses import numpy as np from packaging import version from t5.seqio import utils from t5.seqio.feature_converters import FeatureConverter from t5.seqio.vocabularies import Vocabulary import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds import typing_extensions _DEFAULT_FEATURE_KEYS = ["inputs", "targets"] _VALID_TASK_NAME_REGEX = re.compile(r"^[\w\d\._]+$") _MAX_EXAMPLES_TO_MEM_CACHE = 10000 SHUFFLE_BUFFER_SIZE = 1000 @dataclasses.dataclass(frozen=True) class Feature: vocabulary: Vocabulary add_eos: bool = True required: bool = True dtype: tf.DType = tf.int32 @dataclasses.dataclass(frozen=True) class ShardInfo: index: int num_shards: int class DatasetProviderBase(metaclass=abc.ABCMeta): @abc.abstractproperty def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractproperty def splits(self) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, sequence_length: int, split: str, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: int = 1 ) -> tf.data.Dataset: raise NotImplementedError @abc.abstractmethod def num_input_examples(self, split: str) -> int: raise NotImplementedError class DatasetProviderRegistry(object): _REGISTRY: MutableMapping[str, DatasetProviderBase] _PROVIDER_TYPE: Type[DatasetProviderBase] @classmethod def add_provider(cls, name: str, provider): if name in cls._REGISTRY: raise ValueError("Attempting to register duplicate provider: %s" % name) if not isinstance(provider, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, type(provider).__name__)) cls._REGISTRY[name] = provider @classmethod def add( cls, name: str, provider_cls, *provider_args, **provider_kwargs ): if not issubclass(provider_cls, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, provider_cls)) provider = provider_cls(*provider_args, **provider_kwargs) cls.add_provider(name, provider) return provider @classmethod def remove(cls, name): if name in cls._REGISTRY: del cls._REGISTRY[name] @classmethod def get(cls, name): if name not in cls._REGISTRY: raise ValueError("Provider name not registered: %s" % name) return cls._REGISTRY[name] @classmethod def names(cls): return cls._REGISTRY.keys() @classmethod def reset(cls): cls._REGISTRY = {} @classmethod def get_dataset( cls, name, sequence_length, split, use_cached=False, shuffle=True, seed=None, shard_info=None, num_epochs=1): return cls.get(name).get_dataset( sequence_length=sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) class DataSource(DatasetProviderBase): def __init__( self, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None): self._splits = tuple(splits) self._num_input_examples = ( dict(num_input_examples) if num_input_examples is not None else None) @property def splits(self) -> Sequence[str]: return self._splits @property def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractmethod def list_shards(self, split: str) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: raise NotImplementedError def num_input_examples(self, split: str) -> Optional[int]: if self._num_input_examples is None: return None return self._num_input_examples[split] def _validate_args(fn, expected_pos_args): argspec = inspect.getfullargspec(fn) expected_pos_args = tuple(expected_pos_args) actual_args = tuple(argspec.args) if actual_args[:len(expected_pos_args)] != expected_pos_args: raise ValueError( "'%s' must have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_args)) actual_pos_args = tuple( argspec.args[:-len(argspec.defaults)] if argspec.defaults else argspec.args) if actual_pos_args != expected_pos_args[:len(actual_pos_args)]: raise ValueError( "'%s' may only have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_pos_args)) class DatasetFnCallable(typing_extensions.Protocol): def __call__(self, split: str, shuffle_files: bool, seed: Optional[int] = None) -> tf.data.Dataset: ... class FunctionDataSource(DataSource): def __init__( self, dataset_fn: DatasetFnCallable, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None ): _validate_args(dataset_fn, ["split", "shuffle_files"]) self._dataset_fn = dataset_fn super().__init__(splits=splits, num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: if shard_info and shard_info.num_shards > 1: raise ValueError( "`FunctionDataSource` does not support low-level sharding. Use " "tf.data.Dataset.shard instead.") if seed is None: ds = self._dataset_fn(split=split, shuffle_files=shuffle) else: _validate_args(self._dataset_fn, ["split", "shuffle_files", "seed"]) ds = self._dataset_fn(split=split, shuffle_files=shuffle, seed=seed) return ds def list_shards(self, split: str) -> Sequence[str]: return [split] class TfdsDataSource(DataSource): def __init__( self, tfds_name: str, tfds_data_dir: Optional[str] = None, splits: Optional[Union[Iterable[str], Mapping[str, str]]] = None ): if ":" not in tfds_name: raise ValueError("TFDS name must contain a version number, got: %s" % tfds_name) self._tfds_dataset = utils.LazyTfdsLoader( tfds_name, data_dir=tfds_data_dir, split_map=splits if isinstance(splits, dict) else None) super().__init__(splits=splits or ()) @property def splits(self): return self._splits or self._tfds_dataset.info.splits @property def tfds_dataset(self): return self._tfds_dataset def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: return self.tfds_dataset.load( split, shuffle_files=shuffle, seed=seed, shard_info=shard_info) def num_input_examples(self, split: str) -> int: return self.tfds_dataset.size(split) def list_shards(self, split: str) -> Sequence[str]: return self.tfds_dataset.files(split) class FileDataSource(DataSource): def __init__( self, read_file_fn: Callable[[tf.data.Dataset], tf.data.Dataset], split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], num_input_examples: Optional[Mapping[str, int]] = None, ): self._split_to_filepattern = split_to_filepattern self._reader = read_file_fn super().__init__( splits=split_to_filepattern.keys(), num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: files = self.list_shards(split) if not files: raise ValueError( "No file is found for the file pattern: " f"{self._split_to_filepattern[split]}." ) files_ds = tf.data.Dataset.from_tensor_slices(np.array(files, dtype=np.str)) if shard_info: if len(files) < shard_info.num_shards: raise ValueError( f"Dataset has too few files to shard. {len(files)} files vs " f"{shard_info.num_shards} shards requested.") files_ds = files_ds.shard(shard_info.num_shards, shard_info.index) if shuffle: files_ds = files_ds.shuffle(buffer_size=16, seed=seed) return files_ds.interleave( self._reader, cycle_length=16, block_length=16, num_parallel_calls=tf.data.experimental.AUTOTUNE) def list_shards(self, split: str) -> Sequence[str]: return tf.io.gfile.glob(self._split_to_filepattern[split]) class TextLineDataSource(FileDataSource): def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], skip_header_lines: int = 0, num_input_examples: Optional[Mapping[str, int]] = None, ): self._skip_header_lines = skip_header_lines def read_file_fn(filepattern): return tf.data.TextLineDataset(filepattern).skip(skip_header_lines) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) class TFExampleDataSource(FileDataSource): def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], feature_description: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature]], reader_cls: Type[tf.data.Dataset] = tf.data.TFRecordDataset, num_input_examples: Optional[Mapping[str, int]] = None, ): def read_file_fn(filepattern): return reader_cls(filepattern).map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) def _rename_plaintext_to_pretokenized( dataset: tf.data.Dataset) -> tf.data.Dataset: def _rename(inputs): outputs = {} for k, v in inputs.items(): if k.endswith("_plaintext"): k = k[:-len("plaintext")] + "pretokenized" outputs[k] = v return outputs return dataset.map( _rename, num_parallel_calls=tf.data.experimental.AUTOTUNE) class _CachedDataSource(FileDataSource): def __init__(self, cache_dir: str, split: str): with tf.io.gfile.GFile(utils.get_cached_info_path(cache_dir, split)) as f: split_info = json.load(f) features = split_info["features"] with tf.io.gfile.GFile(utils.get_cached_stats_path(cache_dir, split)) as f: stats = json.load(f) version_when_cached = version.Version( split_info.get("seqio_version", "0.pre")) version_with_true_dtypes = version.Version("0.0.0") if version_when_cached < version_with_true_dtypes: for name, feat in features.items(): if feat["dtype"] == "int64": logging.info("Casting cached '%s' to int32.", name) feat["dtype"] = "int32" def _feature_config(shape, dtype): if dtype in ("int32", "bool"): dtype = "int64" if shape and shape[0] is None: return tf.io.FixedLenSequenceFeature( shape[1:], dtype, allow_missing=True) return tf.io.FixedLenFeature(shape, dtype) feature_description = { feat: _feature_config(**desc) for feat, desc in features.items() } def read_file_fn(filepattern): ds = tf.data.TFRecordDataset(filepattern) ds = ds.map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) ds = ds.map( lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds = _rename_plaintext_to_pretokenized(ds) return ds split_to_filepattern = { split: "%s-*-of-*%d" % ( utils.get_cached_tfrecord_prefix(cache_dir, split), split_info["num_shards"]) } super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples={split: stats["examples"]} ) class CacheDatasetPlaceholder(object): def __init__(self, required=False): self._required = required @property def required(self): return self._required def __call__(self, dataset): raise RuntimeError("`CacheDatasetPlaceholder` should never be called.") MetricFnCallable = Callable[..., Mapping[str, float]] class Task(DatasetProviderBase): def __init__( self, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[MetricFnCallable]] = None, shuffle_buffer_size: Optional[int] = SHUFFLE_BUFFER_SIZE): if not _VALID_TASK_NAME_REGEX.match(name): raise ValueError( "Task name '%s' contains invalid characters. Must match regex: %s" % ( name, _VALID_TASK_NAME_REGEX.pattern)) metric_fns = metric_fns or [] self._predict_metric_fns = [] self._score_metric_fns = [] for metric_fn in metric_fns: pos_args = tuple( key for key, param in inspect.signature(metric_fn).parameters.items() if param.default == inspect.Parameter.empty ) if pos_args == ("targets", "scores"): self._score_metric_fns.append(metric_fn) elif pos_args == ("targets", "predictions"): self._predict_metric_fns.append(metric_fn) else: raise ValueError( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). " f"Got: {pos_args}") self._name = name self._source = source preprocessors = tuple(preprocessors or []) cache_step_idxs = [ i for i, p in enumerate(preprocessors) if isinstance(p, CacheDatasetPlaceholder) ] if len(cache_step_idxs) > 1: raise ValueError( "`CacheDatasetPlaceholder` can appear at most once in the " f"preprocessing pipeline. Found {len(cache_step_idxs)} in '{name}'.") cache_step_idx = cache_step_idxs[0] if cache_step_idxs else None if cache_step_idx is not None: for prep in preprocessors[:cache_step_idx]: prep_args = inspect.signature(prep).parameters.keys() if "sequence_length" in prep_args: raise ValueError( f"'{prep.__name__}' has a `sequence_length` argument but occurs " f"before `CacheDatasetPlaceholder` in '{name}'. This is not " "allowed since the sequence length is specified at run time.") if "seed" in prep_args or "seeds" in prep_args: raise logging.warning( f"'{prep.__name__}' has a `seed(s)` argument but occurs before " f"`CacheDatasetPlaceholder` in '{name}'. This is not recommended " "since the same samples will be used each epoch when reading " "from the cache.") self._cache_step_idx = cache_step_idx self._preprocessors = preprocessors self._metric_fns = tuple(metric_fns) self._postprocess_fn = postprocess_fn self._cache_dir = None self._stats = {} self._shuffle_buffer_size = shuffle_buffer_size self._output_features = collections.OrderedDict( sorted(list(output_features.items())) ) @property def name(self) -> str: return self._name @property def metric_fns(self) -> Sequence[MetricFnCallable]: return self._predict_metric_fns + self._score_metric_fns @property def score_metric_fns(self) -> Sequence[MetricFnCallable]: return self._score_metric_fns @property def predict_metric_fns(self) -> Sequence[MetricFnCallable]: return self._predict_metric_fns @property def output_features(self) -> Mapping[str, Feature]: return self._output_features @property def splits(self) -> Sequence[str]: s = self.source.splits if not s: raise ValueError(f"Task {self.name} has no splits") return s @property def source(self) -> DataSource: return self._source @property def preprocessors(self) -> Sequence[Callable[..., tf.data.Dataset]]: return self._preprocessors def num_input_examples(self, split: str) -> Optional[int]: return self.source.num_input_examples(split) def _preprocess_dataset( self, dataset: tf.data.Dataset, preprocessors: Sequence[Callable[..., tf.data.Dataset]], sequence_length: Optional[Mapping[str, int]] = None) -> tf.data.Dataset: for prep_fn in preprocessors: fn_args = set(inspect.signature(prep_fn).parameters.keys()) kwargs = {} if "sequence_length" in fn_args: kwargs["sequence_length"] = sequence_length if "output_features" in fn_args: kwargs["output_features"] = self.output_features dataset = prep_fn(dataset, **kwargs) return dataset def _validate_preprocessing( self, dataset: tf.data.Dataset ) -> tf.data.Dataset: actual_specs = dataset.element_spec for feat, feat_spec in self.output_features.items(): if feat not in actual_specs: if feat_spec.required: raise ValueError( "Task dataset is missing expected output feature after " f"preprocessing: {feat}") else: continue actual_spec = actual_specs[feat] if feat_spec.dtype != actual_spec.dtype: raise ValueError( f"Task dataset has incorrect type for feature '{feat}' after " f"preprocessing: Got {actual_spec.dtype.name}, expected " f"{feat_spec.dtype.name}") if actual_spec.shape.rank != 1: raise ValueError( f"Task dataset has incorrect rank for feature '{feat}' after " f"preprocessing: Got {actual_spec.shape.rank}, expected 1") return dataset def _trim_output_features( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]] ) -> tf.data.Dataset: def _trim(k: str, v: tf.Tensor) -> tf.Tensor: if k not in self.output_features or not sequence_length: return v return v[:sequence_length[k]] return dataset.map( lambda ex: {k: _trim(k, v) for k, v in ex.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) def preprocess_precache( self, dataset: tf.data.Dataset, seed: Optional[int] = None ) -> tf.data.Dataset: if not self.supports_caching: return dataset with utils.map_seed_manager(seed): return self._preprocess_dataset( dataset, self._preprocessors[:self._cache_step_idx], ) def preprocess_postcache( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]], seed: Optional[int] = None ) -> tf.data.Dataset: start_idx = 0 if self.supports_caching: # Skip a sufficient number of seeds to avoid duplicating any from # pre-cache preprocessing. seed = None if seed is None else seed + 42 * self._cache_step_idx start_idx = self._cache_step_idx + 1 with utils.map_seed_manager(seed): dataset = self._preprocess_dataset( dataset, self._preprocessors[start_idx:], sequence_length=sequence_length, ) return dataset @property def cache_dir(self) -> Optional[str]: if not self._cache_dir: # See if cached data exists in any of the cache directories. potential_cache_dirs = [ os.path.join(d, self.name) for d in utils.get_global_cache_dirs()] for cache_dir in potential_cache_dirs: try: if tf.io.gfile.exists(os.path.join(cache_dir, "COMPLETED")): self._cache_dir = cache_dir logging.info("'%s' is cached at %s.", self.name, self.cache_dir) break except tf.errors.PermissionDeniedError: logging.warning( "Permission denied for global cache folder: %s", cache_dir) if not self._cache_dir: logging.info( "'%s' does not exist in any task cache directories (searched %s).", self.name, potential_cache_dirs, ) return self._cache_dir @property def supports_caching(self) -> bool: return self._cache_step_idx is not None @property def requires_caching(self) -> bool: return (self._cache_step_idx is not None and self.preprocessors[self._cache_step_idx].required) def assert_cached(self) -> None: assert self.cache_dir, ( f"'{self.name}' does not exist in any of the task cache directories.") def get_cached_stats(self, split: str = tfds.Split.TRAIN ) -> Mapping[str, Union[int, float]]: self.assert_cached() if split not in self._stats: stats_path = utils.get_cached_stats_path(self.cache_dir, split) if not tf.io.gfile.exists(stats_path): raise ValueError( "Stats do not exist for '%s' split: %s" % (self.name, split)) with tf.io.gfile.GFile(stats_path) as f: self._stats[split] = json.load(f) return self._stats[split] def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, shuffle_buffer_size: Optional[int] = None, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = 1 ) -> tf.data.Dataset: if use_cached and not self.supports_caching: logging.warning( "Task '%s' does not support caching. Switching to on-the-fly " "preprocessing.", self.name) use_cached = False elif self.requires_caching and not use_cached: raise ValueError( f"Task '{self.name}' requires caching, but was called with " "`use_cached=False`.") if shard_info: # Whether we should shard at source or on the examples from the source. shard_data_source = ( len(self.source.list_shards(split=split)) >= shard_info.num_shards) logging.info("Sharding at the %s: %d of %d", "data source" if shard_data_source else "examples", shard_info.index, shard_info.num_shards) else: # No sharding. shard_data_source = False shard_info = ShardInfo(0, 1) if use_cached: source = self._get_cached_source(split) else: source = self.source if shard_data_source: ds = source.get_dataset( split=split, shuffle=shuffle, seed=seed, shard_info=shard_info) else: ds = source.get_dataset(split=split, shuffle=shuffle, seed=seed) ds = ds.shard(shard_info.num_shards, shard_info.index) if ((use_cached and self.get_cached_stats(split)["examples"] < _MAX_EXAMPLES_TO_MEM_CACHE) or (self.num_input_examples(split) and self.num_input_examples(split) < _MAX_EXAMPLES_TO_MEM_CACHE)): logging.info( "Automatically caching small dataset in memory: '%s:%s'", self.name, split) ds = ds.cache() if not use_cached: ds = self.preprocess_precache(ds, seed=seed) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) # We repeat before calling any (potentially) stochastic post-cache # preprocessing in order to take new samples each epoch. ds = ds.repeat(num_epochs) # Post cache processing. ds = self.preprocess_postcache( ds, sequence_length=sequence_length, seed=seed) ds = self._validate_preprocessing(ds) ds = self._trim_output_features(ds, sequence_length=sequence_length) if shuffle: if self._shuffle_buffer_size is None: raise ValueError( f"Shuffling is disallowed for Task '{self.name}' since its " "`shuffle_buffer_size` was set to `None` on construction.") shuffle_buffer_size = shuffle_buffer_size or self._shuffle_buffer_size # Shuffle before mixing since preprocessor can output multiple # (correlated) examples per input. ds = ds.shuffle(shuffle_buffer_size, seed=seed) return ds.prefetch(tf.data.experimental.AUTOTUNE) def _get_cached_source(self, split) -> _CachedDataSource: self.assert_cached() return _CachedDataSource(self.cache_dir, split) def postprocess_fn(self, decoded_model_output: Any, **postprocess_kwargs) -> Any: if self._postprocess_fn: return self._postprocess_fn(decoded_model_output, **postprocess_kwargs) return decoded_model_output class TaskRegistry(DatasetProviderRegistry): _REGISTRY = {} _PROVIDER_TYPE = Task @classmethod def add( cls, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[Callable[..., Mapping[str, float]]]] = None, **kwargs) -> Task: return super().add(name, Task, name, source, output_features, preprocessors, postprocess_fn, metric_fns, **kwargs) @classmethod def get(cls, name) -> Task: return super().get(name) # ================================ Mixtures ==================================== class Mixture(DatasetProviderBase): def __init__(self, name: str, tasks: Union[Sequence[str], Sequence[Tuple[str, Union[int, float, Callable[[Task], float]]]]], default_rate: Union[float, Callable[[Task], float]] = None): self._task_to_rate = {} self._tasks = [] self._sub_mixtures = [] self._name = name for t in tasks: if isinstance(t, str): task_name = t rate = default_rate if default_rate is None: raise ValueError("need a rate for each task") else: task_name, rate = t if task_name in TaskRegistry.names(): self._tasks.append(TaskRegistry.get(task_name)) self._task_to_rate[task_name] = rate else: self._sub_mixtures.append(MixtureRegistry.get(task_name)) # pytype:disable=name-error self._task_to_rate[task_name] = rate if len(set(tuple(t.output_features) for t in self.tasks)) != 1: raise ValueError( "All Tasks in a Mixture must have the same output features." ) @property def name(self) -> str: return self._name @property def tasks(self) -> Sequence[Task]: sub_tasks = (mix.tasks for mix in self._sub_mixtures) return list(sorted(set(sum(sub_tasks, self._tasks)), key=lambda t: t.name)) @property def total_rate(self) -> float: return sum(float(rate(TaskRegistry.get(name)) if callable(rate) else rate) for name, rate in self._task_to_rate.items()) def get_rate(self, task: Task) -> float: value = 0.0 for mix in self._sub_mixtures: if task in mix.tasks: rate = self._task_to_rate[mix.name] value += rate * mix.get_rate(task) / mix.total_rate if task.name in self._task_to_rate: rate = self._task_to_rate[task.name] value += float(rate(task) if callable(rate) else rate) return value def num_input_examples(self, split: str) -> int: return sum(t.num_input_examples(split) for t in self.tasks) @property def splits(self) -> Sequence[str]: splits = set() for task in self.tasks: splits.update(task.splits) return tuple(splits) @property def output_features(self) -> Mapping[str, Feature]: # We require all tasks to have the same output_features in __init__ # so we can just get the output_features for the 0th task return self.tasks[0].output_features def _check_compatible_features(self) -> None: for name, feature in self.tasks[0].output_features.items(): for task in self.tasks[1:]: if task.output_features[name].vocabulary != feature.vocabulary: raise ValueError( "Features across tasks in a mixture must use the same vocabulary." ) if task.output_features[name].dtype != feature.dtype: raise ValueError( "Features across tasks in a mixture must use the same dtype." ) def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = None, copy_pretokenized: bool = False, compute_stats_empirically: bool = False, ) -> tf.data.Dataset: self._check_compatible_features() tasks = [] for task in self.tasks: if split not in task.splits: logging.warning( "Task %s has no '%s' split, skipping.", task.name, split ) continue tasks.append(task) if not tasks: raise ValueError("No datasets have a '{}' split".format(split)) output_feature_keys = set(self.output_features.keys()) if copy_pretokenized: output_feature_keys.update( {f + "_pretokenized" for f in output_feature_keys}) def filter_features(ex): return {k: v for k, v in ex.items() if k in output_feature_keys} datasets = [ task.get_dataset( # pylint:disable=g-complex-comprehension sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) .map(filter_features, num_parallel_calls=tf.data.experimental.AUTOTUNE) for task in tasks] rates = [self.get_rate(task) for task in tasks] # Sample from the dataset with the rates rates if seed is not None: sample_seed = seed elif shuffle: sample_seed = None else: sample_seed = 42 dataset = tf.data.experimental.sample_from_datasets( datasets, rates, sample_seed) if (split == "train" and use_cached and all(t.supports_caching for t in tasks)): _log_mixing_proportions(tasks, datasets, rates, dataset, sequence_length, compute_stats_empirically) return dataset def _log_padding_fractions(dataset, sequence_length, num_examples=100): logging.info("computing padding fractions") keys = sequence_length.keys() padding_frac = {k: 0 for k in keys} for ex in tfds.as_numpy(dataset.take(num_examples)): for k in keys: padding_frac[k] += 1 - (sequence_length[k] / len(ex[k])) for k in keys: logging.info("%s padding fraction = %g", k, padding_frac[k]) def _log_mixing_proportions( tasks, datasets, rates, mixed_dataset, sequence_length, compute_stats_empirically): def _normalize(l): denom = sum(l) if not denom: return l return [x / denom for x in l] # compute some stats about the mixture examples_fraction = _normalize(rates) if compute_stats_empirically: stats_examples = 100 mean_inputs_length = [] mean_targets_length = [] for dataset in datasets: inputs_sum = 0 targets_sum = 0 for ex in tfds.as_numpy(dataset.take(stats_examples)): # Some tasks, like LMs, don't have inputs. if "inputs" in ex: inputs_sum += ex["inputs"].size targets_sum += ex["targets"].size mean_inputs_length.append(inputs_sum / float(stats_examples)) mean_targets_length.append(targets_sum / float(stats_examples)) else: def _estimated_mean_length(task, key): if key not in sequence_length: return 0 if (task.supports_caching and task._cache_step_idx < len(task._preprocessors) - 1): return sequence_length[key] # Some tasks, like LMs, don't have inputs. if key + "_tokens" in task.get_cached_stats("train"): return min(sequence_length[key], (task.get_cached_stats("train")[key + "_tokens"] / task.get_cached_stats("train")["examples"])) else: return 0 mean_inputs_length = [_estimated_mean_length(task, "inputs") for task in tasks] mean_targets_length = [_estimated_mean_length(task, "targets") for task in tasks] inputs_fraction = _normalize( [l * r for l, r in zip(mean_inputs_length, rates)]) targets_fraction = _normalize( [l * r for l, r in zip(mean_targets_length, rates)]) logging.info("%12s %12s %12s %12s %12s %12s %s", "rate", "ex.frac.", "inp.frac.", "tgt.frac.", "inp.len.", "tgt.len", "task") for i in range(len(rates)): logging.info("%12g %12g %12g %12g %12g %12g %s", rates[i], examples_fraction[i], inputs_fraction[i], targets_fraction[i], mean_inputs_length[i], mean_targets_length[i], tasks[i].name) if compute_stats_empirically: _log_padding_fractions(mixed_dataset, sequence_length) class MixtureRegistry(DatasetProviderRegistry): _REGISTRY = {} _PROVIDER_TYPE = Mixture @classmethod def add(cls, name, tasks, default_rate=None) -> Mixture: return super().add(name, Mixture, name, tasks, default_rate) @classmethod def get(cls, name) -> Mixture: return super().get(name) def get_mixture_or_task(task_or_mixture_name): mixtures = MixtureRegistry.names() tasks = TaskRegistry.names() if task_or_mixture_name in mixtures: if task_or_mixture_name in tasks: logging.warning("%s is both a Task and a Mixture, returning Mixture", task_or_mixture_name) return MixtureRegistry.get(task_or_mixture_name) if task_or_mixture_name in tasks: return TaskRegistry.get(task_or_mixture_name) else: raise ValueError("No Task or Mixture found with name: %s" % task_or_mixture_name) def get_subtasks(task_or_mixture): if isinstance(task_or_mixture, Task): return [task_or_mixture] else: return task_or_mixture.tasks def get_dataset( mixture_or_task_name: str, task_feature_lengths: Mapping[str, int], feature_converter: FeatureConverter, dataset_split: str = "train", use_cached: bool = False, shuffle: bool = False, num_epochs: Optional[int] = 1, shard_info: ShardInfo = None, verbose: bool = True, seed: Optional[int] = None ) -> tf.data.Dataset: if not isinstance(feature_converter, FeatureConverter): raise TypeError( "feature_converter should be an instance of FeatureConverter.") mixture_or_task = get_mixture_or_task(mixture_or_task_name) ds = mixture_or_task.get_dataset( task_feature_lengths, split=dataset_split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) ds = feature_converter(ds, task_feature_lengths=task_feature_lengths) if verbose: logging.info( "The output dataset from seqio.get_dataset has the following features") for feature_name, tensor_spec in ds.element_spec.items(): logging.info("feature: %s \t shape: %s \t dtype: %s", feature_name, tensor_spec.shape.as_list(), tensor_spec.dtype.name) return ds

true

f71fdf38821c8803ae681fd71c4c71d7da8b1c90

2,037

py

Python

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

8,092

2016-04-27T20:32:29.000Z

2019-01-05T07:39:33.000Z

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

2,961

2016-05-05T07:16:16.000Z

2019-01-05T08:47:59.000Z

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

3,546

2016-05-04T20:33:16.000Z

2019-01-05T05:14:26.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks`` Revision ID: 30867afad44a Revises: e9304a3141f0 Create Date: 2021-06-04 22:11:19.849981 """ import sqlalchemy as sa from alembic import op # revision identifiers, used by Alembic. revision = '30867afad44a' down_revision = 'e9304a3141f0' branch_labels = None depends_on = None airflow_version = '2.2.0' def upgrade(): """Apply Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks``""" conn = op.get_bind() is_sqlite = bool(conn.dialect.name == "sqlite") if is_sqlite: op.execute("PRAGMA foreign_keys=off") with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'concurrency', new_column_name='max_active_tasks', type_=sa.Integer(), nullable=False, ) if is_sqlite: op.execute("PRAGMA foreign_keys=on") def downgrade(): """Unapply Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks``""" with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'max_active_tasks', new_column_name='concurrency', type_=sa.Integer(), nullable=False, )

31.338462

88

0.691703

import sqlalchemy as sa from alembic import op revision = '30867afad44a' down_revision = 'e9304a3141f0' branch_labels = None depends_on = None airflow_version = '2.2.0' def upgrade(): conn = op.get_bind() is_sqlite = bool(conn.dialect.name == "sqlite") if is_sqlite: op.execute("PRAGMA foreign_keys=off") with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'concurrency', new_column_name='max_active_tasks', type_=sa.Integer(), nullable=False, ) if is_sqlite: op.execute("PRAGMA foreign_keys=on") def downgrade(): with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'max_active_tasks', new_column_name='concurrency', type_=sa.Integer(), nullable=False, )

true

f71fdf687002e4d434788ab435b395447a70728b

228

py

Python

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

1

2021-07-27T08:22:29.000Z

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

null

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

null

class Node: """Class for storing linked list node.""" def __init__(self, element, next_pointer, prev_pointer=None): self._element = element self._next = next_pointer self._prev = prev_pointer

32.571429

65

0.653509

class Node: def __init__(self, element, next_pointer, prev_pointer=None): self._element = element self._next = next_pointer self._prev = prev_pointer

true

f71fdf8b98012c19be34962342155bc04ff638eb

25,034

py

Python

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

1

2022-02-25T14:32:34.000Z

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

null

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

2

2020-09-02T17:18:33.000Z

2022-02-25T14:32:48.000Z

import logging import random from math import sqrt, log from stellar_system import Star from stellar_system import Planetesimal from stellar_system import Protoplanet from stellar_system import Protomoon from stellar_system import Planet from stellar_system import Orbit from accrete import CircumstellarDisk from constants import ECCENTRICITY_COEFF, PROTOPLANET_MASS from constants import SUN_MASS_IN_EARTH_MASSES from constants import EARTH_ALBEDO, GAS_GIANT_ALBEDO, FREEZING_POINT_OF_WATER, KM_PER_AU, EARTH_AVERAGE_KELVIN, EARTH_EXOSPHERE_TEMP from constants import MOL_NITROGEN, MOL_HYDROGEN, HELIUM from constants import ASTEROID_MASS_LIMIT from constants import MILLIBARS_PER_BAR from enviroment import kothari_radius, gas_life, rms_vel, est_temp, period, day_length, acceleration, gravity, min_molec_weight, orb_zone, volume_radius, volume_density, grnhouse, boiling_point, escape_vel, empirical_density, inclination, iterate_surface_temp, pressure, vol_inventory from enviroment import PlanetType from math import exp from math import inf as INCREDIBLY_LARGE_NUMBER # TODO(woursler): Just use inf from util import about, random_number, random_eccentricity from chemtable import gases logging.getLogger().setLevel(logging.INFO) def random_star(): # TODO: Add seed? # Sources # exoplanets.co/exoplanet-correlations/host-star-mass-distribution.html # en.wikipedia.org/wiki/Main_sequence#mediaviewer/File:HRDiagram.png # TODO: Code up generation. age = random.randrange(1*10**9, 6*10**9) return Star(age=age, mass_ratio=1) def generate_stellar_system(star, do_gases=True, do_moons=True): protoplanets = generate_planetary_masses( star, 0.0, star.stellar_dust_limit, do_moons=do_moons ) star.planets = [ generate_planet( p, star, do_gases=do_gases, do_moons=do_moons ) for p in protoplanets ] return star # Create protoplanets. def random_planetesimal(disk): a = random.uniform(disk.planet_inner_bound, disk.planet_outer_bound) e = 1.0 - (random.uniform(0.0, 1.0) ** ECCENTRICITY_COEFF) if e > .99: e = .99 return Planetesimal( disk=disk, orbit=Orbit( a=a, e=e, ), dust_mass=PROTOPLANET_MASS, gas_mass=0, ) def generate_planetary_masses(star, inner_dust, outer_dust, do_moons=True): disk = CircumstellarDisk(star) planets = [] sequential_failures = 0 while disk.dust_left and sequential_failures < 10**3: canidate = random_planetesimal(disk) iel = canidate.inner_effect_limit oel = canidate.outer_effect_limit if disk.dust_available(iel, oel) > 0: sequential_failures = 0 logging.info("Injecting planetesimal at " + str(canidate.orbit.a) + " AU ...") disk.accrete_dust(canidate) if canidate.mass > PROTOPLANET_MASS: coalesce_planetesimals(disk, planets, canidate, do_moons) logging.info("\tsuccess.\n") else: logging.info("\tfailed due to large neighbor.\n") else: sequential_failures += 1 return planets def convert_planetesimal_to_protoplanet(planetesimal): return Protoplanet( star=planetesimal.disk.star, orbit=planetesimal.orbit, dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass ) def convert_planetesimal_to_protomoon(planetesimal, planet): print(" Capturing a protomoon.") return Protomoon( protoplanet=planet, orbit=Orbit( a=None, e=None, ), dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass, ) def coalesce_planetesimals(disk, planets, canidate, do_moons): finished = False # First we try to find an existing planet with an over-lapping orbit. for planet in planets: #print("Out of order", planet, canidate) diff = planet.orbit.a - canidate.orbit.a if diff > 0.0: dist1 = canidate.orbit.apoapsis * (1.0 + canidate.reduced_mass) - canidate.orbit.a # x aphelion dist2 = planet.orbit.a - (planet.orbit.periapsis * (1.0 - planet.reduced_mass)) else: dist1 = canidate.orbit.a - (canidate.orbit.periapsis * (1.0 - canidate.reduced_mass)) # x perihelion dist2 = (planet.orbit.apoapsis * (1.0 + planet.reduced_mass)) - planet.orbit.a if abs(diff) <= abs(dist1) or abs(diff) <= abs(dist2): # Figure out the new orbit. a = (planet.mass + canidate.mass) / \ ((planet.mass / planet.orbit.a) + (canidate.mass / canidate.orbit.a)) temp = planet.mass * sqrt(planet.orbit.a) * sqrt(1.0 - (planet.orbit.e ** 2.0)) temp = temp + (canidate.mass * sqrt(canidate.orbit.a) * sqrt(sqrt(1.0 - (canidate.orbit.e ** 2.0)))) temp = temp / ((planet.mass + canidate.mass) * sqrt(canidate.orbit.a)) temp = 1.0 - (temp ** 2.0) if temp < 0.0 or temp >= 1.0: temp = 0.0 e = sqrt(temp) if do_moons: if canidate.mass < canidate.critical_mass: if canidate.mass * SUN_MASS_IN_EARTH_MASSES < 2.5 \ and canidate.mass * SUN_MASS_IN_EARTH_MASSES > .0001 \ and planet.mass_of_moons < planet.mass * .05 \ and planet.mass > canidate.mass: # TODO: Remove planet.mass > canidate.mass distinction, just switch the canidate and planet! planet.add_moon( convert_planetesimal_to_protomoon(canidate, planet)) logging.info("Moon captured at " + str(planet.orbit.a) + " AU. Planet Mass: " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses Moon Mass: " + str(canidate.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses.") finished = True break else: # TODO: Reasons. logging.info("Did not capture potential moon at " + str(planet.orbit.a) + " AU. Collision imminent.") logging.info( "Collision between two planetesimals! Computing new orbit and accumulating additional mass.") # Accrete MORE DUST! TODO: Refactor to this. disk.accrete_dust(planet) planet.orbit = Orbit(a=a, e=e) ##### planet.orbit_a = a planet.orbit_e = e planet.dust_mass = planet.dust_mass + canidate.dust_mass # + new_dust planet.gas_mass = planet.gas_mass + canidate.gas_mass # + new_gas finished = True logging.info( "Conglomerate is now " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses at " + str(planet.orbit.a) + " AU." ) if not finished: # TODO: Extra info. logging.info("New Protoplanet at " + str(canidate.orbit.a) + "AU.") planets.append(convert_planetesimal_to_protoplanet(canidate)) def calculate_gases(star, planet, planet_id): if planet.surf_pressure > 0: amount = [0 for _ in range(len(gases))] totamount = 0 pressure = planet.surf_pressure/MILLIBARS_PER_BAR n = 0 for i in range(len(gases)): yp = gases[i].boil / \ (373. * ((log((pressure) + 0.001) / -5050.5) + (1.0 / 373.))) if ((yp >= 0 and yp < planet.low_temp) and (gases[i].weight >= planet.molec_weight)): vrms = rms_vel(gases[i].weight, planet.exospheric_temp) pvrms = pow(1 / (1 + vrms / planet.esc_velocity), star.age / 1e9) abund = gases[i].abunds # gases[i].abunde react = 1.0 fract = 1.0 pres2 = 1.0 if gases[i].symbol == "Ar": react = .15 * star.age/4e9 elif gases[i].symbol == "He": abund = abund * (0.001 + (planet.gas_mass / planet.mass)) pres2 = (0.75 + pressure) react = pow(1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) elif (gases[i].symbol == "O" or gases[i].symbol == "O2") and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.89 + pressure/4) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.25) * pres2) elif gases[i].symbol == "CO2" and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.75 + pressure) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.5) * pres2) react *= 1.5 else: pres2 = 0.75 + pressure react = pow( 1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) fract = (1 - (planet.molec_weight / gases[i].weight)) amount[i] = abund * pvrms * react * fract '''if ((flag_verbose & 0x4000) and (strcmp(gases[i].symbol, "O") == 0 or strcmp(gases[i].symbol, "N") == 0 or strcmp(gases[i].symbol, "Ar") == 0 or strcmp(gases[i].symbol, "He") == 0 or strcmp(gases[i].symbol, "CO2") == 0)) fprintf (stderr, "%-5.2Lf %-3.3s, %-5.2Lf = a %-5.2Lf * p %-5.2Lf * r %-5.2Lf * p2 %-5.2Lf * f %-5.2Lf\t(%.3Lf%%)\n", planet.mass * SUN_MASS_IN_EARTH_MASSES, gases[i].symbol, amount[i], abund, pvrms, react, pres2, fract, 100.0 * (planet.gas_mass / planet.mass) )''' totamount += amount[i] if (amount[i] > 0.0): n += 1 else: amount[i] = 0.0 if n > 0: planet.gases = n planet.atmosphere = [] for i in range(len(gases)): if amount[i] > 0.0: planet.atmosphere.append((gases[i], planet.surf_pressure * amount[i] / totamount)) '''if (flag_verbose & 0x2000) if ((planet.atmosphere[n].num == AN_O) and inspired_partial_pressure (planet.surf_pressure, planet.atmosphere[n].surf_pressure) > gases[i].max_ipp) fprintf (stderr, "%s\t Poisoned by O2\n", planet_id)''' n += 1 # TODO(woursler): sort planet.atmosphere '''if (flag_verbose & 0x0010): fprintf (stderr, "\n%s (%5.1Lf AU) gases:\n", planet_id, planet.orbit.a) for (i = 0; i < planet.gases; i++) fprintf (stderr, "%3d: %6.1Lf, %11.7Lf%%\n", planet.atmosphere[i].num, planet.atmosphere[i].surf_pressure, 100. * (planet.atmosphere[i].surf_pressure / planet.surf_pressure) )''' def roche_limit(planet, moon): return 2.44 * planet.radius * pow((planet.density / moon.density), (1.0 / 3.0)) def hill_sphere(planet, star): return planet.orbit.a * KM_PER_AU * pow((planet.mass / (3.0 * star.mass_ratio)), (1.0 / 3.0)) def generate_planet(protoplanet, star, random_tilt=0, planet_id=None, do_gases=True, do_moons=True, is_moon=False): planet = Planet( sun=star, orbit=protoplanet.orbit, dust_mass=protoplanet.dust_mass, gas_mass=protoplanet.gas_mass, mass=protoplanet.mass, axial_tilt=inclination(protoplanet.orbit.a) if random_tilt else 0, atmosphere=None, surf_temp=0, high_temp=0, low_temp=0, max_temp=0, min_temp=0, greenhs_rise=0, resonant_period=False, orbit_zone=orb_zone(star.luminosity_ratio, protoplanet.orbit.a), orb_period=period(protoplanet.orbit.a, protoplanet.mass, star.mass_ratio) ) planet.exospheric_temp = EARTH_EXOSPHERE_TEMP / \ ((planet.orbit.a / star.r_ecosphere) ** 2) planet.rms_velocity = rms_vel(MOL_NITROGEN, planet.exospheric_temp) planet.core_radius = kothari_radius( planet.dust_mass, False, planet.orbit_zone) # Calculate the radius as a gas giant, to verify it will retain gas. # Then if mass > Earth, it's at least 5% gas and retains He, it's # some flavor of gas giant. planet.density = empirical_density( planet.mass, planet.orbit.a, star.r_ecosphere, True) planet.radius = volume_radius(planet.mass, planet.density) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) if (((planet.mass * SUN_MASS_IN_EARTH_MASSES) > 1.0) and ((planet.gas_mass / planet.mass) > 0.05) and (min_molec_weight(planet) <= 4.0)): if ((planet.gas_mass / planet.mass) < 0.20): planet.type = PlanetType.SUB_SUB_GAS_GIANT elif ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < 20.0): planet.type = PlanetType.SUB_GAS_GIANT else: planet.type = PlanetType.GAS_GIANT else: # If not, it's rocky. planet.radius = kothari_radius(planet.mass, False, planet.orbit_zone) planet.density = volume_density(planet.mass, planet.radius) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if ((planet.gas_mass / planet.mass) > 0.000001): h2_mass = planet.gas_mass * 0.85 he_mass = (planet.gas_mass - h2_mass) * 0.999 h2_loss = 0.0 he_loss = 0.0 h2_life = gas_life(MOL_HYDROGEN, planet) he_life = gas_life(HELIUM, planet) if (h2_life < star.age): #math.exp with a value above 709 results in a math range error #this is a dumb fix. STH 2021-0131 if (star.age / h2_life)>709: h2_loss = ((1.0 - (1.0 / exp(709.0))) * h2_mass) else: h2_loss = ((1.0 - (1.0 / exp(star.age / h2_life))) * h2_mass) planet.gas_mass -= h2_loss planet.mass -= h2_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if (he_life < star.age): he_loss = ((1.0 - (1.0 / exp(star.age / he_life))) * he_mass) planet.gas_mass -= he_loss planet.mass -= he_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) '''if (((h2_loss + he_loss) > .000001) and (flag_verbose & 0x0080)): fprintf(stderr, "%s\tLosing gas: H2: %5.3Lf EM, He: %5.3Lf EM\n", planet_id, h2_loss * SUN_MASS_IN_EARTH_MASSES, he_loss * SUN_MASS_IN_EARTH_MASSES)''' planet.day = day_length(planet) # Modifies planet.resonant_period planet.esc_velocity = escape_vel(planet.mass, planet.radius) if planet.type == PlanetType.GAS_GIANT or planet.type == PlanetType.SUB_GAS_GIANT or planet.type == PlanetType.SUB_SUB_GAS_GIANT: planet.greenhouse_effect = False planet.volatile_gas_inventory = INCREDIBLY_LARGE_NUMBER planet.surf_pressure = INCREDIBLY_LARGE_NUMBER planet.boil_point = INCREDIBLY_LARGE_NUMBER planet.surf_temp = INCREDIBLY_LARGE_NUMBER planet.greenhs_rise = 0 planet.albedo = about(GAS_GIANT_ALBEDO, 0.1) planet.hydrosphere = 1.0 planet.cloud_cover = 1.0 planet.ice_cover = 0.0 planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.surf_grav = INCREDIBLY_LARGE_NUMBER planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, planet.albedo) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) temp = planet.estimated_terr_temp if (temp >= FREEZING_POINT_OF_WATER) and (temp <= EARTH_AVERAGE_KELVIN + 10.) and (star.age > 2.0E9): pass '''if (flag_verbose & 0x8000): fprintf (stderr, "%s\t%s (%4.2LfEM %5.3Lf By)%s with earth-like temperature (%.1Lf C, %.1Lf F, %+.1Lf C Earth).\n", planet_id, planet.type == PlanetType.GAS_GIANT ? "Jovian" : planet.type == PlanetType.SUB_GAS_GIANT ? "Sub-Jovian" : planet.type == PlanetType.SUB_SUB_GAS_GIANT ? "Gas Dwarf" : "Big", planet.mass * SUN_MASS_IN_EARTH_MASSES, star.age /1.0E9, planet.first_moon == NULL ? "" : " WITH MOON", temp - FREEZING_POINT_OF_WATER, 32 + ((temp - FREEZING_POINT_OF_WATER) * 1.8), temp - EARTH_AVERAGE_KELVIN)''' else: planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.greenhouse_effect = grnhouse(star.r_ecosphere, planet.orbit.a) planet.volatile_gas_inventory = vol_inventory(planet.mass, planet.esc_velocity, planet.rms_velocity, star.mass_ratio, planet.orbit_zone, planet.greenhouse_effect, (planet.gas_mass / planet.mass) > 0.000001) planet.surf_pressure = pressure(planet.volatile_gas_inventory, planet.radius, planet.surf_grav) if ((planet.surf_pressure == 0.0)): planet.boil_point = 0.0 else: planet.boil_point = boiling_point(planet.surf_pressure) # Sets: # planet.surf_temp # planet.greenhs_rise # planet.albedo # planet.hydrosphere # planet.cloud_cover # planet.ice_cover iterate_surface_temp(planet) if (do_gases and (planet.max_temp >= FREEZING_POINT_OF_WATER) and (planet.min_temp <= planet.boil_point)): calculate_gases(star, planet, planet_id) # Next we assign a type to the planet. if (planet.surf_pressure < 1.0): if (not is_moon) and ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < ASTEROID_MASS_LIMIT): planet.type = PlanetType.ASTEROIDS else: planet.type = PlanetType.ROCK elif (planet.surf_pressure > 6000.0) and (planet.molec_weight <= 2.0): # Retains Hydrogen planet.type = PlanetType.SUB_SUB_GAS_GIANT planet.gases = 0 planet.atmosphere = None else: # Atmospheres: if (int(planet.day) == int(planet.orb_period * 24.0)) or planet.resonant_period: planet.type = PlanetType.ONE_FACE elif (planet.hydrosphere >= 0.95): planet.type = PlanetType.WATER # >95% water elif (planet.ice_cover >= 0.95): planet.type = PlanetType.ICE # >95% ice elif (planet.hydrosphere > 0.05): planet.type = PlanetType.TERRESTRIAL # Terrestrial # else <5% water elif (planet.max_temp > planet.boil_point): planet.type = PlanetType.VENUSIAN # Hot = Venusian elif ((planet.gas_mass / planet.mass) > 0.0001): # Accreted gas planet.type = PlanetType.ICE # But no Greenhouse planet.ice_cover = 1.0 # or liquid water # Make it an Ice World elif (planet.surf_pressure <= 250.0): # Thin air = Martian planet.type = PlanetType.MARTIAN elif (planet.surf_temp < FREEZING_POINT_OF_WATER): planet.type = PlanetType.ICE else: planet.type = PlanetType.UNKNOWN # TODO(woursler): Consider throwing an error here. '''if (flag_verbose & 0x0001) fprintf (stderr, "%12s\tp=%4.2Lf\tm=%4.2Lf\tg=%4.2Lf\tt=%+.1Lf\t%s\t Unknown %s\n", type_string (planet.type), planet.surf_pressure, planet.mass * SUN_MASS_IN_EARTH_MASSES, planet.surf_grav, planet.surf_temp - EARTH_AVERAGE_KELVIN, planet_id, ((int)planet.day == (int)(planet.orb_period * 24.0) or (planet.resonant_period)) ? "(1-Face)" : "" )''' if do_moons and not is_moon: for protomoon in protoplanet.moons: if protomoon.mass * SUN_MASS_IN_EARTH_MASSES > .000001: protomoon.orbit = planet.orbit # Note: adjusts density, which is used in computing the roche limit. moon = generate_planet( protoplanet=protomoon, star=star, random_tilt=random_tilt, do_gases=do_gases, do_moons=do_moons, is_moon=True ) # TODO(woursler): these should be their own subroutines. roche_limit_r = roche_limit(planet, moon) hill_sphere_r = hill_sphere(planet, star) if (roche_limit_r * 3.0) < hill_sphere_r: moon_a = random_number( roche_limit_r * 1.5, hill_sphere_r / 2.0) / KM_PER_AU moon_e = random_eccentricity() moon.orbit = Orbit(a=moon_a, e=moon_e) else: moon.orbit = Orbit(a=0, e=0) planet.moons.append(moon) ''' if (flag_verbose & 0x40000): fprintf (stderr, " Roche limit: R = %4.2Lg, rM = %4.2Lg, rm = %4.2Lg . %.0Lf km\n" " Hill Sphere: a = %4.2Lg, m = %4.2Lg, M = %4.2Lg . %.0Lf km\n" "%s Moon orbit: a = %.0Lf km, e = %.0Lg\n", planet.radius, planet.density, ptr.density, roche_limit, planet.orbit.a * KM_PER_AU, planet.mass * SOLAR_MASS_IN_KILOGRAMS, star.mass_ratio * SOLAR_MASS_IN_KILOGRAMS, hill_sphere, moon_id, ptr.moon_a * KM_PER_AU, ptr.moon_e ) if (flag_verbose & 0x1000): fprintf (stderr, " %s: (%7.2LfEM) %d %4.2LgEM\n", planet_id, planet.mass * SUN_MASS_IN_EARTH_MASSES, n, ptr.mass * SUN_MASS_IN_EARTH_MASSES)''' return planet ### # Smoke Test ### if __name__ == '__main__': random.seed('earth') print(generate_stellar_system(random_star()))

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284

0.533874

import logging import random from math import sqrt, log from stellar_system import Star from stellar_system import Planetesimal from stellar_system import Protoplanet from stellar_system import Protomoon from stellar_system import Planet from stellar_system import Orbit from accrete import CircumstellarDisk from constants import ECCENTRICITY_COEFF, PROTOPLANET_MASS from constants import SUN_MASS_IN_EARTH_MASSES from constants import EARTH_ALBEDO, GAS_GIANT_ALBEDO, FREEZING_POINT_OF_WATER, KM_PER_AU, EARTH_AVERAGE_KELVIN, EARTH_EXOSPHERE_TEMP from constants import MOL_NITROGEN, MOL_HYDROGEN, HELIUM from constants import ASTEROID_MASS_LIMIT from constants import MILLIBARS_PER_BAR from enviroment import kothari_radius, gas_life, rms_vel, est_temp, period, day_length, acceleration, gravity, min_molec_weight, orb_zone, volume_radius, volume_density, grnhouse, boiling_point, escape_vel, empirical_density, inclination, iterate_surface_temp, pressure, vol_inventory from enviroment import PlanetType from math import exp from math import inf as INCREDIBLY_LARGE_NUMBER from util import about, random_number, random_eccentricity from chemtable import gases logging.getLogger().setLevel(logging.INFO) def random_star(): e(1*10**9, 6*10**9) return Star(age=age, mass_ratio=1) def generate_stellar_system(star, do_gases=True, do_moons=True): protoplanets = generate_planetary_masses( star, 0.0, star.stellar_dust_limit, do_moons=do_moons ) star.planets = [ generate_planet( p, star, do_gases=do_gases, do_moons=do_moons ) for p in protoplanets ] return star def random_planetesimal(disk): a = random.uniform(disk.planet_inner_bound, disk.planet_outer_bound) e = 1.0 - (random.uniform(0.0, 1.0) ** ECCENTRICITY_COEFF) if e > .99: e = .99 return Planetesimal( disk=disk, orbit=Orbit( a=a, e=e, ), dust_mass=PROTOPLANET_MASS, gas_mass=0, ) def generate_planetary_masses(star, inner_dust, outer_dust, do_moons=True): disk = CircumstellarDisk(star) planets = [] sequential_failures = 0 while disk.dust_left and sequential_failures < 10**3: canidate = random_planetesimal(disk) iel = canidate.inner_effect_limit oel = canidate.outer_effect_limit if disk.dust_available(iel, oel) > 0: sequential_failures = 0 logging.info("Injecting planetesimal at " + str(canidate.orbit.a) + " AU ...") disk.accrete_dust(canidate) if canidate.mass > PROTOPLANET_MASS: coalesce_planetesimals(disk, planets, canidate, do_moons) logging.info("\tsuccess.\n") else: logging.info("\tfailed due to large neighbor.\n") else: sequential_failures += 1 return planets def convert_planetesimal_to_protoplanet(planetesimal): return Protoplanet( star=planetesimal.disk.star, orbit=planetesimal.orbit, dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass ) def convert_planetesimal_to_protomoon(planetesimal, planet): print(" Capturing a protomoon.") return Protomoon( protoplanet=planet, orbit=Orbit( a=None, e=None, ), dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass, ) def coalesce_planetesimals(disk, planets, canidate, do_moons): finished = False for planet in planets: diff = planet.orbit.a - canidate.orbit.a if diff > 0.0: dist1 = canidate.orbit.apoapsis * (1.0 + canidate.reduced_mass) - canidate.orbit.a dist2 = planet.orbit.a - (planet.orbit.periapsis * (1.0 - planet.reduced_mass)) else: dist1 = canidate.orbit.a - (canidate.orbit.periapsis * (1.0 - canidate.reduced_mass)) dist2 = (planet.orbit.apoapsis * (1.0 + planet.reduced_mass)) - planet.orbit.a if abs(diff) <= abs(dist1) or abs(diff) <= abs(dist2): a = (planet.mass + canidate.mass) / \ ((planet.mass / planet.orbit.a) + (canidate.mass / canidate.orbit.a)) temp = planet.mass * sqrt(planet.orbit.a) * sqrt(1.0 - (planet.orbit.e ** 2.0)) temp = temp + (canidate.mass * sqrt(canidate.orbit.a) * sqrt(sqrt(1.0 - (canidate.orbit.e ** 2.0)))) temp = temp / ((planet.mass + canidate.mass) * sqrt(canidate.orbit.a)) temp = 1.0 - (temp ** 2.0) if temp < 0.0 or temp >= 1.0: temp = 0.0 e = sqrt(temp) if do_moons: if canidate.mass < canidate.critical_mass: if canidate.mass * SUN_MASS_IN_EARTH_MASSES < 2.5 \ and canidate.mass * SUN_MASS_IN_EARTH_MASSES > .0001 \ and planet.mass_of_moons < planet.mass * .05 \ and planet.mass > canidate.mass: planet.add_moon( convert_planetesimal_to_protomoon(canidate, planet)) logging.info("Moon captured at " + str(planet.orbit.a) + " AU. Planet Mass: " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses Moon Mass: " + str(canidate.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses.") finished = True break else: logging.info("Did not capture potential moon at " + str(planet.orbit.a) + " AU. Collision imminent.") logging.info( "Collision between two planetesimals! Computing new orbit and accumulating additional mass.") disk.accrete_dust(planet) planet.orbit = Orbit(a=a, e=e) planet.orbit_a = a planet.orbit_e = e planet.dust_mass = planet.dust_mass + canidate.dust_mass planet.gas_mass = planet.gas_mass + canidate.gas_mass finished = True logging.info( "Conglomerate is now " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses at " + str(planet.orbit.a) + " AU." ) if not finished: logging.info("New Protoplanet at " + str(canidate.orbit.a) + "AU.") planets.append(convert_planetesimal_to_protoplanet(canidate)) def calculate_gases(star, planet, planet_id): if planet.surf_pressure > 0: amount = [0 for _ in range(len(gases))] totamount = 0 pressure = planet.surf_pressure/MILLIBARS_PER_BAR n = 0 for i in range(len(gases)): yp = gases[i].boil / \ (373. * ((log((pressure) + 0.001) / -5050.5) + (1.0 / 373.))) if ((yp >= 0 and yp < planet.low_temp) and (gases[i].weight >= planet.molec_weight)): vrms = rms_vel(gases[i].weight, planet.exospheric_temp) pvrms = pow(1 / (1 + vrms / planet.esc_velocity), star.age / 1e9) abund = gases[i].abunds react = 1.0 fract = 1.0 pres2 = 1.0 if gases[i].symbol == "Ar": react = .15 * star.age/4e9 elif gases[i].symbol == "He": abund = abund * (0.001 + (planet.gas_mass / planet.mass)) pres2 = (0.75 + pressure) react = pow(1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) elif (gases[i].symbol == "O" or gases[i].symbol == "O2") and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.89 + pressure/4) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.25) * pres2) elif gases[i].symbol == "CO2" and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.75 + pressure) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.5) * pres2) react *= 1.5 else: pres2 = 0.75 + pressure react = pow( 1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) fract = (1 - (planet.molec_weight / gases[i].weight)) amount[i] = abund * pvrms * react * fract totamount += amount[i] if (amount[i] > 0.0): n += 1 else: amount[i] = 0.0 if n > 0: planet.gases = n planet.atmosphere = [] for i in range(len(gases)): if amount[i] > 0.0: planet.atmosphere.append((gases[i], planet.surf_pressure * amount[i] / totamount)) n += 1 def roche_limit(planet, moon): return 2.44 * planet.radius * pow((planet.density / moon.density), (1.0 / 3.0)) def hill_sphere(planet, star): return planet.orbit.a * KM_PER_AU * pow((planet.mass / (3.0 * star.mass_ratio)), (1.0 / 3.0)) def generate_planet(protoplanet, star, random_tilt=0, planet_id=None, do_gases=True, do_moons=True, is_moon=False): planet = Planet( sun=star, orbit=protoplanet.orbit, dust_mass=protoplanet.dust_mass, gas_mass=protoplanet.gas_mass, mass=protoplanet.mass, axial_tilt=inclination(protoplanet.orbit.a) if random_tilt else 0, atmosphere=None, surf_temp=0, high_temp=0, low_temp=0, max_temp=0, min_temp=0, greenhs_rise=0, resonant_period=False, orbit_zone=orb_zone(star.luminosity_ratio, protoplanet.orbit.a), orb_period=period(protoplanet.orbit.a, protoplanet.mass, star.mass_ratio) ) planet.exospheric_temp = EARTH_EXOSPHERE_TEMP / \ ((planet.orbit.a / star.r_ecosphere) ** 2) planet.rms_velocity = rms_vel(MOL_NITROGEN, planet.exospheric_temp) planet.core_radius = kothari_radius( planet.dust_mass, False, planet.orbit_zone) planet.density = empirical_density( planet.mass, planet.orbit.a, star.r_ecosphere, True) planet.radius = volume_radius(planet.mass, planet.density) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) if (((planet.mass * SUN_MASS_IN_EARTH_MASSES) > 1.0) and ((planet.gas_mass / planet.mass) > 0.05) and (min_molec_weight(planet) <= 4.0)): if ((planet.gas_mass / planet.mass) < 0.20): planet.type = PlanetType.SUB_SUB_GAS_GIANT elif ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < 20.0): planet.type = PlanetType.SUB_GAS_GIANT else: planet.type = PlanetType.GAS_GIANT else: planet.radius = kothari_radius(planet.mass, False, planet.orbit_zone) planet.density = volume_density(planet.mass, planet.radius) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if ((planet.gas_mass / planet.mass) > 0.000001): h2_mass = planet.gas_mass * 0.85 he_mass = (planet.gas_mass - h2_mass) * 0.999 h2_loss = 0.0 he_loss = 0.0 h2_life = gas_life(MOL_HYDROGEN, planet) he_life = gas_life(HELIUM, planet) if (h2_life < star.age): #math.exp with a value above 709 results in a math range error #this is a dumb fix. STH 2021-0131 if (star.age / h2_life)>709: h2_loss = ((1.0 - (1.0 / exp(709.0))) * h2_mass) else: h2_loss = ((1.0 - (1.0 / exp(star.age / h2_life))) * h2_mass) planet.gas_mass -= h2_loss planet.mass -= h2_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if (he_life < star.age): he_loss = ((1.0 - (1.0 / exp(star.age / he_life))) * he_mass) planet.gas_mass -= he_loss planet.mass -= he_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) '''if (((h2_loss + he_loss) > .000001) and (flag_verbose & 0x0080)): fprintf(stderr, "%s\tLosing gas: H2: %5.3Lf EM, He: %5.3Lf EM\n", planet_id, h2_loss * SUN_MASS_IN_EARTH_MASSES, he_loss * SUN_MASS_IN_EARTH_MASSES)''' planet.day = day_length(planet) # Modifies planet.resonant_period planet.esc_velocity = escape_vel(planet.mass, planet.radius) if planet.type == PlanetType.GAS_GIANT or planet.type == PlanetType.SUB_GAS_GIANT or planet.type == PlanetType.SUB_SUB_GAS_GIANT: planet.greenhouse_effect = False planet.volatile_gas_inventory = INCREDIBLY_LARGE_NUMBER planet.surf_pressure = INCREDIBLY_LARGE_NUMBER planet.boil_point = INCREDIBLY_LARGE_NUMBER planet.surf_temp = INCREDIBLY_LARGE_NUMBER planet.greenhs_rise = 0 planet.albedo = about(GAS_GIANT_ALBEDO, 0.1) planet.hydrosphere = 1.0 planet.cloud_cover = 1.0 planet.ice_cover = 0.0 planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.surf_grav = INCREDIBLY_LARGE_NUMBER planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, planet.albedo) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) temp = planet.estimated_terr_temp if (temp >= FREEZING_POINT_OF_WATER) and (temp <= EARTH_AVERAGE_KELVIN + 10.) and (star.age > 2.0E9): pass else: planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.greenhouse_effect = grnhouse(star.r_ecosphere, planet.orbit.a) planet.volatile_gas_inventory = vol_inventory(planet.mass, planet.esc_velocity, planet.rms_velocity, star.mass_ratio, planet.orbit_zone, planet.greenhouse_effect, (planet.gas_mass / planet.mass) > 0.000001) planet.surf_pressure = pressure(planet.volatile_gas_inventory, planet.radius, planet.surf_grav) if ((planet.surf_pressure == 0.0)): planet.boil_point = 0.0 else: planet.boil_point = boiling_point(planet.surf_pressure) # Sets: # planet.surf_temp # planet.greenhs_rise # planet.albedo # planet.hydrosphere # planet.cloud_cover # planet.ice_cover iterate_surface_temp(planet) if (do_gases and (planet.max_temp >= FREEZING_POINT_OF_WATER) and (planet.min_temp <= planet.boil_point)): calculate_gases(star, planet, planet_id) # Next we assign a type to the planet. if (planet.surf_pressure < 1.0): if (not is_moon) and ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < ASTEROID_MASS_LIMIT): planet.type = PlanetType.ASTEROIDS else: planet.type = PlanetType.ROCK elif (planet.surf_pressure > 6000.0) and (planet.molec_weight <= 2.0): # Retains Hydrogen planet.type = PlanetType.SUB_SUB_GAS_GIANT planet.gases = 0 planet.atmosphere = None else: # Atmospheres: if (int(planet.day) == int(planet.orb_period * 24.0)) or planet.resonant_period: planet.type = PlanetType.ONE_FACE elif (planet.hydrosphere >= 0.95): planet.type = PlanetType.WATER # >95% water elif (planet.ice_cover >= 0.95): planet.type = PlanetType.ICE # >95% ice elif (planet.hydrosphere > 0.05): planet.type = PlanetType.TERRESTRIAL # Terrestrial # else <5% water elif (planet.max_temp > planet.boil_point): planet.type = PlanetType.VENUSIAN # Hot = Venusian elif ((planet.gas_mass / planet.mass) > 0.0001): # Accreted gas planet.type = PlanetType.ICE # But no Greenhouse planet.ice_cover = 1.0 # or liquid water # Make it an Ice World elif (planet.surf_pressure <= 250.0): # Thin air = Martian planet.type = PlanetType.MARTIAN elif (planet.surf_temp < FREEZING_POINT_OF_WATER): planet.type = PlanetType.ICE else: planet.type = PlanetType.UNKNOWN # TODO(woursler): Consider throwing an error here. '''if (flag_verbose & 0x0001) fprintf (stderr, "%12s\tp=%4.2Lf\tm=%4.2Lf\tg=%4.2Lf\tt=%+.1Lf\t%s\t Unknown %s\n", type_string (planet.type), planet.surf_pressure, planet.mass * SUN_MASS_IN_EARTH_MASSES, planet.surf_grav, planet.surf_temp - EARTH_AVERAGE_KELVIN, planet_id, ((int)planet.day == (int)(planet.orb_period * 24.0) or (planet.resonant_period)) ? "(1-Face)" : "" )''' if do_moons and not is_moon: for protomoon in protoplanet.moons: if protomoon.mass * SUN_MASS_IN_EARTH_MASSES > .000001: protomoon.orbit = planet.orbit # Note: adjusts density, which is used in computing the roche limit. moon = generate_planet( protoplanet=protomoon, star=star, random_tilt=random_tilt, do_gases=do_gases, do_moons=do_moons, is_moon=True ) # TODO(woursler): these should be their own subroutines. roche_limit_r = roche_limit(planet, moon) hill_sphere_r = hill_sphere(planet, star) if (roche_limit_r * 3.0) < hill_sphere_r: moon_a = random_number( roche_limit_r * 1.5, hill_sphere_r / 2.0) / KM_PER_AU moon_e = random_eccentricity() moon.orbit = Orbit(a=moon_a, e=moon_e) else: moon.orbit = Orbit(a=0, e=0) planet.moons.append(moon) return planet ### # Smoke Test ### if __name__ == '__main__': random.seed('earth') print(generate_stellar_system(random_star()))

true

f71fe0dc7aac7d9afad4c1f08c6e9b94fde74e57

4,160

py

Python

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

try: from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt5 import QtCore except: from PyQt4.QtGui import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt4 import QtCore from vqt.main import idlethread from vqt.basics import VBox from vqt.common import ACT ''' This is an example of a vivisect GUI extension module. Set the environment variable VIV_EXT_PATH to point at a directory full of python modules such as this to extend and implement your own vivisect features. The extension should be a python module, either in the form of a .py file or a directory with a __init__.py file. Either way, the module will be loaded into memory and the "vivExtension" function called. ''' from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit from vqt.main import idlethread from vqt.basics import VBox class ExampleToolbar(QToolBar): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QToolBar.__init__(self, parent=vwgui) # Add a label to the toolbar self.addWidget( QLabel('Example Toolbar:', parent=self) ) # Add an action button to the toolbar self.addAction('ONE', self.doOne) def doOne(self): self.vw.vprint('did one!') class ExampleWindow(QWidget): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QWidget.__init__(self, parent=vwgui) # Set the window title self.setWindowTitle('Example Window!') # Add a Button and a Text Edit object in a basic VBox layout button = QPushButton('My Button!', parent=self) textedit = QTextEdit('WOOT! Some text!', parent=self) self.setLayout( VBox(button, textedit) ) def vprint(vw, s, *args, **kwargs): vw.vprint(s % args) print(s % args) def ctxMenuHook(vw, va, expr, menu, parent, nav): ''' Context Menu handler (adds options as we wish) ''' try: if va == 0x41414141: menu.addAction('WAT?', ACT(vw.vprint, "We're at AAAA!")) menu.addAction('bookmark (B)', ACT(vw.getVivGui().addBookmark, va)) menu.addAction('YEEE HAH', ACT(vw.vprint, "YEE HAH %x %r %r %r %r" % (va, expr, menu, parent, nav))) menu.addAction('YEEE HAH1', ACT(vprint, vw, "YEE HAH %x %r %r %r %r", va, expr, menu, parent, nav)) except Exception as e: import traceback traceback.print_exc() class Crap: ''' This is a helpful class for storing vw and vwgui and "doing the thing" Currently Vivisect's Hot Keys are tied to the many gui widgets, so vw and vwgui are not available when the "thing" is called. ''' def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui def thing(self): vprint(self.vw, "Blah Blah Blah") def printUserInput(self): # ok is whether the "OK" button was pressed, utext is the user text utext, ok = QInputDialog.getText(self.vwgui, 'Enter...', 'User Text') vprint(self.vw, '%r: %r', ok, utext) @idlethread def vivExtension(vw, vwgui): # Create a toolbar and add it to the GUI toolbar = ExampleToolbar(vw, vwgui) vwgui.addToolBar(QtCore.Qt.TopToolBarArea, toolbar) # Create a new Vivisect Dock Window (based on a QWidget) window = ExampleWindow(vw, vwgui) d = vwgui.vqDockWidget(window, floating=True) d.resize(300,200) # Add a menu item vwgui.vqAddMenuField('&Example.&FooBar.&PrintDiscoveredStats', vw.printDiscoveredStats, ()) # hook context menu vw.addCtxMenuHook('example', ctxMenuHook) # add HotKeyTargets and HotKeys tempmod = Crap(vw, vwgui) vwgui.addHotKey('ctrl+p', 'file:hackme') vwgui.addHotKeyTarget('file:hackme', tempmod.thing) # Popups/Dialogs - add a menu entry to ask for input and print the output vwgui.vqAddMenuField("&Example.&FooBar.&PrintUserInput", tempmod.printUserInput, ()) # get Dock Windows by name for w, vqDW in vwgui.vqGetDockWidgetsByName('viv'): vprint(vw, "Window: %r DockWidget: %r (%r)", w, vqDW, w.getEnviNavName())

31.755725

111

0.665625

try: from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt5 import QtCore except: from PyQt4.QtGui import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt4 import QtCore from vqt.main import idlethread from vqt.basics import VBox from vqt.common import ACT from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit from vqt.main import idlethread from vqt.basics import VBox class ExampleToolbar(QToolBar): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QToolBar.__init__(self, parent=vwgui) self.addWidget( QLabel('Example Toolbar:', parent=self) ) self.addAction('ONE', self.doOne) def doOne(self): self.vw.vprint('did one!') class ExampleWindow(QWidget): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QWidget.__init__(self, parent=vwgui) self.setWindowTitle('Example Window!') button = QPushButton('My Button!', parent=self) textedit = QTextEdit('WOOT! Some text!', parent=self) self.setLayout( VBox(button, textedit) ) def vprint(vw, s, *args, **kwargs): vw.vprint(s % args) print(s % args) def ctxMenuHook(vw, va, expr, menu, parent, nav): try: if va == 0x41414141: menu.addAction('WAT?', ACT(vw.vprint, "We're at AAAA!")) menu.addAction('bookmark (B)', ACT(vw.getVivGui().addBookmark, va)) menu.addAction('YEEE HAH', ACT(vw.vprint, "YEE HAH %x %r %r %r %r" % (va, expr, menu, parent, nav))) menu.addAction('YEEE HAH1', ACT(vprint, vw, "YEE HAH %x %r %r %r %r", va, expr, menu, parent, nav)) except Exception as e: import traceback traceback.print_exc() class Crap: def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui def thing(self): vprint(self.vw, "Blah Blah Blah") def printUserInput(self): # ok is whether the "OK" button was pressed, utext is the user text utext, ok = QInputDialog.getText(self.vwgui, 'Enter...', 'User Text') vprint(self.vw, '%r: %r', ok, utext) @idlethread def vivExtension(vw, vwgui): # Create a toolbar and add it to the GUI toolbar = ExampleToolbar(vw, vwgui) vwgui.addToolBar(QtCore.Qt.TopToolBarArea, toolbar) # Create a new Vivisect Dock Window (based on a QWidget) window = ExampleWindow(vw, vwgui) d = vwgui.vqDockWidget(window, floating=True) d.resize(300,200) # Add a menu item vwgui.vqAddMenuField('&Example.&FooBar.&PrintDiscoveredStats', vw.printDiscoveredStats, ()) # hook context menu vw.addCtxMenuHook('example', ctxMenuHook) # add HotKeyTargets and HotKeys tempmod = Crap(vw, vwgui) vwgui.addHotKey('ctrl+p', 'file:hackme') vwgui.addHotKeyTarget('file:hackme', tempmod.thing) # Popups/Dialogs - add a menu entry to ask for input and print the output vwgui.vqAddMenuField("&Example.&FooBar.&PrintUserInput", tempmod.printUserInput, ()) # get Dock Windows by name for w, vqDW in vwgui.vqGetDockWidgetsByName('viv'): vprint(vw, "Window: %r DockWidget: %r (%r)", w, vqDW, w.getEnviNavName())

true

f71fe1a680e38a876089f6c92424e564085015cd

728

py

Python

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

null

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

null

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

1

2021-04-30T14:11:42.000Z

"""test Bit operations""" import pytest from aiomysql.bit import Bit @pytest.mark.parametrize( 'length, value, expected', ( (10, None, ValueError), (10, 1, 1), (10, '123', TypeError), (10, '0', 0), (10, '1', 1), (10, '010', 2), (10, '1010', 10), (10, '01000000000', ValueError), ), ) def test_bit(length, value, expected): """test different inputs""" bit = Bit(length) if expected in (ValueError, TypeError): with pytest.raises(expected): bit(value) else: assert bit(value).value == expected def test_as_binary(): """verify binary conversion""" bit = Bit(5)(10) assert bit.as_binary() == '1010'

22.060606

43

0.54533

import pytest from aiomysql.bit import Bit @pytest.mark.parametrize( 'length, value, expected', ( (10, None, ValueError), (10, 1, 1), (10, '123', TypeError), (10, '0', 0), (10, '1', 1), (10, '010', 2), (10, '1010', 10), (10, '01000000000', ValueError), ), ) def test_bit(length, value, expected): bit = Bit(length) if expected in (ValueError, TypeError): with pytest.raises(expected): bit(value) else: assert bit(value).value == expected def test_as_binary(): bit = Bit(5)(10) assert bit.as_binary() == '1010'

true

f71fe27393e1a3a75142186240ffddd9c0d963a4

15,812

py

Python

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

15

2015-01-21T12:29:01.000Z

2018-12-09T09:17:33.000Z

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

null

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

9

2015-12-12T09:12:46.000Z

2021-12-26T13:29:14.000Z

#!/usr/bin/env python """ """ __docformat__ = 'restructuredtext' __version__ = '$Id: $' import ctypes import pyglet from pyglet.libs.darwin import carbon, _oscheck, create_cfstring from pyglet.libs.darwin.constants import * import input import usage # non-broken c_void_p void_p = ctypes.POINTER(ctypes.c_int) class CFUUIDBytes(ctypes.Structure): _fields_ = [('byte%d' % i, ctypes.c_uint8) for i in range(16)] mach_port_t = void_p io_iterator_t = void_p kern_return_t = ctypes.c_int IOReturn = ctypes.c_uint CFDictionaryRef = void_p CFMutableDictionaryRef = void_p CFArrayRef = void_p CFUUIDRef = ctypes.POINTER(CFUUIDBytes) AbsoluteTime = ctypes.c_double HRESULT = ctypes.c_int REFIID = CFUUIDBytes IOHIDElementType = ctypes.c_int kIOHIDElementTypeInput_Misc = 1 kIOHIDElementTypeInput_Button = 2 kIOHIDElementTypeInput_Axis = 3 kIOHIDElementTypeInput_ScanCodes = 4 kIOHIDElementTypeOutput = 129 kIOHIDElementTypeFeature = 257 kIOHIDElementTypeCollection = 513 IOHIDElementCookie = ctypes.c_void_p MACH_PORT_NULL = 0 kIOHIDDeviceKey = "IOHIDDevice" kIOServicePlane = "IOService" kIOHIDProductIDKey = "ProductID" kCFNumberIntType = 9 kIOHIDOptionsTypeSeizeDevice = 1 kIOReturnExclusiveAccess = 0xe00002c5 carbon.CFUUIDGetConstantUUIDWithBytes.restype = CFUUIDRef kIOHIDDeviceUserClientTypeID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xFA, 0x12, 0xFA, 0x38, 0x6F, 0x1A, 0x11, 0xD4, 0xBA, 0x0C, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) kIOCFPlugInInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xC2, 0x44, 0xE8, 0x58, 0x10, 0x9C, 0x11, 0xD4, 0x91, 0xD4, 0x00, 0x50, 0xE4, 0xC6, 0x42, 0x6F) kIOHIDDeviceInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0x78, 0xBD, 0x42, 0x0C, 0x6F, 0x14, 0x11, 0xD4, 0x94, 0x74, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) class IOHIDEventStruct(ctypes.Structure): _fields_ = ( ('type', IOHIDElementType), ('elementCookie', IOHIDElementCookie), ('value', ctypes.c_int32), ('timestamp', AbsoluteTime), ('longValueSize', ctypes.c_uint32), ('longValue', ctypes.c_void_p) ) Self = ctypes.c_void_p class IUnknown(ctypes.Structure): _fields_ = ( ('_reserved', ctypes.c_void_p), ('QueryInterface', ctypes.CFUNCTYPE(HRESULT, Self, REFIID, ctypes.c_void_p)), ('AddRef', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ('Release', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ) # Most of these function prototypes are not filled in yet because I haven't # bothered. class IOHIDQueueInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('create', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32, ctypes.c_uint32)), ('dispose', ctypes.CFUNCTYPE(IOReturn, Self)), ('addElement', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie)), ('removeElement', ctypes.c_void_p), ('hasElement', ctypes.c_void_p), ('start', ctypes.CFUNCTYPE(IOReturn, Self)), ('stop', ctypes.CFUNCTYPE(IOReturn, Self)), ('getNextEvent', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.POINTER(IOHIDEventStruct), AbsoluteTime, ctypes.c_uint32)), ('setEventCallout', ctypes.c_void_p), ('getEventCallout', ctypes.c_void_p), ) class IOHIDDeviceInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('open', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32)), ('close', ctypes.CFUNCTYPE(IOReturn, Self)), ('setRemovalCallback', ctypes.c_void_p), ('getElementValue', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie, ctypes.POINTER(IOHIDEventStruct))), ('setElementValue', ctypes.c_void_p), ('queryElementValue', ctypes.c_void_p), ('startAllQueues', ctypes.c_void_p), ('stopAllQueues', ctypes.c_void_p), ('allocQueue', ctypes.CFUNCTYPE( ctypes.POINTER(ctypes.POINTER(IOHIDQueueInterface)), Self)), ('allocOutputTransaction', ctypes.c_void_p), # 1.2.1 (10.2.3) ('setReport', ctypes.c_void_p), ('getReport', ctypes.c_void_p), # 1.2.2 (10.3) ('copyMatchingElements', ctypes.CFUNCTYPE(IOReturn, Self, CFDictionaryRef, ctypes.POINTER(CFArrayRef))), ('setInterruptReportHandlerCallback', ctypes.c_void_p), ) def get_master_port(): master_port = mach_port_t() _oscheck( carbon.IOMasterPort(MACH_PORT_NULL, ctypes.byref(master_port)) ) return master_port def get_matching_dictionary(): carbon.IOServiceMatching.restype = CFMutableDictionaryRef matching_dictionary = carbon.IOServiceMatching(kIOHIDDeviceKey) return matching_dictionary def get_existing_devices(master_port, matching_dictionary): # Consumes reference to matching_dictionary iterator = io_iterator_t() _oscheck( carbon.IOServiceGetMatchingServices(master_port, matching_dictionary, ctypes.byref(iterator)) ) devices = list() while carbon.IOIteratorIsValid(iterator): device = carbon.IOIteratorNext(iterator) if not device: break devices.append(Device(device)) carbon.IOObjectRelease(iterator) return devices def cfstring_to_string(value_string): value_length = carbon.CFStringGetLength(value_string) buffer_length = carbon.CFStringGetMaximumSizeForEncoding( value_length, kCFStringEncodingUTF8) buffer = ctypes.c_buffer(buffer_length + 1) result = carbon.CFStringGetCString(value_string, buffer, len(buffer), kCFStringEncodingUTF8) if not result: return return buffer.value def cfnumber_to_int(value): result = ctypes.c_int() carbon.CFNumberGetValue(value, kCFNumberIntType, ctypes.byref(result)) return result.value def cfvalue_to_value(value): if not value: return None value_type = carbon.CFGetTypeID(value) if value_type == carbon.CFStringGetTypeID(): return cfstring_to_string(value) elif value_type == carbon.CFNumberGetTypeID(): return cfnumber_to_int(value) else: return None def get_property_value(properties, key): key_string = create_cfstring(key) value = ctypes.c_void_p() present = carbon.CFDictionaryGetValueIfPresent(properties, key_string, ctypes.byref(value)) carbon.CFRelease(key_string) if not present: return None return value def get_property(properties, key): return cfvalue_to_value(get_property_value(properties, key)) def dump_properties(properties): def func(key, value, context): print('%s = %s' % (cfstring_to_string(key), cfvalue_to_value(value))) CFDictionaryApplierFunction = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p) carbon.CFDictionaryApplyFunction(properties, CFDictionaryApplierFunction(func), None) class Device: """ :IVariables: `name` : str `manufacturer` : str """ def __init__(self, generic_device): self._init_properties(generic_device) self._device = self._get_device_interface(generic_device) self.elements = self._get_elements() self._open = False self._queue = None self._queue_depth = 8 # Number of events queue can buffer def _init_properties(self, generic_device): properties = CFMutableDictionaryRef() _oscheck( carbon.IORegistryEntryCreateCFProperties(generic_device, ctypes.byref(properties), None, 0) ) self.name = get_property(properties, "Product") self.manufacturer = get_property(properties, "Manufacturer") carbon.CFRelease(properties) def _get_device_interface(self, generic_device): plug_in_interface = \ ctypes.POINTER(ctypes.POINTER(IUnknown))() score = ctypes.c_int32() _oscheck( carbon.IOCreatePlugInInterfaceForService( generic_device, kIOHIDDeviceUserClientTypeID, kIOCFPlugInInterfaceID, ctypes.byref(plug_in_interface), ctypes.byref(score)) ) carbon.CFUUIDGetUUIDBytes.restype = CFUUIDBytes hid_device_interface = \ ctypes.POINTER(ctypes.POINTER(IOHIDDeviceInterface))() _oscheck( plug_in_interface.contents.contents.QueryInterface( plug_in_interface, carbon.CFUUIDGetUUIDBytes(kIOHIDDeviceInterfaceID), ctypes.byref(hid_device_interface)) ) plug_in_interface.contents.contents.Release(plug_in_interface) return hid_device_interface def _get_elements(self): elements_array = CFArrayRef() _oscheck( self._device.contents.contents.copyMatchingElements(self._device, None, ctypes.byref( elements_array)) ) self._element_cookies = dict() elements = list() n_elements = carbon.CFArrayGetCount(elements_array) for i in range(n_elements): properties = carbon.CFArrayGetValueAtIndex(elements_array, i) element = DeviceElement(self, properties) elements.append(element) self._element_cookies[element._cookie] = element carbon.CFRelease(elements_array) return elements def __repr__(self): return '%s(name=%r, manufacturer=%r)' % ( self.__class__.__name__, self.product, self.manufacturer) def open(self, exclusive=False): flags = 0 if exclusive: flags |= kIOHIDOptionsTypeSeizeDevice result = self._device.contents.contents.open(self._device, flags) if result == 0: self._open = True elif result == kIOReturnExclusiveAccess: raise input.InputDeviceExclusiveException() # Create event queue self._queue = self._device.contents.contents.allocQueue(self._device) _oscheck( self._queue.contents.contents.create(self._queue, 0, self._queue_depth) ) # Add all elements into queue # TODO: only "interesting/known" elements? for element in self.elements: r = self._queue.contents.contents.addElement(self._queue, element._cookie, 0) if r != 0: print('error adding %r' % element) _oscheck( self._queue.contents.contents.start(self._queue) ) # HACK TODO: pyglet.clock.schedule(self.dispatch_events) def close(self): if not self._open: return # HACK TODO: pyglet.clock.unschedule(self.dispatch_events) _oscheck( self._queue.contents.contents.stop(self._queue) ) _oscheck( self._queue.contents.contents.dispose(self._queue) ) self._queue.contents.contents.Release(self._queue) self._queue = None _oscheck( self._device.contents.contents.close(self._device) ) self._open = False # TODO: TEMP/HACK def dispatch_events(self, dt=None): if not self._open: return event = IOHIDEventStruct() r = self._queue.contents.contents.getNextEvent(self._queue, ctypes.byref(event), 0, 0) if r != 0: # Undocumented behaviour? returns 3758097127L when no events are # in queue (is documented to block) return try: element = self._element_cookies[event.elementCookie] element.value = event.value except KeyError: pass class DeviceElement: def __init__(self, device, properties): self.device = device self._cookie = get_property(properties, 'ElementCookie') _usage = get_property(properties, 'Usage') usage_page = get_property(properties, 'UsagePage') self.name = usage.get_element_usage_name(usage_page, _usage) self.known = usage.get_element_usage_known(usage_page, _usage) self.value = None def get_value(self): return self.value """ def get_value(self): event = IOHIDEventStruct() self.device._device.contents.contents.getElementValue( self.device._device, self._cookie, ctypes.byref(event)) return event.value """ def get_devices(): return get_existing_devices(get_master_port(), get_matching_dictionary())

34.982301

84

0.544397

__docformat__ = 'restructuredtext' __version__ = '$Id: $' import ctypes import pyglet from pyglet.libs.darwin import carbon, _oscheck, create_cfstring from pyglet.libs.darwin.constants import * import input import usage void_p = ctypes.POINTER(ctypes.c_int) class CFUUIDBytes(ctypes.Structure): _fields_ = [('byte%d' % i, ctypes.c_uint8) for i in range(16)] mach_port_t = void_p io_iterator_t = void_p kern_return_t = ctypes.c_int IOReturn = ctypes.c_uint CFDictionaryRef = void_p CFMutableDictionaryRef = void_p CFArrayRef = void_p CFUUIDRef = ctypes.POINTER(CFUUIDBytes) AbsoluteTime = ctypes.c_double HRESULT = ctypes.c_int REFIID = CFUUIDBytes IOHIDElementType = ctypes.c_int kIOHIDElementTypeInput_Misc = 1 kIOHIDElementTypeInput_Button = 2 kIOHIDElementTypeInput_Axis = 3 kIOHIDElementTypeInput_ScanCodes = 4 kIOHIDElementTypeOutput = 129 kIOHIDElementTypeFeature = 257 kIOHIDElementTypeCollection = 513 IOHIDElementCookie = ctypes.c_void_p MACH_PORT_NULL = 0 kIOHIDDeviceKey = "IOHIDDevice" kIOServicePlane = "IOService" kIOHIDProductIDKey = "ProductID" kCFNumberIntType = 9 kIOHIDOptionsTypeSeizeDevice = 1 kIOReturnExclusiveAccess = 0xe00002c5 carbon.CFUUIDGetConstantUUIDWithBytes.restype = CFUUIDRef kIOHIDDeviceUserClientTypeID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xFA, 0x12, 0xFA, 0x38, 0x6F, 0x1A, 0x11, 0xD4, 0xBA, 0x0C, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) kIOCFPlugInInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xC2, 0x44, 0xE8, 0x58, 0x10, 0x9C, 0x11, 0xD4, 0x91, 0xD4, 0x00, 0x50, 0xE4, 0xC6, 0x42, 0x6F) kIOHIDDeviceInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0x78, 0xBD, 0x42, 0x0C, 0x6F, 0x14, 0x11, 0xD4, 0x94, 0x74, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) class IOHIDEventStruct(ctypes.Structure): _fields_ = ( ('type', IOHIDElementType), ('elementCookie', IOHIDElementCookie), ('value', ctypes.c_int32), ('timestamp', AbsoluteTime), ('longValueSize', ctypes.c_uint32), ('longValue', ctypes.c_void_p) ) Self = ctypes.c_void_p class IUnknown(ctypes.Structure): _fields_ = ( ('_reserved', ctypes.c_void_p), ('QueryInterface', ctypes.CFUNCTYPE(HRESULT, Self, REFIID, ctypes.c_void_p)), ('AddRef', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ('Release', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ) # bothered. class IOHIDQueueInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('create', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32, ctypes.c_uint32)), ('dispose', ctypes.CFUNCTYPE(IOReturn, Self)), ('addElement', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie)), ('removeElement', ctypes.c_void_p), ('hasElement', ctypes.c_void_p), ('start', ctypes.CFUNCTYPE(IOReturn, Self)), ('stop', ctypes.CFUNCTYPE(IOReturn, Self)), ('getNextEvent', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.POINTER(IOHIDEventStruct), AbsoluteTime, ctypes.c_uint32)), ('setEventCallout', ctypes.c_void_p), ('getEventCallout', ctypes.c_void_p), ) class IOHIDDeviceInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('open', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32)), ('close', ctypes.CFUNCTYPE(IOReturn, Self)), ('setRemovalCallback', ctypes.c_void_p), ('getElementValue', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie, ctypes.POINTER(IOHIDEventStruct))), ('setElementValue', ctypes.c_void_p), ('queryElementValue', ctypes.c_void_p), ('startAllQueues', ctypes.c_void_p), ('stopAllQueues', ctypes.c_void_p), ('allocQueue', ctypes.CFUNCTYPE( ctypes.POINTER(ctypes.POINTER(IOHIDQueueInterface)), Self)), ('allocOutputTransaction', ctypes.c_void_p), # 1.2.1 (10.2.3) ('setReport', ctypes.c_void_p), ('getReport', ctypes.c_void_p), # 1.2.2 (10.3) ('copyMatchingElements', ctypes.CFUNCTYPE(IOReturn, Self, CFDictionaryRef, ctypes.POINTER(CFArrayRef))), ('setInterruptReportHandlerCallback', ctypes.c_void_p), ) def get_master_port(): master_port = mach_port_t() _oscheck( carbon.IOMasterPort(MACH_PORT_NULL, ctypes.byref(master_port)) ) return master_port def get_matching_dictionary(): carbon.IOServiceMatching.restype = CFMutableDictionaryRef matching_dictionary = carbon.IOServiceMatching(kIOHIDDeviceKey) return matching_dictionary def get_existing_devices(master_port, matching_dictionary): # Consumes reference to matching_dictionary iterator = io_iterator_t() _oscheck( carbon.IOServiceGetMatchingServices(master_port, matching_dictionary, ctypes.byref(iterator)) ) devices = list() while carbon.IOIteratorIsValid(iterator): device = carbon.IOIteratorNext(iterator) if not device: break devices.append(Device(device)) carbon.IOObjectRelease(iterator) return devices def cfstring_to_string(value_string): value_length = carbon.CFStringGetLength(value_string) buffer_length = carbon.CFStringGetMaximumSizeForEncoding( value_length, kCFStringEncodingUTF8) buffer = ctypes.c_buffer(buffer_length + 1) result = carbon.CFStringGetCString(value_string, buffer, len(buffer), kCFStringEncodingUTF8) if not result: return return buffer.value def cfnumber_to_int(value): result = ctypes.c_int() carbon.CFNumberGetValue(value, kCFNumberIntType, ctypes.byref(result)) return result.value def cfvalue_to_value(value): if not value: return None value_type = carbon.CFGetTypeID(value) if value_type == carbon.CFStringGetTypeID(): return cfstring_to_string(value) elif value_type == carbon.CFNumberGetTypeID(): return cfnumber_to_int(value) else: return None def get_property_value(properties, key): key_string = create_cfstring(key) value = ctypes.c_void_p() present = carbon.CFDictionaryGetValueIfPresent(properties, key_string, ctypes.byref(value)) carbon.CFRelease(key_string) if not present: return None return value def get_property(properties, key): return cfvalue_to_value(get_property_value(properties, key)) def dump_properties(properties): def func(key, value, context): print('%s = %s' % (cfstring_to_string(key), cfvalue_to_value(value))) CFDictionaryApplierFunction = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p) carbon.CFDictionaryApplyFunction(properties, CFDictionaryApplierFunction(func), None) class Device: def __init__(self, generic_device): self._init_properties(generic_device) self._device = self._get_device_interface(generic_device) self.elements = self._get_elements() self._open = False self._queue = None self._queue_depth = 8 # Number of events queue can buffer def _init_properties(self, generic_device): properties = CFMutableDictionaryRef() _oscheck( carbon.IORegistryEntryCreateCFProperties(generic_device, ctypes.byref(properties), None, 0) ) self.name = get_property(properties, "Product") self.manufacturer = get_property(properties, "Manufacturer") carbon.CFRelease(properties) def _get_device_interface(self, generic_device): plug_in_interface = \ ctypes.POINTER(ctypes.POINTER(IUnknown))() score = ctypes.c_int32() _oscheck( carbon.IOCreatePlugInInterfaceForService( generic_device, kIOHIDDeviceUserClientTypeID, kIOCFPlugInInterfaceID, ctypes.byref(plug_in_interface), ctypes.byref(score)) ) carbon.CFUUIDGetUUIDBytes.restype = CFUUIDBytes hid_device_interface = \ ctypes.POINTER(ctypes.POINTER(IOHIDDeviceInterface))() _oscheck( plug_in_interface.contents.contents.QueryInterface( plug_in_interface, carbon.CFUUIDGetUUIDBytes(kIOHIDDeviceInterfaceID), ctypes.byref(hid_device_interface)) ) plug_in_interface.contents.contents.Release(plug_in_interface) return hid_device_interface def _get_elements(self): elements_array = CFArrayRef() _oscheck( self._device.contents.contents.copyMatchingElements(self._device, None, ctypes.byref( elements_array)) ) self._element_cookies = dict() elements = list() n_elements = carbon.CFArrayGetCount(elements_array) for i in range(n_elements): properties = carbon.CFArrayGetValueAtIndex(elements_array, i) element = DeviceElement(self, properties) elements.append(element) self._element_cookies[element._cookie] = element carbon.CFRelease(elements_array) return elements def __repr__(self): return '%s(name=%r, manufacturer=%r)' % ( self.__class__.__name__, self.product, self.manufacturer) def open(self, exclusive=False): flags = 0 if exclusive: flags |= kIOHIDOptionsTypeSeizeDevice result = self._device.contents.contents.open(self._device, flags) if result == 0: self._open = True elif result == kIOReturnExclusiveAccess: raise input.InputDeviceExclusiveException() # Create event queue self._queue = self._device.contents.contents.allocQueue(self._device) _oscheck( self._queue.contents.contents.create(self._queue, 0, self._queue_depth) ) # Add all elements into queue # TODO: only "interesting/known" elements? for element in self.elements: r = self._queue.contents.contents.addElement(self._queue, element._cookie, 0) if r != 0: print('error adding %r' % element) _oscheck( self._queue.contents.contents.start(self._queue) ) # HACK TODO: pyglet.clock.schedule(self.dispatch_events) def close(self): if not self._open: return # HACK TODO: pyglet.clock.unschedule(self.dispatch_events) _oscheck( self._queue.contents.contents.stop(self._queue) ) _oscheck( self._queue.contents.contents.dispose(self._queue) ) self._queue.contents.contents.Release(self._queue) self._queue = None _oscheck( self._device.contents.contents.close(self._device) ) self._open = False # TODO: TEMP/HACK def dispatch_events(self, dt=None): if not self._open: return event = IOHIDEventStruct() r = self._queue.contents.contents.getNextEvent(self._queue, ctypes.byref(event), 0, 0) if r != 0: # Undocumented behaviour? returns 3758097127L when no events are # in queue (is documented to block) return try: element = self._element_cookies[event.elementCookie] element.value = event.value except KeyError: pass class DeviceElement: def __init__(self, device, properties): self.device = device self._cookie = get_property(properties, 'ElementCookie') _usage = get_property(properties, 'Usage') usage_page = get_property(properties, 'UsagePage') self.name = usage.get_element_usage_name(usage_page, _usage) self.known = usage.get_element_usage_known(usage_page, _usage) self.value = None def get_value(self): return self.value def get_devices(): return get_existing_devices(get_master_port(), get_matching_dictionary())

true

f71fe39b002b6987bd56fdc8f822aa4a1ab3f554

548

py

Python

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

import json class Struct(object): def __init__(self, data): for name, value in data.items(): setattr(self, name, self._wrap(value)) def _wrap(self, value): if isinstance(value, (tuple, list, set, frozenset)): return type(value)([self._wrap(v) for v in value]) else: return Struct(value) if isinstance(value, dict) else value config_path = "/home/pi/qrcode_detect/cfg.json" with open(config_path) as config: cfg = json.load(config, object_hook=Struct)

27.4

71

0.616788

import json class Struct(object): def __init__(self, data): for name, value in data.items(): setattr(self, name, self._wrap(value)) def _wrap(self, value): if isinstance(value, (tuple, list, set, frozenset)): return type(value)([self._wrap(v) for v in value]) else: return Struct(value) if isinstance(value, dict) else value config_path = "/home/pi/qrcode_detect/cfg.json" with open(config_path) as config: cfg = json.load(config, object_hook=Struct)

true

f71fe39bd8323e42c4bdd0221f1966de94ab7729

904

py

Python

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

2,150

2019-06-12T20:55:41.000Z

2022-03-21T07:14:51.000Z

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

124

2019-06-22T17:12:27.000Z

2022-02-26T11:43:13.000Z

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

329

2019-06-13T03:03:54.000Z

2022-03-30T07:04:55.000Z

# Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Example for commanding robot with position control using moveit planner """ import time from pyrobot import Robot def main(): target_joints = [ [0.704, -0.455, -0.159, 1.395, -1.240, 1.069, 2.477], [-0.341, -0.384, -0.018, 1.533, -0.977, -1.492, -1.084], ] config = dict(moveit_planner_type="ESTkConfigDefault") bot = Robot( "sawyer", use_arm=True, use_base=False, use_camera=False, use_gripper=True, arm_config=config, ) bot.arm.go_home() time.sleep(1) for joint in target_joints: bot.arm.set_joint_positions(joint, plan=True) time.sleep(1) bot.arm.go_home() if __name__ == "__main__": main()

21.52381

71

0.622788

import time from pyrobot import Robot def main(): target_joints = [ [0.704, -0.455, -0.159, 1.395, -1.240, 1.069, 2.477], [-0.341, -0.384, -0.018, 1.533, -0.977, -1.492, -1.084], ] config = dict(moveit_planner_type="ESTkConfigDefault") bot = Robot( "sawyer", use_arm=True, use_base=False, use_camera=False, use_gripper=True, arm_config=config, ) bot.arm.go_home() time.sleep(1) for joint in target_joints: bot.arm.set_joint_positions(joint, plan=True) time.sleep(1) bot.arm.go_home() if __name__ == "__main__": main()

true

f71fe3a7ec4b148032db722f5c53b2c067b9a249

1,863

py

Python

random_colors.py

electric-blue-green/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

1

2021-06-05T03:12:36.000Z

random_colors.py

aejb/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

1

2018-02-26T11:22:50.000Z

random_colors.py

electric-blue-green/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

null

import board import busio import time import random dotstar = busio.SPI(board.APA102_SCK, board.APA102_MOSI) #colors = [1, 128, 244] # set colors all to 1 colors = [random.randint(3, 240), random.randint(3, 240), random.randint(3, 240), ] # selects random start color in "safe zone" steps = [1, 3, 4] # set wavelength steps = [random.randint(1, 5), random.randint(1, 5), random.randint(1, 5)] # selects random step beteween 1 and 5 print("INIT") ## REPL def getColor(index, colors, steps): if colors[index] >= 255 or colors[index] <= 0: # flip the sign of the step at the max/min steps[index] *= -1 colors[index] += steps[index] # increment the value if colors[index] > 255: colors[index] = 255 # accounting for stepping over 255 if colors[index] < 0: colors[index] = 0 # accounting for stepping under 0 return (colors[index], colors, steps) # returns colors for index def setPixel(red, green, blue): # call setpixel if not dotstar.try_lock(): # see if clock is locked return #print("setting pixel to: %d %d %d" % (red, green, blue)) # debug dotstar.write(bytearray([0x00, 0x00, 0x00, 0x00, 0xff, blue, green, red, 0xff, 0xff, 0xff, 0xff])) dotstar.unlock() # pass new color while True: r, colors, steps = getColor(0, colors, steps) # gets red g, colors, steps = getColor(1, colors, steps) # gets green b, colors, steps = getColor(2, colors, steps) # gets blue print("STEP = ", steps, "COLOR = ", colors) # REPL debug print setPixel(r, g, b) # calls setPixel time.sleep(random.random()) # random wait time between 0 and 1

56.454545

131

0.574342

import board import busio import time import random dotstar = busio.SPI(board.APA102_SCK, board.APA102_MOSI) dint(3, 240), random.randint(3, 240), random.randint(3, 240), ] steps = [1, 3, 4] steps = [random.randint(1, 5), random.randint(1, 5), random.randint(1, 5)] print("INIT") etColor(index, colors, steps): if colors[index] >= 255 or colors[index] <= 0: steps[index] *= -1 colors[index] += steps[index] if colors[index] > 255: colors[index] = 255 if colors[index] < 0: colors[index] = 0 return (colors[index], colors, steps) def setPixel(red, green, blue): if not dotstar.try_lock(): return tstar.write(bytearray([0x00, 0x00, 0x00, 0x00, 0xff, blue, green, red, 0xff, 0xff, 0xff, 0xff])) dotstar.unlock() while True: r, colors, steps = getColor(0, colors, steps) g, colors, steps = getColor(1, colors, steps) b, colors, steps = getColor(2, colors, steps) print("STEP = ", steps, "COLOR = ", colors) setPixel(r, g, b) time.sleep(random.random())

true

f71fe485cdc1d845da328b9bf8355e5b4665fa3d

393

py

Python

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

# Akachukwu Obi, 2018 # Project Euler #6 # see .js file for build up def diffOfSumOfSquares(max): sumOfNumbers = max * (max + 1) / 2 # sum of n natural numbers is n(n + 1)/2 sumOfSquares = (max / 6.0) * (2 * max + 1) * (max + 1) # I used 6.0 to avoid getting a math.floor situation in puthon2.7 return sumOfNumbers * sumOfNumbers - sumOfSquares print(diffOfSumOfSquares(100)) #25164150.0

35.727273

121

0.689567

def diffOfSumOfSquares(max): sumOfNumbers = max * (max + 1) / 2 sumOfSquares = (max / 6.0) * (2 * max + 1) * (max + 1) return sumOfNumbers * sumOfNumbers - sumOfSquares print(diffOfSumOfSquares(100))

true

f71fe4adf7dd0cb5bea1c0b3a038207a017240b9

250

py

Python

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

import numpy as np atoms_to_save = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 18]) u_cdl = np.load("u_cdl.npy") v_cdl = np.load("v_cdl.npy") np.save("u_cdl_modified.npy", u_cdl[atoms_to_save]) np.save("v_cdl_modified.npy", v_cdl[atoms_to_save])

25

65

0.68

import numpy as np atoms_to_save = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 18]) u_cdl = np.load("u_cdl.npy") v_cdl = np.load("v_cdl.npy") np.save("u_cdl_modified.npy", u_cdl[atoms_to_save]) np.save("v_cdl_modified.npy", v_cdl[atoms_to_save])

true

f71fe97a19d5758ce4f745c12696da1bb54d0fba

15,439

py

Python

src/catsys/exe/_res.py

AtomCrafty/catsystem-py

11bd96708e3959be84e41e30397820ebaa54974d

[ "MIT" ]

6

2020-10-20T13:26:56.000Z

2022-02-15T05:26:38.000Z

src/catsys/exe/_res.py

AtomCrafty/catsystem-py

11bd96708e3959be84e41e30397820ebaa54974d

[ "MIT" ]

2

2020-10-20T16:15:35.000Z

2021-07-08T18:15:23.000Z

src/catsys/exe/_res.py

AtomCrafty/catsystem-py

11bd96708e3959be84e41e30397820ebaa54974d

[ "MIT" ]

1

2020-10-19T15:20:50.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- """Windows resource types Requires packages: pefile WARNING: This module is deprecated, broken, and will be removed eventually. """ __version__ = '0.0.1' __date__ = '2020-01-01' __author__ = 'Robert Jordan' __all__ = ['ResourceName', 'ResourceId'] ####################################################################################### import abc, collections, datetime, pefile from typing import Any, Dict, Iterable, List, Optional, Set, Text, Tuple, Union ## PREDECLARE TYPES ## ResName = Union[int,str] ResNameArg = Union[int,str,pefile.UnicodeStringWrapperPostProcessor,pefile.ResourceDirEntryData] OptionalResName = Optional[ResName] OptionalResNameArg = Optional[ResNameArg] class ResourceName(object): """ A single name or id for a resource. This can be the type, name, or language id. """ __slots__ = ('value',) def __new__(cls, resname:Union[int,str,'ResourceName',None]): if resname is None: return None elif cls is ResourceName: # and resname is not None: if resname.__class__ is ResourceName: return resname # Return existing instance return object.__new__(cls) def __init__(self, resname:Union[int,str,None]=None): """Convert argument to a valid resource name for use with ResourceId""" if isinstance(resname, ResourceName): resname = resname.value elif not isinstance(resname, (int,str)): raise TypeError('ResourceName() expected int, str or ResourceName, not {0}'.format(type(resname).__name__)) self.value:Union[int,str] = resname def __setattr__(self, key:str, value): if not hasattr(self, 'value'): # Very lazy method to check for initialization object.__setattr__(self, key, value) else: # Very lazy method to throw a different error on attribute existance val = object.__getattribute__(self, key) raise AttributeError('ResourceName object attribute {0!r} is read-only'.format(key)) @staticmethod def cast(other:Union[int,str,'ResourceName',None]) -> Union[int,str,None]: if other is None: return None elif isinstance(other, (int,str)): return other elif isinstance(other, ResourceName): return other.value else: raise TypeError('ResourceName cast() expected int, str, ResourceName or None, not {0}'.format(type(other).__name__)) @staticmethod def cast_entry(entry:Union[pefile.ResourceDataEntryData,pefile.UnicodeStringWrapperPostProcessor,None]) -> Union[int,str,None]: """Convert pefile module types to resource names.""" if entry is None: return None elif isinstance(entry, pefile.ResourceDirEntryData): return entry.name.decode() if entry.name is not None else entry.id elif isinstance(entry, pefile.UnicodeStringWrapperPostProcessor): return entry.decode() else: raise TypeError('ResourceName read_name() expected ResourceDirEntryData, UnicodeStringWrapperPostProcessor or None, not {0}'.format(type(entry).__name__)) @staticmethod def from_entry(entry:Union[pefile.ResourceDataEntryData,pefile.UnicodeStringWrapperPostProcessor,None]) -> 'ResourceName': """Convert pefile module types to resource names.""" if entry is None: return ResourceName(None) elif isinstance(entry, pefile.ResourceDirEntryData): return ResourceName(entry.name.decode() if entry.name is not None else entry.id) elif isinstance(entry, pefile.UnicodeStringWrapperPostProcessor): return ResourceName(entry.decode()) else: raise TypeError('ResourceName from_entry() expected ResourceDirEntryData, UnicodeStringWrapperPostProcessor or None, not {0}'.format(type(entry).__name__)) def __bool__(self) -> bool: #TODO: How to handle bool? #return self.value is not None return bool(self.value) def __int__(self) -> int: if isinstance(self.value, int): return self.value raise TypeError('ResourceName() cannot cast {0} to int'.format(type(self.value).__name__)) def __str__(self) -> str: return str(self.value) def __repr__(self) -> str: return 'ResourceName({0!r})'.format(self.value) #return repr(self.value) def __hash__(self) -> int: return hash(self.value) # int/str equality def __eq__(self, other) -> bool: return self.value == ResourceName._cast(other) def __ne__(self, other) -> bool: return self.value != ResourceName._cast(other) def __lt__(self, other) -> bool: return self.value < ResourceName._cast(other) def __gt__(self, other) -> bool: return self.value > ResourceName._cast(other) def __le__(self, other) -> bool: return self.value <= ResourceName._cast(other) def __ge__(self, other) -> bool: return self.value >= ResourceName._cast(other) @staticmethod def from_entry(entry:Union[pefile.ResourceDataEntryData,pefile.UnicodeStringWrapperPostProcessor,None]) -> 'ResourceName': """Convert pefile module types to resource names.""" if entry is None: return ResourceName(None) elif isinstance(entry, pefile.ResourceDirEntryData): return ResourceName(entry.name.decode() if entry.name is not None else entry.id) elif isinstance(entry, pefile.UnicodeStringWrapperPostProcessor): return ResourceName(entry.decode()) else: raise TypeError('ResourceName from_entry() expected ResourceDirEntryData, UnicodeStringWrapperPostProcessor or None, not {0}'.format(type(entry).__name__)) @property def id(self) -> Union[int,None]: """Returns the resource id when an int, otherwise None.""" return self.value if isinstance(self.value, int) else None @property def name(self) -> Union[str,None]: """Returns the resource name when a str, otherwise None.""" return self.value if isinstance(self.value, str) else None @staticmethod def _cast(other:OptionalResNameArg) -> Union[str,int,None]: if isinstance(other, ResourceName): return other.value elif isinstance(other, pefile.ResourceDirEntryData): return other.name.decode() if other.name is not None else other.id elif isinstance(other, pefile.UnicodeStringWrapperPostProcessor): return other.decode() return other ResourceName(None) # Instantiate global _ResourceIdNamedTuple = collections.namedtuple('_ResourceIdNamedTuple', ('type','name','lang')) class ResourceId(_ResourceIdNamedTuple): """ResourceId(type, name, lang)""" type:ResourceName name:ResourceName lang:ResourceName def __new__(cls, type:ResourceName=None, name:ResourceName=None, lang:ResourceName=None): if cls is ResourceId and type is not None and name is None and lang is None: if type.__class__ is ResourceId: return type # Return existing instance if isinstance(type, tuple) and name is None and lang is None: type, name, lang = type if type is not None and return tuple.__new__(cls, (ResourceName(type), ResourceName(name), ResourceName(lang))) def __repr__(self) -> str: return 'ResourceId(type={0.value!r}, name={1.value!r}, lang={2.value!r})'.format(*self) def __str__(self) -> str: return 'ResourceId(type={0.value!r}, name={1.value!r}, lang={2.value!r})'.format(*self) del _ResourceIdNamedTuple def read_struct(pe:pefile.PE, struct:pefile.Structure) -> bytes: return pe.get_string_at_rva(struct.OffsetToData, struct.Size) class Resource(object): def __init__(self, resid:ResourceId, resdata:Union[pefile.ResourceDirEntryData,pefile.ResourceDataEntryData], pe:Optional[pefile.PE]=None): if pe is not None and not isinstance(pe, pefile.PE): raise TypeError('Resource() argument pe must be pefile.PE or None, not {0}'.format(type(pe).__name__)) if resid is None or not isinstance(resid, (ResourceId, tuple)): raise TypeError('Resource() argument resid must be ResourceId or tuple, not {0}'.format(type(resid).__name__)) if resdata is None or not isinstance(resdata, (pefile.ResourceDirEntryData,pefile.ResourceDataEntryData)): raise TypeError('Resource() argument resdata must be pefile.ResourceDirEntryData or pefile.ResourceDataEntryData, not {0}'.format(type(resdata).__name__)) if not isinstance(resdata, pefile.ResourceDataEntryData): if not hasattr(resdata, 'data'): raise ValueError('Resource() argument resdata must have a data attribute when pefile.ResourceDirEntryData') resdata = resdata.data self._resid:ResourceId = resid if isinstance(resid, ResourceId) else ResourceId(*resid) self._resdata:pefile.ResourceDataEntryData = resdata self._data:bytes = self.read(pe) if pe is not None else None #(pe, resdata.struct) #self.read(pe) def read(self, pe:pefile.PE) -> bytes: if self._data is None: self._data = read_struct(pe, self._resdata.struct) return self._data @property def data(self) -> bytes: return self._data @property def offset(self) -> int: return self._resdata.struct.OffsetToData @property def size(self) -> int: return self._resdata.struct.Size @property def struct(self): return self._resdata.struct # def read(pe:pefile.PE=...) -> bytes: # if self._data is not None: # return self._data # #elif pe is None: # # raise TypeError('Data has') # return pe.get_string_at_rva(self.offset, self.size) def __repr__(self) -> str: return 'Resource(type={0.value!r}, name={1.value!r}, lang={2.value!r}, size={3!r})'.format(*self._resid, self.size) def __str__(self) -> str: return 'Resource(type={0.value!r}, name={1.value!r}, lang={2.value!r}, size={3!r})'.format(*self._resid, self.size) def _match_entry(entry:pefile.ResourceDirEntryData, search_set:Set[ResourceName]) -> Union[ResourceName,None]: entry_match = ResourceName.cast_entry(entry) if entry_match in search_set: return ResourceName(entry) search_set.remove(entry_match) return def find_resource_match(entry:pefile.ResourceDirEntryData, resname:ResourceName) -> Optional[ResourceName]: name:Union[int,str] = ResourceName.from_entry(entry) if resname is None: return ResourceName(name) elif name == resname: return resname return None def find_resources(pe:pefile.PE, search_id:ResourceId, read_data:bool=False) -> List[Resource]: if isinstance(pe, pefile.PE): if not hasattr(pe, 'DIRECTORY_ENTRY_RESOURCE'): raise TypeError('iter_resource*() argument pe (PE) does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) directory = pe.DIRECTORY_ENTRY_RESOURCE search_id = search_id if isinstance(search_id, ResourceId) else ResourceId(*search_id) results:List[Resource] = [] for entry_t in directory.entries: t = find_resource_match(entry_t, search_id.type) if t is None: continue for entry_n in entry_t.directory.entries: n = find_resource_match(entry_n, search_id.name) if n is None: continue for entry_l in entry_n.directory.entries: l = find_resource_match(entry_l, search_id.lang) if l is not None: results.append(Resource(ResourceId(t,n,l), entry_l.data, pe=pe if read_data else None)) return results # def iter_resources(pe:pefile.PE) # results:List[Resource]=[] # if isinstance(types, tuple) and not isinstance(types, ResourceId): # search_types = set(ResourceName(t) for t in types) # for entry_t in directory.entries: # entry_type = ResourceName.cast_entry(entry_t) # if search_id.type is not None and entry_type != search_id.type: # continue # entry_type = search_id.type or ResourceName(entry_type) # for entry_n in entry_t.directory.entries: # entry_type = ResourceName.cast_entry(entry_n) # if search_id.type is not None and entry_type != search_id.type: # continue # entry_type = search_id.type or ResourceName(entry_type) # for entry_l in entry_n.directory.entries: # l = ResourceName.cast_entry(entry_l) # if search_id.lang is None: # l = ResourceName(l) # elif l == search_id.lang: # l = search_id.lang # continue # l = search_id.lang or # if entry_type in search_types: # search_types.remove(entry_type) # def _iter_resource_names(entries): # search_types = set(ResourceName(t) for t in types) # for entry in directory.entries: # entry_type = ResourceName.cast_entry(entry) # if entry_type in search_types: # search_types.remove(entry_type) # def _iter_resource_types(entries) # def iter_resource_types(pe:pefile.PE) -> Iterable[ResourceName]: # if 'DIRECTORY_ENTRY_RESOURCE' not in pe: # raise ValueError('iter_resource*() argument pe does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) # entries = pe.DIRECTORY_ENTRY_RESOURCE.entries # for entry in entries: # yield ResourceName.from_entry(entry) # def iter_resource_names(pe:Union[pefile.PE,pefile.ResourceDirData,pefile.ResourceDirEntryData], types:Union[ResourceName,Tuple[ResourceName]]=...) -> Iterable[ResourceName]: # if isinstance(pe, pefile.PE): # if 'DIRECTORY_ENTRY_RESOURCE' not in pe: # raise TypeError('iter_resource*() argument pe (PE) does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) # directory = pe.DIRECTORY_ENTRY_RESOURCE # # elif isinstance(pe, pefile.ResourceDirData): # # # if not hasattr(pe, 'directory'): # # # raise TypeError('iter_resource*() argument pe (ResourceDirData) does not have {0!r}'.format('entries')) # # directory = pe # # elif isinstance(pe, pefile.ResourceDirEntryData): # # if not hasattr(pe, 'directory'): # # raise TypeError('iter_resource*() argument pe (ResourceDirEntryData) does not have {0!r}'.format('directory')) # # directory = pe.directory # #entries = pe.DIRECTORY_ENTRY_RESOURCE.entries # entries = directory.entries # if isinstance(types, tuple) and not isinstance(types, ResourceId): # search_types = set(ResourceName(t) for t in types) # for entry in directory.entries: # entry_type = ResourceName.cast_entry(entry) # if entry_type in search_types: # search_types.remove(entry_type) # for entry in directory.entries: # yield ResourceName.from_entry(entry) # def # pe2.parse_data_directories(directories=[pefile.DIRECTORY_ENTRY['IMAGE_DIRECTORY_ENTRY_IMPORT']]) # def iter_resource_names(pe:Union[pefile.PE,pefile.ResourceDirEntryData], type:Union[ResourceName,Tuple[ResourceName]]) -> Iterable[ResourceName]: # if isinstance(pe, pefile.ResourceDirEntryData): # if not hasattr(pe, 'directory'): # raise TypeError('iter_resource*() argument pe (as directory) does not have {0!r}'.format('directory')) # directory = # elif isinstance(pe, pefile.PE): # if 'DIRECTORY_ENTRY_RESOURCE' not in pe: # raise TypeError('iter_resource*() argument pe does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) # directory = pe.DIRECTORY_ENTRY_RESOURCE # if 'DIRECTORY_ENTRY_RESOURCE' not in pe: # raise ValueError('iter_resource*() argument pe does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) # if 'DIRECTORY_ENTRY_RESOURCE' not in pe: # return # entries = pe.DIRECTORY_ENTRY_RESOURCE.entries # for entry in entries: # yield ResourceName.from_entry(entry) # def iter_resource_ids(pe:pefile.PE, ) # def iter_resource_entries(directory:pefile.ResourceDirData) -> Iterable[ResourceName]: # entries = directory.entries # for entry in entries: # yield ResourceName.from_entry(entry)

43.985755

175

0.711704

"""Windows resource types Requires packages: pefile WARNING: This module is deprecated, broken, and will be removed eventually. """ __version__ = '0.0.1' __date__ = '2020-01-01' __author__ = 'Robert Jordan' __all__ = ['ResourceName', 'ResourceId'] ol: return self.value < ResourceName._cast(other) def __gt__(self, other) -> bool: return self.value > ResourceName._cast(other) def __le__(self, other) -> bool: return self.value <= ResourceName._cast(other) def __ge__(self, other) -> bool: return self.value >= ResourceName._cast(other) @staticmethod def from_entry(entry:Union[pefile.ResourceDataEntryData,pefile.UnicodeStringWrapperPostProcessor,None]) -> 'ResourceName': """Convert pefile module types to resource names.""" if entry is None: return ResourceName(None) elif isinstance(entry, pefile.ResourceDirEntryData): return ResourceName(entry.name.decode() if entry.name is not None else entry.id) elif isinstance(entry, pefile.UnicodeStringWrapperPostProcessor): return ResourceName(entry.decode()) else: raise TypeError('ResourceName from_entry() expected ResourceDirEntryData, UnicodeStringWrapperPostProcessor or None, not {0}'.format(type(entry).__name__)) @property def id(self) -> Union[int,None]: """Returns the resource id when an int, otherwise None.""" return self.value if isinstance(self.value, int) else None @property def name(self) -> Union[str,None]: """Returns the resource name when a str, otherwise None.""" return self.value if isinstance(self.value, str) else None @staticmethod def _cast(other:OptionalResNameArg) -> Union[str,int,None]: if isinstance(other, ResourceName): return other.value elif isinstance(other, pefile.ResourceDirEntryData): return other.name.decode() if other.name is not None else other.id elif isinstance(other, pefile.UnicodeStringWrapperPostProcessor): return other.decode() return other ResourceName(None) _ResourceIdNamedTuple = collections.namedtuple('_ResourceIdNamedTuple', ('type','name','lang')) class ResourceId(_ResourceIdNamedTuple): """ResourceId(type, name, lang)""" type:ResourceName name:ResourceName lang:ResourceName def __new__(cls, type:ResourceName=None, name:ResourceName=None, lang:ResourceName=None): if cls is ResourceId and type is not None and name is None and lang is None: if type.__class__ is ResourceId: return type if isinstance(type, tuple) and name is None and lang is None: type, name, lang = type if type is not None and return tuple.__new__(cls, (ResourceName(type), ResourceName(name), ResourceName(lang))) def __repr__(self) -> str: return 'ResourceId(type={0.value!r}, name={1.value!r}, lang={2.value!r})'.format(*self) def __str__(self) -> str: return 'ResourceId(type={0.value!r}, name={1.value!r}, lang={2.value!r})'.format(*self) del _ResourceIdNamedTuple def read_struct(pe:pefile.PE, struct:pefile.Structure) -> bytes: return pe.get_string_at_rva(struct.OffsetToData, struct.Size) class Resource(object): def __init__(self, resid:ResourceId, resdata:Union[pefile.ResourceDirEntryData,pefile.ResourceDataEntryData], pe:Optional[pefile.PE]=None): if pe is not None and not isinstance(pe, pefile.PE): raise TypeError('Resource() argument pe must be pefile.PE or None, not {0}'.format(type(pe).__name__)) if resid is None or not isinstance(resid, (ResourceId, tuple)): raise TypeError('Resource() argument resid must be ResourceId or tuple, not {0}'.format(type(resid).__name__)) if resdata is None or not isinstance(resdata, (pefile.ResourceDirEntryData,pefile.ResourceDataEntryData)): raise TypeError('Resource() argument resdata must be pefile.ResourceDirEntryData or pefile.ResourceDataEntryData, not {0}'.format(type(resdata).__name__)) if not isinstance(resdata, pefile.ResourceDataEntryData): if not hasattr(resdata, 'data'): raise ValueError('Resource() argument resdata must have a data attribute when pefile.ResourceDirEntryData') resdata = resdata.data self._resid:ResourceId = resid if isinstance(resid, ResourceId) else ResourceId(*resid) self._resdata:pefile.ResourceDataEntryData = resdata self._data:bytes = self.read(pe) if pe is not None else None lf, pe:pefile.PE) -> bytes: if self._data is None: self._data = read_struct(pe, self._resdata.struct) return self._data @property def data(self) -> bytes: return self._data @property def offset(self) -> int: return self._resdata.struct.OffsetToData @property def size(self) -> int: return self._resdata.struct.Size @property def struct(self): return self._resdata.struct 'Resource(type={0.value!r}, name={1.value!r}, lang={2.value!r}, size={3!r})'.format(*self._resid, self.size) def __str__(self) -> str: return 'Resource(type={0.value!r}, name={1.value!r}, lang={2.value!r}, size={3!r})'.format(*self._resid, self.size) def _match_entry(entry:pefile.ResourceDirEntryData, search_set:Set[ResourceName]) -> Union[ResourceName,None]: entry_match = ResourceName.cast_entry(entry) if entry_match in search_set: return ResourceName(entry) search_set.remove(entry_match) return def find_resource_match(entry:pefile.ResourceDirEntryData, resname:ResourceName) -> Optional[ResourceName]: name:Union[int,str] = ResourceName.from_entry(entry) if resname is None: return ResourceName(name) elif name == resname: return resname return None def find_resources(pe:pefile.PE, search_id:ResourceId, read_data:bool=False) -> List[Resource]: if isinstance(pe, pefile.PE): if not hasattr(pe, 'DIRECTORY_ENTRY_RESOURCE'): raise TypeError('iter_resource*() argument pe (PE) does not have {0!r}'.format('DIRECTORY_ENTRY_RESOURCE')) directory = pe.DIRECTORY_ENTRY_RESOURCE search_id = search_id if isinstance(search_id, ResourceId) else ResourceId(*search_id) results:List[Resource] = [] for entry_t in directory.entries: t = find_resource_match(entry_t, search_id.type) if t is None: continue for entry_n in entry_t.directory.entries: n = find_resource_match(entry_n, search_id.name) if n is None: continue for entry_l in entry_n.directory.entries: l = find_resource_match(entry_l, search_id.lang) if l is not None: results.append(Resource(ResourceId(t,n,l), entry_l.data, pe=pe if read_data else None)) return results

false

true

f71fead294ee942753789629c9a72d7384c394a0

2,087

py

Python

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

3

2020-12-22T09:03:15.000Z

2021-05-13T18:17:44.000Z

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

3

2020-11-11T15:33:13.000Z

2021-12-13T20:18:41.000Z

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

1

2021-02-18T19:56:09.000Z

import os from scrapy.crawler import CrawlerProcess import pandas as pd import logging import nltk import json_reader from sentiment_score import clean_text, calculate_sentiment_score from reddit_scraper.reddit_scraper.spiders.reddit_post_scraper import RedditPostCrawler if __name__ == '__main__': # Initial setup: Disable scrapy logs and download NLTK files logging.getLogger('scrapy').propagate = False nltk.download('averaged_perceptron_tagger', quiet=True) nltk.download('wordnet', quiet=True) # Ask for user query subreddit = input('Subreddit: ') term = input('Search term: ') term = term.replace(' ', '+') # Start crawler process print('[LOG] Crawling Reddit, this will take a little time...') process = CrawlerProcess(settings={ 'FEED_FORMAT': 'jl', 'FEED_URI': 'data.jl' }) process.crawl(RedditPostCrawler, domain=f'https://old.reddit.com/r/{subreddit}/search?q={term}&restrict_sr=on&sort=relevance&t=all') process.start() # Convert data file to class print('[LOG] Creating DataFrame table...') reddit_posts = json_reader.convert_json('data.jl') all_comments = [] all_upvotes = [] for post in reddit_posts: for comment in post.comments: all_comments.append(clean_text(comment.text)) # Convert upvote text to float, e.g. '15.3k upvotes' -> 15300 upvote = comment.upvotes.split(' ')[0] if 'k' in upvote: upvote = upvote[:-1] upvote = float(upvote) * 1000 all_upvotes.append(float(upvote)) df = pd.DataFrame({'comment': all_comments, 'upvotes': all_upvotes}) df = df[df.upvotes >= 1] print('[LOG] Calculating sentiment score, this may take a longer time...') df = calculate_sentiment_score(df) # df.to_csv('results.csv') normalized_result = df.sentiment.mean() print('[LOG] Completed!\n') print('Average sentiment:', normalized_result) print('where +1 is most positive and -1 is most negative') os.remove('data.jl')

33.66129

117

0.661236

import os from scrapy.crawler import CrawlerProcess import pandas as pd import logging import nltk import json_reader from sentiment_score import clean_text, calculate_sentiment_score from reddit_scraper.reddit_scraper.spiders.reddit_post_scraper import RedditPostCrawler if __name__ == '__main__': logging.getLogger('scrapy').propagate = False nltk.download('averaged_perceptron_tagger', quiet=True) nltk.download('wordnet', quiet=True) subreddit = input('Subreddit: ') term = input('Search term: ') term = term.replace(' ', '+') print('[LOG] Crawling Reddit, this will take a little time...') process = CrawlerProcess(settings={ 'FEED_FORMAT': 'jl', 'FEED_URI': 'data.jl' }) process.crawl(RedditPostCrawler, domain=f'https://old.reddit.com/r/{subreddit}/search?q={term}&restrict_sr=on&sort=relevance&t=all') process.start() print('[LOG] Creating DataFrame table...') reddit_posts = json_reader.convert_json('data.jl') all_comments = [] all_upvotes = [] for post in reddit_posts: for comment in post.comments: all_comments.append(clean_text(comment.text)) upvote = comment.upvotes.split(' ')[0] if 'k' in upvote: upvote = upvote[:-1] upvote = float(upvote) * 1000 all_upvotes.append(float(upvote)) df = pd.DataFrame({'comment': all_comments, 'upvotes': all_upvotes}) df = df[df.upvotes >= 1] print('[LOG] Calculating sentiment score, this may take a longer time...') df = calculate_sentiment_score(df) normalized_result = df.sentiment.mean() print('[LOG] Completed!\n') print('Average sentiment:', normalized_result) print('where +1 is most positive and -1 is most negative') os.remove('data.jl')

true

f71feb945e7c962481692be8e2384e367a3c4bbd

3,443

py

Python

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

112

2019-06-11T17:52:57.000Z

2022-03-18T00:05:21.000Z

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

91

2019-05-28T11:33:40.000Z

2022-02-27T12:12:07.000Z

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

69

2019-07-10T16:58:06.000Z

2022-03-30T22:09:44.000Z

"""The sqlalchemy model for a polloption.""" from __future__ import annotations from sqlalchemy import Column, ForeignKey, Index, func from sqlalchemy.orm import relationship from sqlalchemy.types import BigInteger, DateTime, Integer, String from pollbot.db import base from pollbot.enums import ReferenceType class Reference(base): """The model for a Reference.""" __tablename__ = "reference" __mapper_args__ = {"confirm_deleted_rows": False} id = Column(Integer, primary_key=True) type = Column(String) bot_inline_message_id = Column(String) message_id = Column(BigInteger) # Keep those for now, in case we migrate to mtproto message_dc_id = Column(BigInteger) message_access_hash = Column(BigInteger) user_id = Column( BigInteger, ForeignKey("user.id", ondelete="cascade", name="user_fk"), nullable=True, index=True, ) user = relationship("User", foreign_keys="Reference.user_id") created_at = Column(DateTime, server_default=func.now(), nullable=False) updated_at = Column( DateTime, server_default=func.now(), onupdate=func.now(), nullable=False ) # ManyToOne poll_id = Column( Integer, ForeignKey("poll.id", ondelete="cascade", name="reference_poll"), nullable=False, index=True, ) poll = relationship("Poll", back_populates="references") def __init__( self, poll, reference_type, user=None, message_id=None, inline_message_id=None, ): """Create a new poll.""" self.poll = poll self.type = reference_type # There are three types of references # 1. Messages in private chat: # - Admin interface # - Private vote if ( user is not None and message_id is not None and reference_type in [ReferenceType.admin.name, ReferenceType.private_vote.name] ): self.user = user self.message_id = message_id # 2. Messages shared via inline query elif ( inline_message_id is not None and reference_type == ReferenceType.inline.name ): self.bot_inline_message_id = inline_message_id else: raise Exception( "Tried to create Reference with wrong type or missing parameters" ) def __repr__(self): """Print as string.""" if self.type == ReferenceType.inline.name: message = f"Reference {self.id}: message_id {self.message_id}" elif self.type == ReferenceType.admin.name: message = f"Reference {self.id}: message_id {self.message_id}, admin: {self.user.id}" else: message = f"Reference {self.id}: message_id {self.message_id}, user: {self.user.id}" return message Index( "ix_unique_admin_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "admin", ) Index( "ix_unique_private_vote_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "private_vote", ) Index( "ix_unique_inline_share", Reference.poll_id, Reference.bot_inline_message_id, unique=True, postgresql_where=Reference.type == "inline", )

27.99187

97

0.637816

from __future__ import annotations from sqlalchemy import Column, ForeignKey, Index, func from sqlalchemy.orm import relationship from sqlalchemy.types import BigInteger, DateTime, Integer, String from pollbot.db import base from pollbot.enums import ReferenceType class Reference(base): __tablename__ = "reference" __mapper_args__ = {"confirm_deleted_rows": False} id = Column(Integer, primary_key=True) type = Column(String) bot_inline_message_id = Column(String) message_id = Column(BigInteger) message_dc_id = Column(BigInteger) message_access_hash = Column(BigInteger) user_id = Column( BigInteger, ForeignKey("user.id", ondelete="cascade", name="user_fk"), nullable=True, index=True, ) user = relationship("User", foreign_keys="Reference.user_id") created_at = Column(DateTime, server_default=func.now(), nullable=False) updated_at = Column( DateTime, server_default=func.now(), onupdate=func.now(), nullable=False ) poll_id = Column( Integer, ForeignKey("poll.id", ondelete="cascade", name="reference_poll"), nullable=False, index=True, ) poll = relationship("Poll", back_populates="references") def __init__( self, poll, reference_type, user=None, message_id=None, inline_message_id=None, ): self.poll = poll self.type = reference_type if ( user is not None and message_id is not None and reference_type in [ReferenceType.admin.name, ReferenceType.private_vote.name] ): self.user = user self.message_id = message_id elif ( inline_message_id is not None and reference_type == ReferenceType.inline.name ): self.bot_inline_message_id = inline_message_id else: raise Exception( "Tried to create Reference with wrong type or missing parameters" ) def __repr__(self): if self.type == ReferenceType.inline.name: message = f"Reference {self.id}: message_id {self.message_id}" elif self.type == ReferenceType.admin.name: message = f"Reference {self.id}: message_id {self.message_id}, admin: {self.user.id}" else: message = f"Reference {self.id}: message_id {self.message_id}, user: {self.user.id}" return message Index( "ix_unique_admin_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "admin", ) Index( "ix_unique_private_vote_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "private_vote", ) Index( "ix_unique_inline_share", Reference.poll_id, Reference.bot_inline_message_id, unique=True, postgresql_where=Reference.type == "inline", )

true

f71fec1a43d05877719e3969203eaab05fae4883

10,145

py

Python

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

26

2020-09-23T13:04:52.000Z

2022-03-03T03:07:49.000Z

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

5

2020-11-04T13:26:09.000Z

2021-09-17T07:42:01.000Z

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

12

2020-11-03T12:01:35.000Z

2021-12-19T03:58:50.000Z

""" Runs the scoring procedure for the challenge. It assumes that there exists a ./model_dir folder containing both the submission code and the saved learner. It will create a folder named ./scoring_output (default) in which a txt file will contain the average score over 600 episodes. You can change the folder name via the score_dir flag. Usage example executed from the metadl/ repository : python -m metadl.core.scoring.scoring --meta_test_dir=<path_dataset.meta_test> """ import os from sys import path import scipy.stats import gin import numpy as np from absl import app from absl import flags from absl import logging import tensorflow as tf from metadl.data.dataset import DataGenerator from metadl.core.ingestion.ingestion import get_gin_path, show_dir FLAGS = flags.FLAGS flags.DEFINE_string('meta_test_dir', '/Users/adrian/GitInria/meta-dataset/records/', ('Directory of the meta-test dataset. This directory ' + 'should contain records and a json spec file.')) flags.DEFINE_string('saved_model_dir', './model_dir', ('Directory path that contains the participant\'s code ' + 'along with the serialized learner from meta-fit.')) flags.DEFINE_string('score_dir', './scoring_output', 'Path to the score directory.') flags.DEFINE_string('evaltype', 'test', 'Data type on which to perform evaluation. [train, val, test]') tf.random.set_seed(1234) def NwayKshot_accuracy(predictions, ground_truth, metric): """ N-way, K-shot accuracy which corresponds to the accuracy in a multi-classification context with N classes. Args: predictions : tensors, sparse tensors corresponding to the predicted labels. ground_truth : tensors, sparse tensors corresponding the ground truth labels. metric : keras.metrics , the metric we use to evaluate the classification performance of the meta-learning algorithm. We use the SparseCategoricalAccuracy in this challenge. Retruns: score : Float, the resulting performance using the given metric. """ ground_truth = tf.expand_dims(ground_truth, axis = 1) predictions = tf.expand_dims(predictions, axis = 1) logging.debug('Predictions shape : {} - Ground truth shape : {}'.format( predictions.shape, ground_truth.shape)) metric.update_state(ground_truth, predictions) score = metric.result() logging.debug('An episode score: {}'.format(score)) metric.reset_states() return score def is_one_hot_vector(x, axis=None, keepdims=False): """Check if a vector 'x' is one-hot (i.e. one entry is 1 and others 0).""" norm_1 = np.linalg.norm(x, ord=1, axis=axis, keepdims=keepdims) norm_inf = np.linalg.norm(x, ord=np.inf, axis=axis, keepdims=keepdims) return np.logical_and(norm_1 == 1, norm_inf == 1) def write_score(score, conf_int, file_score, duration=-1): """Write score of the k-th task in the given file_score.""" file_score.write('set1_score: {:.6f}\n'.format(float(score))) file_score.write('conf_int: {:.3f}\n'.format(float(conf_int))) file_score.write('Duration: {:.6f}\n'.format(float(duration))) def extract_elapsed_time(saved_model_dir): """ Extracts elapsed time from the metadata file. It corresponds to the meta-training time, the duration of the ingestion process. """ if not os.path.isdir(saved_model_dir): raise ValueError('Saved model directory does not exists.') if os.path.isfile(os.path.join(saved_model_dir, 'metadata')): with open(os.path.join(saved_model_dir, 'metadata'), 'r') as f : lines = f.readlines() for line in lines : splitted_line = line.split(' ') for k, word in enumerate(splitted_line): if 'elapsed' in splitted_line[k]: elapsed_time = float(splitted_line[k+1]) return elapsed_time return -1 def process_task(task): """We are using the meta-dataset code to generate episodes from a dataset. Generated episodes have a specific format. Each is processed such that the the support and query sets are ready to be used by the participants. Each set is returned as a tf.data.Dataset object. The que_labs are kept hidden. Returns : support_dataset : tf.data.Dataset containing the support examples and labels. query_dataset : tf.data.Dataset containing the query examples que_labs : tuple (query_batch_size, 1), the query examples labels i.e. the ground truth labels. """ sup_set = tf.data.Dataset.from_tensor_slices(\ (task[0][1], task[0][0])) dim = task[0][4].shape[1] arr = np.arange(dim) np.random.shuffle(arr) # shuffling arr query_labs = task[0][4] query_imgs = task[0][3] query_labs_s = tf.gather(query_labs, arr, axis=1) query_imgs_s = tf.gather(query_imgs, arr, axis=1) que_set = tf.data.Dataset.from_tensor_slices( (query_labs_s, query_imgs_s) ) new_ds = tf.data.Dataset.zip((sup_set, que_set)) for ((supp_labs, supp_img), (que_labs, que_img)) \ in new_ds : logging.debug('Supp labs : {}'.format(supp_labs)) logging.debug('Query labs : {}'.format(que_labs)) support_set = tf.data.Dataset.from_tensor_slices(\ (supp_img, supp_labs)) query_set = tf.data.Dataset.from_tensor_slices(\ (que_img,)) support_set = support_set.batch(5) query_set = query_set.batch(95) return support_set, query_set, que_labs def scoring(argv): """ For each task, load and fit the Learner with the support set and evaluate the submission performance with the query set. A directory 'scoring_output' is created and contains a txt file that contains the submission score and duration. Note that the former is the time elapsed during the ingestion program and hence the meta-fit() duration. The metric considered here is the Sparse Categorical Accuracy for a 5 classes image classification problem. """ del argv saved_model_dir = FLAGS.saved_model_dir meta_test_dir = FLAGS.meta_test_dir eval_type = FLAGS.evaltype # Making eval type compatible with DataGenerator specs if eval_type == 'train' or eval_type == 'val': data_generator_eval_type = 'train' elif eval_type == 'test': data_generator_eval_type = 'test' # Use CodaLab's path `run/input/ref` in parallel with `run/input/res` if not os.path.isdir(meta_test_dir): meta_test_dir = os.path.join(saved_model_dir, os.pardir, 'ref') # Evaluation type scenario: if meta_test is specified -> act as normal # scoring on meta_test data if (eval_type == 'train' or eval_type == 'val') and 'meta_test' in meta_test_dir: raise ValueError('Cannot perform train/val evaluation on meta-test data!') #if 'meta_test' not in meta_test_dir: # if eval_type == 'test': # meta_test_dir = os.path.join(meta_test_dir, 'meta_test') # else: # meta_test_dir = os.path.join(meta_test_dir, 'meta_train') code_dir = os.path.join(saved_model_dir, 'code_dir') score_dir = FLAGS.score_dir path.append(code_dir) from model import MyLearner if(os.path.exists(os.path.join(code_dir, 'model.gin'))): gin.parse_config_file(os.path.join(code_dir, 'model.gin')) logging.info('Ingestion done! Starting scoring process ... ') logging.info('Creating the meta-test episode generator ... \n ') generator = DataGenerator(path_to_records=meta_test_dir, batch_config=None, episode_config=[28, 5, 1, 19], pool= data_generator_eval_type, mode='episode') if eval_type == 'test': meta_test_dataset = generator.meta_test_pipeline elif eval_type == 'train': meta_test_dataset = generator.meta_train_pipeline elif eval_type == 'val': meta_test_dataset = generator.meta_valid_pipeline else: raise ValueError('Wrong eval_type : {}'.format(eval_type)) logging.info('Evaluating performance on episodes ... ') meta_test_dataset = meta_test_dataset.batch(1) meta_test_dataset = meta_test_dataset.prefetch(5) learner = MyLearner() if (not os.path.isdir(score_dir)): os.mkdir(score_dir) score_file = os.path.join(score_dir, 'scores.txt') results = [] metric = tf.metrics.SparseCategoricalAccuracy() nbr_episodes = 600 for k , task in enumerate(meta_test_dataset) : support_set, query_set, ground_truth = process_task(task) learner.load(saved_model_dir) predictor = learner.fit(support_set) predictions = predictor.predict(query_set) score = NwayKshot_accuracy(predictions, ground_truth, metric) results.append(score) logging.debug('Score on {} : {}'.format(k, score)) logging.debug('Results : {}'.format(results[:20])) if(k > nbr_episodes): break def mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) return m, h m, conf_int = mean_confidence_interval(results) with open(score_file, 'w') as f : write_score(m, conf_int, f, extract_elapsed_time(saved_model_dir)) logging.info(('Scoring done! The average score over {} ' + 'episodes is : {:.3%}').format(nbr_episodes, sum(results)/len(results)) ) if __name__ == '__main__': np.random.seed(seed=1234) tf.get_logger().setLevel('ERROR') app.run(scoring)

38.869732

99

0.648398

import os from sys import path import scipy.stats import gin import numpy as np from absl import app from absl import flags from absl import logging import tensorflow as tf from metadl.data.dataset import DataGenerator from metadl.core.ingestion.ingestion import get_gin_path, show_dir FLAGS = flags.FLAGS flags.DEFINE_string('meta_test_dir', '/Users/adrian/GitInria/meta-dataset/records/', ('Directory of the meta-test dataset. This directory ' + 'should contain records and a json spec file.')) flags.DEFINE_string('saved_model_dir', './model_dir', ('Directory path that contains the participant\'s code ' + 'along with the serialized learner from meta-fit.')) flags.DEFINE_string('score_dir', './scoring_output', 'Path to the score directory.') flags.DEFINE_string('evaltype', 'test', 'Data type on which to perform evaluation. [train, val, test]') tf.random.set_seed(1234) def NwayKshot_accuracy(predictions, ground_truth, metric): ground_truth = tf.expand_dims(ground_truth, axis = 1) predictions = tf.expand_dims(predictions, axis = 1) logging.debug('Predictions shape : {} - Ground truth shape : {}'.format( predictions.shape, ground_truth.shape)) metric.update_state(ground_truth, predictions) score = metric.result() logging.debug('An episode score: {}'.format(score)) metric.reset_states() return score def is_one_hot_vector(x, axis=None, keepdims=False): norm_1 = np.linalg.norm(x, ord=1, axis=axis, keepdims=keepdims) norm_inf = np.linalg.norm(x, ord=np.inf, axis=axis, keepdims=keepdims) return np.logical_and(norm_1 == 1, norm_inf == 1) def write_score(score, conf_int, file_score, duration=-1): file_score.write('set1_score: {:.6f}\n'.format(float(score))) file_score.write('conf_int: {:.3f}\n'.format(float(conf_int))) file_score.write('Duration: {:.6f}\n'.format(float(duration))) def extract_elapsed_time(saved_model_dir): if not os.path.isdir(saved_model_dir): raise ValueError('Saved model directory does not exists.') if os.path.isfile(os.path.join(saved_model_dir, 'metadata')): with open(os.path.join(saved_model_dir, 'metadata'), 'r') as f : lines = f.readlines() for line in lines : splitted_line = line.split(' ') for k, word in enumerate(splitted_line): if 'elapsed' in splitted_line[k]: elapsed_time = float(splitted_line[k+1]) return elapsed_time return -1 def process_task(task): sup_set = tf.data.Dataset.from_tensor_slices(\ (task[0][1], task[0][0])) dim = task[0][4].shape[1] arr = np.arange(dim) np.random.shuffle(arr) # shuffling arr query_labs = task[0][4] query_imgs = task[0][3] query_labs_s = tf.gather(query_labs, arr, axis=1) query_imgs_s = tf.gather(query_imgs, arr, axis=1) que_set = tf.data.Dataset.from_tensor_slices( (query_labs_s, query_imgs_s) ) new_ds = tf.data.Dataset.zip((sup_set, que_set)) for ((supp_labs, supp_img), (que_labs, que_img)) \ in new_ds : logging.debug('Supp labs : {}'.format(supp_labs)) logging.debug('Query labs : {}'.format(que_labs)) support_set = tf.data.Dataset.from_tensor_slices(\ (supp_img, supp_labs)) query_set = tf.data.Dataset.from_tensor_slices(\ (que_img,)) support_set = support_set.batch(5) query_set = query_set.batch(95) return support_set, query_set, que_labs def scoring(argv): del argv saved_model_dir = FLAGS.saved_model_dir meta_test_dir = FLAGS.meta_test_dir eval_type = FLAGS.evaltype # Making eval type compatible with DataGenerator specs if eval_type == 'train' or eval_type == 'val': data_generator_eval_type = 'train' elif eval_type == 'test': data_generator_eval_type = 'test' # Use CodaLab's path `run/input/ref` in parallel with `run/input/res` if not os.path.isdir(meta_test_dir): meta_test_dir = os.path.join(saved_model_dir, os.pardir, 'ref') if (eval_type == 'train' or eval_type == 'val') and 'meta_test' in meta_test_dir: raise ValueError('Cannot perform train/val evaluation on meta-test data!') code_dir = os.path.join(saved_model_dir, 'code_dir') score_dir = FLAGS.score_dir path.append(code_dir) from model import MyLearner if(os.path.exists(os.path.join(code_dir, 'model.gin'))): gin.parse_config_file(os.path.join(code_dir, 'model.gin')) logging.info('Ingestion done! Starting scoring process ... ') logging.info('Creating the meta-test episode generator ... \n ') generator = DataGenerator(path_to_records=meta_test_dir, batch_config=None, episode_config=[28, 5, 1, 19], pool= data_generator_eval_type, mode='episode') if eval_type == 'test': meta_test_dataset = generator.meta_test_pipeline elif eval_type == 'train': meta_test_dataset = generator.meta_train_pipeline elif eval_type == 'val': meta_test_dataset = generator.meta_valid_pipeline else: raise ValueError('Wrong eval_type : {}'.format(eval_type)) logging.info('Evaluating performance on episodes ... ') meta_test_dataset = meta_test_dataset.batch(1) meta_test_dataset = meta_test_dataset.prefetch(5) learner = MyLearner() if (not os.path.isdir(score_dir)): os.mkdir(score_dir) score_file = os.path.join(score_dir, 'scores.txt') results = [] metric = tf.metrics.SparseCategoricalAccuracy() nbr_episodes = 600 for k , task in enumerate(meta_test_dataset) : support_set, query_set, ground_truth = process_task(task) learner.load(saved_model_dir) predictor = learner.fit(support_set) predictions = predictor.predict(query_set) score = NwayKshot_accuracy(predictions, ground_truth, metric) results.append(score) logging.debug('Score on {} : {}'.format(k, score)) logging.debug('Results : {}'.format(results[:20])) if(k > nbr_episodes): break def mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) return m, h m, conf_int = mean_confidence_interval(results) with open(score_file, 'w') as f : write_score(m, conf_int, f, extract_elapsed_time(saved_model_dir)) logging.info(('Scoring done! The average score over {} ' + 'episodes is : {:.3%}').format(nbr_episodes, sum(results)/len(results)) ) if __name__ == '__main__': np.random.seed(seed=1234) tf.get_logger().setLevel('ERROR') app.run(scoring)

true

f71fed6c463f4fb9305f4215a3d3f237674e9c98

6,399

py

Python

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

3

2020-12-29T04:07:58.000Z

2022-01-11T14:47:16.000Z

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

1

2021-01-02T10:28:07.000Z

2021-01-04T18:01:42.000Z

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

1

2022-01-09T12:55:41.000Z

#!/usr/bin/env python # pylint: disable=W0201 import sys import argparse import yaml import numpy as np # torch import torch import torch.nn as nn import torch.optim as optim # torchlight import torchlight from torchlight import str2bool from torchlight import DictAction from torchlight import import_class from .processor import Processor from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv1d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('Conv2d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) class REC_Processor(Processor): """ Processor for Skeleton-based Action Recgnition """ def load_model(self): # print("load model") self.model = self.io.load_model(self.arg.model, **(self.arg.model_args)) self.model.apply(weights_init) self.loss = nn.CrossEntropyLoss() # self.loss = nn.BCEWithLogitsLoss() def load_optimizer(self): if self.arg.optimizer == 'SGD': self.optimizer = optim.SGD( self.model.parameters(), lr=self.arg.base_lr, momentum=0.9, nesterov=self.arg.nesterov, weight_decay=self.arg.weight_decay) elif self.arg.optimizer == 'Adam': self.optimizer = optim.Adam( self.model.parameters(), lr=self.arg.base_lr, weight_decay=self.arg.weight_decay) else: raise ValueError() def adjust_lr(self): if self.arg.optimizer == 'SGD' and self.arg.step: lr = self.arg.base_lr * ( 0.1**np.sum(self.meta_info['epoch'] >= np.array(self.arg.step))) for param_group in self.optimizer.param_groups: param_group['lr'] = lr self.lr = lr else: self.lr = self.arg.base_lr def show_topk(self, k): rank = self.result.argsort() hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)] accuracy = sum(hit_top_k) * 1.0 / len(hit_top_k) self.io.print_log('\tTop{}: {:.2f}%'.format(k, 100 * accuracy)) def train(self): self.model.train() self.adjust_lr() loader = self.data_loader['train'] loss_value = [] result_frag = [] label_frag = [] # print("train") for data, label in loader: # get data data = data.float().to(self.dev) label = label.long().to(self.dev) # forward output = self.model(data) result_frag.extend( output.data.cpu().numpy().argmax(axis=1)) label_frag.extend(label.data.cpu().numpy()) # print(output) loss = self.loss(output, label) # print(label) # backward self.optimizer.zero_grad() loss.backward() self.optimizer.step() # statistics self.iter_info['loss'] = loss.data.item() self.iter_info['lr'] = '{:.6f}'.format(self.lr) loss_value.append(self.iter_info['loss']) self.show_iter_info() self.meta_info['iter'] += 1 ac = accuracy_score(label_frag, result_frag) # print(result_frag) # print(label_frag) print("train acc: {}".format(ac)) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() # self.io.print_timer() def test(self, evaluation=True): self.model.eval() loader = self.data_loader['test'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: # get data data = data.float().to(self.dev) label = label.long().to(self.dev) # inference with torch.no_grad(): output = self.model(data) result_frag.append(output.data.cpu().numpy()) # get loss if evaluation: loss = self.loss(output, label) loss_value.append(loss.item()) label_frag.append(label.data.cpu().numpy()) self.result = np.concatenate(result_frag) # print(self.result) if evaluation: self.label = np.concatenate(label_frag) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() # show top-k accuracy for k in self.arg.show_topk: self.show_topk(k) top = self.result.argmax(axis=1) print(top) print(self.label) cm = confusion_matrix(self.label, top) print(cm) @staticmethod def get_parser(add_help=False): # parameter priority: command line > config > default parent_parser = Processor.get_parser(add_help=False) parser = argparse.ArgumentParser( add_help=add_help, parents=[parent_parser], description='Spatial Temporal Graph Convolution Network') # region arguments yapf: disable # evaluation parser.add_argument('--show_topk', type=int, default=[1], nargs='+', help='which Top K accuracy will be shown') # optim parser.add_argument('--base_lr', type=float, default=0.01, help='initial learning rate') parser.add_argument('--step', type=int, default=[], nargs='+', help='the epoch where optimizer reduce the learning rate') parser.add_argument('--optimizer', default='SGD', help='type of optimizer') parser.add_argument('--nesterov', type=str2bool, default=True, help='use nesterov or not') parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimizer') # endregion yapf: enable return parser

32.482234

94

0.556493

import sys import argparse import yaml import numpy as np import torch import torch.nn as nn import torch.optim as optim import torchlight from torchlight import str2bool from torchlight import DictAction from torchlight import import_class from .processor import Processor from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv1d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('Conv2d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) class REC_Processor(Processor): def load_model(self): self.model = self.io.load_model(self.arg.model, **(self.arg.model_args)) self.model.apply(weights_init) self.loss = nn.CrossEntropyLoss() def load_optimizer(self): if self.arg.optimizer == 'SGD': self.optimizer = optim.SGD( self.model.parameters(), lr=self.arg.base_lr, momentum=0.9, nesterov=self.arg.nesterov, weight_decay=self.arg.weight_decay) elif self.arg.optimizer == 'Adam': self.optimizer = optim.Adam( self.model.parameters(), lr=self.arg.base_lr, weight_decay=self.arg.weight_decay) else: raise ValueError() def adjust_lr(self): if self.arg.optimizer == 'SGD' and self.arg.step: lr = self.arg.base_lr * ( 0.1**np.sum(self.meta_info['epoch'] >= np.array(self.arg.step))) for param_group in self.optimizer.param_groups: param_group['lr'] = lr self.lr = lr else: self.lr = self.arg.base_lr def show_topk(self, k): rank = self.result.argsort() hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)] accuracy = sum(hit_top_k) * 1.0 / len(hit_top_k) self.io.print_log('\tTop{}: {:.2f}%'.format(k, 100 * accuracy)) def train(self): self.model.train() self.adjust_lr() loader = self.data_loader['train'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: data = data.float().to(self.dev) label = label.long().to(self.dev) output = self.model(data) result_frag.extend( output.data.cpu().numpy().argmax(axis=1)) label_frag.extend(label.data.cpu().numpy()) loss = self.loss(output, label) self.optimizer.zero_grad() loss.backward() self.optimizer.step() self.iter_info['loss'] = loss.data.item() self.iter_info['lr'] = '{:.6f}'.format(self.lr) loss_value.append(self.iter_info['loss']) self.show_iter_info() self.meta_info['iter'] += 1 ac = accuracy_score(label_frag, result_frag) print("train acc: {}".format(ac)) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() def test(self, evaluation=True): self.model.eval() loader = self.data_loader['test'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: data = data.float().to(self.dev) label = label.long().to(self.dev) with torch.no_grad(): output = self.model(data) result_frag.append(output.data.cpu().numpy()) if evaluation: loss = self.loss(output, label) loss_value.append(loss.item()) label_frag.append(label.data.cpu().numpy()) self.result = np.concatenate(result_frag) if evaluation: self.label = np.concatenate(label_frag) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() for k in self.arg.show_topk: self.show_topk(k) top = self.result.argmax(axis=1) print(top) print(self.label) cm = confusion_matrix(self.label, top) print(cm) @staticmethod def get_parser(add_help=False): parent_parser = Processor.get_parser(add_help=False) parser = argparse.ArgumentParser( add_help=add_help, parents=[parent_parser], description='Spatial Temporal Graph Convolution Network') parser.add_argument('--show_topk', type=int, default=[1], nargs='+', help='which Top K accuracy will be shown') parser.add_argument('--base_lr', type=float, default=0.01, help='initial learning rate') parser.add_argument('--step', type=int, default=[], nargs='+', help='the epoch where optimizer reduce the learning rate') parser.add_argument('--optimizer', default='SGD', help='type of optimizer') parser.add_argument('--nesterov', type=str2bool, default=True, help='use nesterov or not') parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimizer') return parser

true

f71fedf23526603a8b5b482439d51773bdec5bd3

3,347

py

Python

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

# Generated by Django 3.2.10 on 2022-01-25 22:13 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('forename', models.CharField(max_length=100)), ('surname', models.CharField(max_length=100)), ('email', models.EmailField(blank=True, max_length=254, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Journal', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Organization', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Paper', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pdf', models.FileField(null=True, unique=True, upload_to='pdfs/%Y/%m/%d/')), ('tei', models.FileField(null=True, unique=True, upload_to='xmls/%Y/%m/%d/')), ('title', models.TextField(blank=True, null=True, unique=True)), ('abstract', models.TextField(blank=True, null=True, unique=True)), ('doi', models.CharField(blank=True, max_length=100, null=True, unique=True)), ('url', models.URLField(blank=True, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('authors', models.ManyToManyField(to='medseer.Author')), ('journal', models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.journal')), ], ), migrations.AddField( model_name='author', name='organization', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.organization'), ), migrations.AddConstraint( model_name='author', constraint=models.UniqueConstraint(fields=('forename', 'surname'), name='unique_author_name'), ), ]

46.486111

125

0.582611

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('forename', models.CharField(max_length=100)), ('surname', models.CharField(max_length=100)), ('email', models.EmailField(blank=True, max_length=254, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Journal', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Organization', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Paper', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pdf', models.FileField(null=True, unique=True, upload_to='pdfs/%Y/%m/%d/')), ('tei', models.FileField(null=True, unique=True, upload_to='xmls/%Y/%m/%d/')), ('title', models.TextField(blank=True, null=True, unique=True)), ('abstract', models.TextField(blank=True, null=True, unique=True)), ('doi', models.CharField(blank=True, max_length=100, null=True, unique=True)), ('url', models.URLField(blank=True, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('authors', models.ManyToManyField(to='medseer.Author')), ('journal', models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.journal')), ], ), migrations.AddField( model_name='author', name='organization', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.organization'), ), migrations.AddConstraint( model_name='author', constraint=models.UniqueConstraint(fields=('forename', 'surname'), name='unique_author_name'), ), ]

true

f71fee147634858badedf4ea69e3f4bc26bb7e78

2,052

py

Python

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for the iPod plist plugin.""" from __future__ import unicode_literals import unittest from plaso.formatters import ipod as _ # pylint: disable=unused-import from plaso.lib import definitions from plaso.parsers.plist_plugins import ipod from tests import test_lib as shared_test_lib from tests.parsers.plist_plugins import test_lib class TestIPodPlugin(test_lib.PlistPluginTestCase): """Tests for the iPod plist plugin.""" @shared_test_lib.skipUnlessHasTestFile(['com.apple.iPod.plist']) def testProcess(self): """Tests the Process function.""" plist_name = 'com.apple.iPod.plist' plugin = ipod.IPodPlugin() storage_writer = self._ParsePlistFileWithPlugin( plugin, [plist_name], plist_name) self.assertEqual(storage_writer.number_of_warnings, 0) self.assertEqual(storage_writer.number_of_events, 4) # The order in which PlistParser generates events is nondeterministic # hence we sort the events. events = list(storage_writer.GetSortedEvents()) event = events[0] self.CheckTimestamp(event.timestamp, '1995-11-22 18:25:07.000000') self.assertEqual(event.device_id, '0000A11300000000') event = events[2] self.CheckTimestamp(event.timestamp, '2013-10-09 19:27:54.000000') expected_message = ( 'Device ID: 4C6F6F6E65000000 ' 'Type: iPhone [10016] ' 'Connected 1 times ' 'Serial nr: 526F676572 ' 'IMEI [012345678901234]') expected_short_message = '{0:s}...'.format(expected_message[:77]) self._TestGetMessageStrings(event, expected_message, expected_short_message) self.assertEqual( event.timestamp_desc, definitions.TIME_DESCRIPTION_LAST_CONNECTED) self.assertEqual(event.device_class, 'iPhone') self.assertEqual(event.device_id, '4C6F6F6E65000000') self.assertEqual(event.firmware_version, 256) self.assertEqual(event.imei, '012345678901234') self.assertEqual(event.use_count, 1) if __name__ == '__main__': unittest.main()

30.176471

80

0.729532

from __future__ import unicode_literals import unittest from plaso.formatters import ipod as _ from plaso.lib import definitions from plaso.parsers.plist_plugins import ipod from tests import test_lib as shared_test_lib from tests.parsers.plist_plugins import test_lib class TestIPodPlugin(test_lib.PlistPluginTestCase): @shared_test_lib.skipUnlessHasTestFile(['com.apple.iPod.plist']) def testProcess(self): plist_name = 'com.apple.iPod.plist' plugin = ipod.IPodPlugin() storage_writer = self._ParsePlistFileWithPlugin( plugin, [plist_name], plist_name) self.assertEqual(storage_writer.number_of_warnings, 0) self.assertEqual(storage_writer.number_of_events, 4) events = list(storage_writer.GetSortedEvents()) event = events[0] self.CheckTimestamp(event.timestamp, '1995-11-22 18:25:07.000000') self.assertEqual(event.device_id, '0000A11300000000') event = events[2] self.CheckTimestamp(event.timestamp, '2013-10-09 19:27:54.000000') expected_message = ( 'Device ID: 4C6F6F6E65000000 ' 'Type: iPhone [10016] ' 'Connected 1 times ' 'Serial nr: 526F676572 ' 'IMEI [012345678901234]') expected_short_message = '{0:s}...'.format(expected_message[:77]) self._TestGetMessageStrings(event, expected_message, expected_short_message) self.assertEqual( event.timestamp_desc, definitions.TIME_DESCRIPTION_LAST_CONNECTED) self.assertEqual(event.device_class, 'iPhone') self.assertEqual(event.device_id, '4C6F6F6E65000000') self.assertEqual(event.firmware_version, 256) self.assertEqual(event.imei, '012345678901234') self.assertEqual(event.use_count, 1) if __name__ == '__main__': unittest.main()

true

f71fee22e27eb7d42dc3efe0a61407b797d283a4

63,809

py

Python

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

""" Provide the groupby split-apply-combine paradigm. Define the GroupBy class providing the base-class of operations. The SeriesGroupBy and DataFrameGroupBy sub-class (defined in pandas.core.groupby.generic) expose these user-facing objects to provide specific functionailty. """ import types from functools import wraps, partial import datetime import collections import warnings from contextlib import contextmanager import numpy as np from pandas._libs import groupby as libgroupby, Timestamp from pandas.util._validators import validate_kwargs from pandas.util._decorators import ( cache_readonly, Substitution, Appender) from pandas import compat from pandas.compat import zip, range, callable, set_function_name from pandas.compat.numpy import function as nv from pandas.core.dtypes.common import ( is_numeric_dtype, is_scalar, ensure_float) from pandas.core.dtypes.cast import maybe_downcast_to_dtype from pandas.core.dtypes.missing import isna, notna from pandas.core.groupby import base from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.index import Index, MultiIndex from pandas.core.generic import NDFrame from pandas.core.frame import DataFrame from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter import pandas.core.common as com import pandas.core.algorithms as algorithms from pandas.core.config import option_context _doc_template = """ See also -------- pandas.Series.%(name)s pandas.DataFrame.%(name)s pandas.Panel.%(name)s """ _apply_docs = dict( template=""" Apply function `func` group-wise and combine the results together. The function passed to `apply` must take a {input} as its first argument and return a DataFrame, Series or scalar. `apply` will then take care of combining the results back together into a single dataframe or series. `apply` is therefore a highly flexible grouping method. While `apply` is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like `agg` or `transform`. Pandas offers a wide range of method that will be much faster than using `apply` for their specific purposes, so try to use them before reaching for `apply`. Parameters ---------- func : callable A callable that takes a {input} as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to `func`. Returns ------- applied : Series or DataFrame Notes ----- In the current implementation `apply` calls `func` twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if `func` has side-effects, as they will take effect twice for the first group. Examples -------- {examples} See also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. """, dataframe_examples=""" >>> df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1,2,3], 'C': [4,6, 5]}) >>> g = df.groupby('A') Notice that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function passed to `apply` takes a DataFrame as its argument and returns a DataFrame. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x.max() - x.min()) B C A a 1 2 b 0 0 Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 """, series_examples=""" >>> s = pd.Series([0, 1, 2], index='a a b'.split()) >>> g = s.groupby(s.index) From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: The function passed to `apply` takes a Series as its argument and returns a Series. `apply` combines the result for each group together into a new Series: >>> g.apply(lambda x: x*2 if x.name == 'b' else x/2) 0 0.0 1 0.5 2 4.0 dtype: float64 Example 2: The function passed to `apply` takes a Series as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.max() - x.min()) a 1 b 0 dtype: int64 """) _pipe_template = """\ Apply a function `func` with arguments to this %(klass)s object and return the function's result. %(versionadded)s Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) which is much more readable. Parameters ---------- func : callable or tuple of (callable, string) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. args : iterable, optional positional arguments passed into `func`. kwargs : dict, optional a dictionary of keyword arguments passed into `func`. Returns ------- object : the return type of `func`. Notes ----- See more `here <http://pandas.pydata.org/pandas-docs/stable/groupby.html#piping-function-calls>`_ Examples -------- %(examples)s See Also -------- pandas.Series.pipe : Apply a function with arguments to a series pandas.DataFrame.pipe: Apply a function with arguments to a dataframe apply : Apply function to each group instead of to the full %(klass)s object. """ _transform_template = """ Call function producing a like-indexed %(klass)s on each group and return a %(klass)s having the same indexes as the original object filled with the transformed values Parameters ---------- f : function Function to apply to each group Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, f returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. Returns ------- %(klass)s See also -------- aggregate, transform Examples -------- # Same shape >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000 # Broadcastable >>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0 """ class GroupByPlot(PandasObject): """ Class implementing the .plot attribute for groupby objects """ def __init__(self, groupby): self._groupby = groupby def __call__(self, *args, **kwargs): def f(self): return self.plot(*args, **kwargs) f.__name__ = 'plot' return self._groupby.apply(f) def __getattr__(self, name): def attr(*args, **kwargs): def f(self): return getattr(self.plot, name)(*args, **kwargs) return self._groupby.apply(f) return attr @contextmanager def _group_selection_context(groupby): """ set / reset the _group_selection_context """ groupby._set_group_selection() yield groupby groupby._reset_group_selection() class _GroupBy(PandasObject, SelectionMixin): _group_selection = None _apply_whitelist = frozenset([]) def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): self._selection = selection if isinstance(obj, NDFrame): obj._consolidate_inplace() self.level = level if not as_index: if not isinstance(obj, DataFrame): raise TypeError('as_index=False only valid with DataFrame') if axis != 0: raise ValueError('as_index=False only valid for axis=0') self.as_index = as_index self.keys = keys self.sort = sort self.group_keys = group_keys self.squeeze = squeeze self.observed = observed self.mutated = kwargs.pop('mutated', False) if grouper is None: from pandas.core.groupby.grouper import _get_grouper grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, level=level, sort=sort, observed=observed, mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() # we accept no other args validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) def __unicode__(self): # TODO: Better unicode/repr for GroupBy object return object.__repr__(self) def _assure_grouper(self): """ we create the grouper on instantiation sub-classes may have a different policy """ pass @property def groups(self): """ dict {group name -> group labels} """ self._assure_grouper() return self.grouper.groups @property def ngroups(self): self._assure_grouper() return self.grouper.ngroups @property def indices(self): """ dict {group name -> group indices} """ self._assure_grouper() return self.grouper.indices def _get_indices(self, names): """ safe get multiple indices, translate keys for datelike to underlying repr """ def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isinstance(s, (Timestamp, datetime.datetime)): return lambda key: Timestamp(key) elif isinstance(s, np.datetime64): return lambda key: Timestamp(key).asm8 else: return lambda key: key if len(names) == 0: return [] if len(self.indices) > 0: index_sample = next(iter(self.indices)) else: index_sample = None # Dummy sample name_sample = names[0] if isinstance(index_sample, tuple): if not isinstance(name_sample, tuple): msg = ("must supply a tuple to get_group with multiple" " grouping keys") raise ValueError(msg) if not len(name_sample) == len(index_sample): try: # If the original grouper was a tuple return [self.indices[name] for name in names] except KeyError: # turns out it wasn't a tuple msg = ("must supply a a same-length tuple to get_group" " with multiple grouping keys") raise ValueError(msg) converters = [get_converter(s) for s in index_sample] names = [tuple(f(n) for f, n in zip(converters, name)) for name in names] else: converter = get_converter(index_sample) names = [converter(name) for name in names] return [self.indices.get(name, []) for name in names] def _get_index(self, name): """ safe get index, translate keys for datelike to underlying repr """ return self._get_indices([name])[0] @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, Series): if self._group_selection is not None: return self.obj[self._group_selection] return self.obj else: return self.obj[self._selection] def _reset_group_selection(self): """ Clear group based selection. Used for methods needing to return info on each group regardless of whether a group selection was previously set. """ if self._group_selection is not None: # GH12839 clear cached selection too when changing group selection self._group_selection = None self._reset_cache('_selected_obj') def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection """ grp = self.grouper if not (self.as_index and getattr(grp, 'groupings', None) is not None and self.obj.ndim > 1 and self._group_selection is None): return ax = self.obj._info_axis groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis] if len(groupers): # GH12839 clear selected obj cache when group selection changes self._group_selection = ax.difference(Index(groupers)).tolist() self._reset_cache('_selected_obj') def _set_result_index_ordered(self, result): # set the result index on the passed values object and # return the new object, xref 8046 # the values/counts are repeated according to the group index # shortcut if we have an already ordered grouper if not self.grouper.is_monotonic: index = Index(np.concatenate( self._get_indices(self.grouper.result_index))) result.set_axis(index, axis=self.axis, inplace=True) result = result.sort_index(axis=self.axis) result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True) return result def _dir_additions(self): return self.obj._dir_additions() | self._apply_whitelist def __getattr__(self, attr): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] if hasattr(self.obj, attr): return self._make_wrapper(attr) raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) @Substitution(klass='GroupBy', versionadded='.. versionadded:: 0.21.0', examples="""\ >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby('A').pipe(lambda x: x.max() - x.min()) B A a 2 b 2""") @Appender(_pipe_template) def pipe(self, func, *args, **kwargs): return com._pipe(self, func, *args, **kwargs) plot = property(GroupByPlot) def _make_wrapper(self, name): if name not in self._apply_whitelist: is_callable = callable(getattr(self._selected_obj, name, None)) kind = ' callable ' if is_callable else ' ' msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try " "using the 'apply' method".format(kind, name, type(self).__name__)) raise AttributeError(msg) self._set_group_selection() # need to setup the selection # as are not passed directly but in the grouper f = getattr(self._selected_obj, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) f = getattr(type(self._selected_obj), name) def wrapper(*args, **kwargs): # a little trickery for aggregation functions that need an axis # argument kwargs_with_axis = kwargs.copy() if 'axis' not in kwargs_with_axis or \ kwargs_with_axis['axis'] is None: kwargs_with_axis['axis'] = self.axis def curried_with_axis(x): return f(x, *args, **kwargs_with_axis) def curried(x): return f(x, *args, **kwargs) # preserve the name so we can detect it when calling plot methods, # to avoid duplicates curried.__name__ = curried_with_axis.__name__ = name # special case otherwise extra plots are created when catching the # exception below if name in base.plotting_methods: return self.apply(curried) try: return self.apply(curried_with_axis) except Exception: try: return self.apply(curried) except Exception: # related to : GH3688 # try item-by-item # this can be called recursively, so need to raise # ValueError # if we don't have this method to indicated to aggregate to # mark this column as an error try: return self._aggregate_item_by_item(name, *args, **kwargs) except (AttributeError): raise ValueError return wrapper def get_group(self, name, obj=None): """ Constructs NDFrame from group with provided name Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used Returns ------- group : same type as obj """ if obj is None: obj = self._selected_obj inds = self._get_index(name) if not len(inds): raise KeyError(name) return obj._take(inds, axis=self.axis) def __iter__(self): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ return self.grouper.get_iterator(self.obj, axis=self.axis) @Appender(_apply_docs['template'] .format(input="dataframe", examples=_apply_docs['dataframe_examples'])) def apply(self, func, *args, **kwargs): func = self._is_builtin_func(func) # this is needed so we don't try and wrap strings. If we could # resolve functions to their callable functions prior, this # wouldn't be needed if args or kwargs: if callable(func): @wraps(func) def f(g): with np.errstate(all='ignore'): return func(g, *args, **kwargs) else: raise ValueError('func must be a callable if args or ' 'kwargs are supplied') else: f = func # ignore SettingWithCopy here in case the user mutates with option_context('mode.chained_assignment', None): try: result = self._python_apply_general(f) except Exception: # gh-20949 # try again, with .apply acting as a filtering # operation, by excluding the grouping column # This would normally not be triggered # except if the udf is trying an operation that # fails on *some* columns, e.g. a numeric operation # on a string grouper column with _group_selection_context(self): return self._python_apply_general(f) return result def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) return self._wrap_applied_output( keys, values, not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self._selection_name, self._selected_obj def transform(self, func, *args, **kwargs): raise com.AbstractMethodError(self) def _cumcount_array(self, ascending=True): """ Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general """ ids, _, ngroups = self.grouper.group_info sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: return np.empty(0, dtype=np.int64) run = np.r_[True, ids[:-1] != ids[1:]] rep = np.diff(np.r_[np.nonzero(run)[0], count]) out = (~run).cumsum() if ascending: out -= np.repeat(out[run], rep) else: out = np.repeat(out[np.r_[run[1:], True]], rep) - out rev = np.empty(count, dtype=np.intp) rev[sorter] = np.arange(count, dtype=np.intp) return out[rev].astype(np.int64, copy=False) def _try_cast(self, result, obj, numeric_only=False): """ try to cast the result to our obj original type, we may have roundtripped thru object in the mean-time if numeric_only is True, then only try to cast numerics and not datetimelikes """ if obj.ndim > 1: dtype = obj.values.dtype else: dtype = obj.dtype if not is_scalar(result): if numeric_only and is_numeric_dtype(dtype) or not numeric_only: result = maybe_downcast_to_dtype(result, dtype) return result def _transform_should_cast(self, func_nm): """ Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation """ return (self.size().fillna(0) > 0).any() and ( func_nm not in base.cython_cast_blacklist) def _cython_transform(self, how, numeric_only=True, **kwargs): output = collections.OrderedDict() for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.transform(obj.values, how, **kwargs) except NotImplementedError: continue except AssertionError as e: raise GroupByError(str(e)) if self._transform_should_cast(how): output[name] = self._try_cast(result, obj) else: output[name] = result if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_transformed_output(output, names) def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1): output = {} for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.aggregate(obj.values, how, min_count=min_count) except AssertionError as e: raise GroupByError(str(e)) output[name] = self._try_cast(result, obj) if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_aggregated_output(output, names) def _python_agg_general(self, func, *args, **kwargs): func = self._is_builtin_func(func) f = lambda x: func(x, *args, **kwargs) # iterate through "columns" ex exclusions to populate output dict output = {} for name, obj in self._iterate_slices(): try: result, counts = self.grouper.agg_series(obj, f) output[name] = self._try_cast(result, obj, numeric_only=True) except TypeError: continue if len(output) == 0: return self._python_apply_general(f) if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 for name, result in compat.iteritems(output): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): values = ensure_float(values) output[name] = self._try_cast(values[mask], result) return self._wrap_aggregated_output(output) def _wrap_applied_output(self, *args, **kwargs): raise com.AbstractMethodError(self) def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.core.reshape.concat import concat def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in com._not_none(*values): ax = v._get_axis(self.axis) ax._reset_identity() return values if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) if isinstance(result, Series): result = result.reindex(ax) else: # this is a very unfortunate situation # we have a multi-index that is NOT lexsorted # and we have a result which is duplicated # we can't reindex, so we resort to this # GH 14776 if isinstance(ax, MultiIndex) and not ax.is_unique: indexer = algorithms.unique1d( result.index.get_indexer_for(ax.values)) result = result.take(indexer, axis=self.axis) else: result = result.reindex(ax, axis=self.axis) elif self.group_keys: values = reset_identity(values) if self.as_index: # possible MI return case group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names, sort=False) else: # GH5610, returns a MI, with the first level being a # range index keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: values = reset_identity(values) result = concat(values, axis=self.axis) if (isinstance(result, Series) and getattr(self, '_selection_name', None) is not None): result.name = self._selection_name return result def _apply_filter(self, indices, dropna): if len(indices) == 0: indices = np.array([], dtype='int64') else: indices = np.sort(np.concatenate(indices)) if dropna: filtered = self._selected_obj.take(indices, axis=self.axis) else: mask = np.empty(len(self._selected_obj.index), dtype=bool) mask.fill(False) mask[indices.astype(int)] = True # mask fails to broadcast when passed to where; broadcast manually. mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T filtered = self._selected_obj.where(mask) # Fill with NaNs. return filtered class GroupBy(_GroupBy): """ Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas object axis : int, default 0 level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : string Most users should ignore this Notes ----- After grouping, see aggregate, apply, and transform functions. Here are some other brief notes about usage. When grouping by multiple groups, the result index will be a MultiIndex (hierarchical) by default. Iteration produces (key, group) tuples, i.e. chunking the data by group. So you can write code like: :: grouped = obj.groupby(keys, axis=axis) for key, group in grouped: # do something with the data Function calls on GroupBy, if not specially implemented, "dispatch" to the grouped data. So if you group a DataFrame and wish to invoke the std() method on each group, you can simply do: :: df.groupby(mapper).std() rather than :: df.groupby(mapper).aggregate(np.std) You can pass arguments to these "wrapped" functions, too. See the online documentation for full exposition on these topics and much more Returns ------- **Attributes** groups : dict {group name -> group labels} len(grouped) : int Number of groups """ def _bool_agg(self, val_test, skipna): """Shared func to call any / all Cython GroupBy implementations""" def objs_to_bool(vals): try: vals = vals.astype(np.bool) except ValueError: # for objects vals = np.array([bool(x) for x in vals]) return vals.view(np.uint8) def result_to_bool(result): return result.astype(np.bool, copy=False) return self._get_cythonized_result('group_any_all', self.grouper, aggregate=True, cython_dtype=np.uint8, needs_values=True, needs_mask=True, pre_processing=objs_to_bool, post_processing=result_to_bool, val_test=val_test, skipna=skipna) @Substitution(name='groupby') @Appender(_doc_template) def any(self, skipna=True): """ Returns True if any value in the group is truthful, else False Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('any', skipna) @Substitution(name='groupby') @Appender(_doc_template) def all(self, skipna=True): """Returns True if all values in the group are truthful, else False Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('all', skipna) @Substitution(name='groupby') @Appender(_doc_template) def count(self): """Compute count of group, excluding missing values""" # defined here for API doc raise NotImplementedError @Substitution(name='groupby') @Appender(_doc_template) def mean(self, *args, **kwargs): """ Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C']) Groupby one column and return the mean of the remaining columns in each group. >>> df.groupby('A').mean() >>> B C A 1 3.0 1.333333 2 4.0 1.500000 Groupby two columns and return the mean of the remaining column. >>> df.groupby(['A', 'B']).mean() >>> C A B 1 2.0 2 4.0 1 2 3.0 1 5.0 2 Groupby one column and return the mean of only particular column in the group. >>> df.groupby('A')['B'].mean() >>> A 1 3.0 2 4.0 Name: B, dtype: float64 """ nv.validate_groupby_func('mean', args, kwargs, ['numeric_only']) try: return self._cython_agg_general('mean', **kwargs) except GroupByError: raise except Exception: # pragma: no cover with _group_selection_context(self): f = lambda x: x.mean(axis=self.axis, **kwargs) return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def median(self, **kwargs): """ Compute median of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex """ try: return self._cython_agg_general('median', **kwargs) except GroupByError: raise except Exception: # pragma: no cover def f(x): if isinstance(x, np.ndarray): x = Series(x) return x.median(axis=self.axis, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def std(self, ddof=1, *args, **kwargs): """ Compute standard deviation of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ # TODO: implement at Cython level? nv.validate_groupby_func('std', args, kwargs) return np.sqrt(self.var(ddof=ddof, **kwargs)) @Substitution(name='groupby') @Appender(_doc_template) def var(self, ddof=1, *args, **kwargs): """ Compute variance of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validate_groupby_func('var', args, kwargs) if ddof == 1: try: return self._cython_agg_general('var', **kwargs) except Exception: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) else: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def sem(self, ddof=1): """ Compute standard error of the mean of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ return self.std(ddof=ddof) / np.sqrt(self.count()) @Substitution(name='groupby') @Appender(_doc_template) def size(self): """Compute group sizes""" result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result @classmethod def _add_numeric_operations(cls): """ add numeric operations to the GroupBy generically """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)s of group values" @Substitution(name='groupby', f=name) @Appender(_doc_template) @Appender(_local_template) def f(self, **kwargs): if 'numeric_only' not in kwargs: kwargs['numeric_only'] = numeric_only if 'min_count' not in kwargs: kwargs['min_count'] = min_count self._set_group_selection() try: return self._cython_agg_general( alias, alt=npfunc, **kwargs) except AssertionError as e: raise SpecificationError(str(e)) except Exception: result = self.aggregate( lambda x: npfunc(x, axis=self.axis)) if _convert: result = result._convert(datetime=True) return result set_function_name(f, name, cls) return f def first_compat(x, axis=0): def first(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[0] if isinstance(x, DataFrame): return x.apply(first, axis=axis) else: return first(x) def last_compat(x, axis=0): def last(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[-1] if isinstance(x, DataFrame): return x.apply(last, axis=axis) else: return last(x) cls.sum = groupby_function('sum', 'add', np.sum, min_count=0) cls.prod = groupby_function('prod', 'prod', np.prod, min_count=0) cls.min = groupby_function('min', 'min', np.min, numeric_only=False) cls.max = groupby_function('max', 'max', np.max, numeric_only=False) cls.first = groupby_function('first', 'first', first_compat, numeric_only=False) cls.last = groupby_function('last', 'last', last_compat, numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def ohlc(self): """ Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex """ return self._apply_to_column_groupbys( lambda x: x._cython_agg_general('ohlc')) @Appender(DataFrame.describe.__doc__) def describe(self, **kwargs): with _group_selection_context(self): result = self.apply(lambda x: x.describe(**kwargs)) if self.axis == 1: return result.T return result.unstack() @Substitution(name='groupby') @Appender(_doc_template) def resample(self, rule, *args, **kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ from pandas.core.resample import get_resampler_for_grouping return get_resampler_for_grouping(self, rule, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def rolling(self, *args, **kwargs): """ Return a rolling grouper, providing rolling functionality per group """ from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def expanding(self, *args, **kwargs): """ Return an expanding grouper, providing expanding functionality per group """ from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, *args, **kwargs) def _fill(self, direction, limit=None): """Shared function for `pad` and `backfill` to call Cython method Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to a forward fill limit : int, default None Maximum number of consecutive values to fill. If `None`, this method will convert to -1 prior to passing to Cython Returns ------- `Series` or `DataFrame` with filled values See Also -------- pad backfill """ # Need int value for Cython if limit is None: limit = -1 return self._get_cythonized_result('group_fillna_indexer', self.grouper, needs_mask=True, cython_dtype=np.int64, result_is_index=True, direction=direction, limit=limit) @Substitution(name='groupby') def pad(self, limit=None): """ Forward fill the values Parameters ---------- limit : integer, optional limit of how many values to fill See Also -------- Series.pad DataFrame.pad Series.fillna DataFrame.fillna """ return self._fill('ffill', limit=limit) ffill = pad @Substitution(name='groupby') def backfill(self, limit=None): """ Backward fill the values Parameters ---------- limit : integer, optional limit of how many values to fill See Also -------- Series.backfill DataFrame.backfill Series.fillna DataFrame.fillna """ return self._fill('bfill', limit=limit) bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) def nth(self, n, dropna=None): """ Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby. Parameters ---------- n : int or list of ints a single nth value for the row or a list of nth values dropna : None or str, optional apply the specified dropna operation before counting which row is the nth row. Needs to be None, 'any' or 'all' Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B']) >>> g = df.groupby('A') >>> g.nth(0) B A 1 NaN 2 3.0 >>> g.nth(1) B A 1 2.0 2 5.0 >>> g.nth(-1) B A 1 4.0 2 5.0 >>> g.nth([0, 1]) B A 1 NaN 1 2.0 2 3.0 2 5.0 Specifying `dropna` allows count ignoring ``NaN`` >>> g.nth(0, dropna='any') B A 1 2.0 2 3.0 NaNs denote group exhausted when using dropna >>> g.nth(3, dropna='any') B A 1 NaN 2 NaN Specifying `as_index=False` in `groupby` keeps the original index. >>> df.groupby('A', as_index=False).nth(1) A B 1 1 2.0 4 2 5.0 """ if isinstance(n, int): nth_values = [n] elif isinstance(n, (set, list, tuple)): nth_values = list(set(n)) if dropna is not None: raise ValueError( "dropna option with a list of nth values is not supported") else: raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) self._set_group_selection() if not dropna: mask = np.in1d(self._cumcount_array(), nth_values) | \ np.in1d(self._cumcount_array(ascending=False) + 1, -nth_values) out = self._selected_obj[mask] if not self.as_index: return out ids, _, _ = self.grouper.group_info out.index = self.grouper.result_index[ids[mask]] return out.sort_index() if self.sort else out if dropna not in ['any', 'all']: if isinstance(self._selected_obj, Series) and dropna is True: warnings.warn("the dropna={dropna} keyword is deprecated," "use dropna='all' instead. " "For a Series groupby, dropna must be " "either None, 'any' or 'all'.".format( dropna=dropna), FutureWarning, stacklevel=2) dropna = 'all' else: # Note: when agg-ing picker doesn't raise this, # just returns NaN raise ValueError("For a DataFrame groupby, dropna must be " "either None, 'any' or 'all', " "(was passed %s)." % (dropna),) # old behaviour, but with all and any support for DataFrames. # modified in GH 7559 to have better perf max_len = n if n >= 0 else - 1 - n dropped = self.obj.dropna(how=dropna, axis=self.axis) # get a new grouper for our dropped obj if self.keys is None and self.level is None: # we don't have the grouper info available # (e.g. we have selected out # a column that is not in the current object) axis = self.grouper.axis grouper = axis[axis.isin(dropped.index)] else: # create a grouper with the original parameters, but on the dropped # object from pandas.core.groupby.grouper import _get_grouper grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, sort=self.sort, mutated=self.mutated) grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) sizes, result = grb.size(), grb.nth(n) mask = (sizes < max_len).values # set the results which don't meet the criteria if len(result) and mask.any(): result.loc[mask] = np.nan # reset/reindex to the original groups if len(self.obj) == len(dropped) or \ len(result) == len(self.grouper.result_index): result.index = self.grouper.result_index else: result = result.reindex(self.grouper.result_index) return result @Substitution(name='groupby') def ngroup(self, ascending=True): """ Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. .. versionadded:: 0.20.2 Parameters ---------- ascending : bool, default True If False, number in reverse, from number of group - 1 to 0. Examples -------- >>> df = pd.DataFrame({"A": list("aaabba")}) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').ngroup() 0 0 1 0 2 0 3 1 4 1 5 0 dtype: int64 >>> df.groupby('A').ngroup(ascending=False) 0 1 1 1 2 1 3 0 4 0 5 1 dtype: int64 >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup() 0 0 1 0 2 1 3 3 4 2 5 0 dtype: int64 See also -------- .cumcount : Number the rows in each group. """ with _group_selection_context(self): index = self._selected_obj.index result = Series(self.grouper.group_info[0], index) if not ascending: result = self.ngroups - 1 - result return result @Substitution(name='groupby') def cumcount(self, ascending=True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Examples -------- >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], ... columns=['A']) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').cumcount() 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 >>> df.groupby('A').cumcount(ascending=False) 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 See also -------- .ngroup : Number the groups themselves. """ with _group_selection_context(self): index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) return Series(cumcounts, index) @Substitution(name='groupby') @Appender(_doc_template) def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): """ Provides the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group * min: lowest rank in group * max: highest rank in group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups ascending : boolean, default True False for ranks by high (1) to low (N) na_option : {'keep', 'top', 'bottom'}, default 'keep' * keep: leave NA values where they are * top: smallest rank if ascending * bottom: smallest rank if descending pct : boolean, default False Compute percentage rank of data within each group axis : int, default 0 The axis of the object over which to compute the rank. Returns ----- DataFrame with ranking of values within each group """ if na_option not in {'keep', 'top', 'bottom'}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) return self._cython_transform('rank', numeric_only=False, ties_method=method, ascending=ascending, na_option=na_option, pct=pct, axis=axis) @Substitution(name='groupby') @Appender(_doc_template) def cumprod(self, axis=0, *args, **kwargs): """Cumulative product for each group""" nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, **kwargs)) return self._cython_transform('cumprod', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cumsum(self, axis=0, *args, **kwargs): """Cumulative sum for each group""" nv.validate_groupby_func('cumsum', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumsum(axis=axis, **kwargs)) return self._cython_transform('cumsum', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cummin(self, axis=0, **kwargs): """Cumulative min for each group""" if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def cummax(self, axis=0, **kwargs): """Cumulative max for each group""" if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False) def _get_cythonized_result(self, how, grouper, aggregate=False, cython_dtype=None, needs_values=False, needs_mask=False, needs_ngroups=False, result_is_index=False, pre_processing=None, post_processing=None, **kwargs): """Get result for Cythonized functions Parameters ---------- how : str, Cythonized function name to be called grouper : Grouper object containing pertinent group info aggregate : bool, default False Whether the result should be aggregated to match the number of groups cython_dtype : default None Type of the array that will be modified by the Cython call. If `None`, the type will be inferred from the values of each slice needs_values : bool, default False Whether the values should be a part of the Cython call signature needs_mask : bool, default False Whether boolean mask needs to be part of the Cython call signature needs_ngroups : bool, default False Whether number of groups is part of the Cython call signature result_is_index : bool, default False Whether the result of the Cython operation is an index of values to be retrieved, instead of the actual values themselves pre_processing : function, default None Function to be applied to `values` prior to passing to Cython Raises if `needs_values` is False post_processing : function, default None Function to be applied to result of Cython function **kwargs : dict Extra arguments to be passed back to Cython funcs Returns ------- `Series` or `DataFrame` with filled values """ if result_is_index and aggregate: raise ValueError("'result_is_index' and 'aggregate' cannot both " "be True!") if post_processing: if not callable(pre_processing): raise ValueError("'post_processing' must be a callable!") if pre_processing: if not callable(pre_processing): raise ValueError("'pre_processing' must be a callable!") if not needs_values: raise ValueError("Cannot use 'pre_processing' without " "specifying 'needs_values'!") labels, _, ngroups = grouper.group_info output = collections.OrderedDict() base_func = getattr(libgroupby, how) for name, obj in self._iterate_slices(): if aggregate: result_sz = ngroups else: result_sz = len(obj.values) if not cython_dtype: cython_dtype = obj.values.dtype result = np.zeros(result_sz, dtype=cython_dtype) func = partial(base_func, result, labels) if needs_values: vals = obj.values if pre_processing: vals = pre_processing(vals) func = partial(func, vals) if needs_mask: mask = isna(obj.values).view(np.uint8) func = partial(func, mask) if needs_ngroups: func = partial(func, ngroups) func(**kwargs) # Call func to modify indexer values in place if result_is_index: result = algorithms.take_nd(obj.values, result) if post_processing: result = post_processing(result) output[name] = result if aggregate: return self._wrap_aggregated_output(output) else: return self._wrap_transformed_output(output) @Substitution(name='groupby') @Appender(_doc_template) def shift(self, periods=1, freq=None, axis=0): """ Shift each group by periods observations Parameters ---------- periods : integer, default 1 number of periods to shift freq : frequency string axis : axis to shift, default 0 """ if freq is not None or axis != 0: return self.apply(lambda x: x.shift(periods, freq, axis)) return self._get_cythonized_result('group_shift_indexer', self.grouper, cython_dtype=np.int64, needs_ngroups=True, result_is_index=True, periods=periods) @Substitution(name='groupby') @Appender(_doc_template) def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, axis=0): """Calculate pct_change of each value to previous entry in group""" if freq is not None or axis != 0: return self.apply(lambda x: x.pct_change(periods=periods, fill_method=fill_method, limit=limit, freq=freq, axis=axis)) filled = getattr(self, fill_method)(limit=limit).drop( self.grouper.names, axis=1) shifted = filled.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @Substitution(name='groupby') @Appender(_doc_template) def head(self, n=5): """ Returns first n rows of each group. Essentially equivalent to ``.apply(lambda x: x.head(n))``, except ignores as_index flag. Examples -------- >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) >>> df.groupby('A', as_index=False).head(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(1) A B 0 1 2 2 5 6 """ self._reset_group_selection() mask = self._cumcount_array() < n return self._selected_obj[mask].dropna(subset=[self.keys]) @Substitution(name='groupby') @Appender(_doc_template) def tail(self, n=5): """ Returns last n rows of each group Essentially equivalent to ``.apply(lambda x: x.tail(n))``, except ignores as_index flag. Examples -------- >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]], columns=['A', 'B']) >>> df.groupby('A').tail(1) A B 1 a 2 3 b 2 >>> df.groupby('A').head(1) A B 0 a 1 2 b 1 """ self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n return self._selected_obj[mask].dropna(subset=[self.keys]) GroupBy._add_numeric_operations() @Appender(GroupBy.__doc__) def groupby(obj, by, **kwds): if isinstance(obj, Series): from pandas.core.groupby.generic import SeriesGroupBy klass = SeriesGroupBy elif isinstance(obj, DataFrame): from pandas.core.groupby.generic import DataFrameGroupBy klass = DataFrameGroupBy else: # pragma: no cover raise TypeError('invalid type: %s' % type(obj)) return klass(obj, by, **kwds)

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import types from functools import wraps, partial import datetime import collections import warnings from contextlib import contextmanager import numpy as np from pandas._libs import groupby as libgroupby, Timestamp from pandas.util._validators import validate_kwargs from pandas.util._decorators import ( cache_readonly, Substitution, Appender) from pandas import compat from pandas.compat import zip, range, callable, set_function_name from pandas.compat.numpy import function as nv from pandas.core.dtypes.common import ( is_numeric_dtype, is_scalar, ensure_float) from pandas.core.dtypes.cast import maybe_downcast_to_dtype from pandas.core.dtypes.missing import isna, notna from pandas.core.groupby import base from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.index import Index, MultiIndex from pandas.core.generic import NDFrame from pandas.core.frame import DataFrame from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter import pandas.core.common as com import pandas.core.algorithms as algorithms from pandas.core.config import option_context _doc_template = """ See also -------- pandas.Series.%(name)s pandas.DataFrame.%(name)s pandas.Panel.%(name)s """ _apply_docs = dict( template=""" Apply function `func` group-wise and combine the results together. The function passed to `apply` must take a {input} as its first argument and return a DataFrame, Series or scalar. `apply` will then take care of combining the results back together into a single dataframe or series. `apply` is therefore a highly flexible grouping method. While `apply` is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like `agg` or `transform`. Pandas offers a wide range of method that will be much faster than using `apply` for their specific purposes, so try to use them before reaching for `apply`. Parameters ---------- func : callable A callable that takes a {input} as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to `func`. Returns ------- applied : Series or DataFrame Notes ----- In the current implementation `apply` calls `func` twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if `func` has side-effects, as they will take effect twice for the first group. Examples -------- {examples} See also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. """, dataframe_examples=""" >>> df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1,2,3], 'C': [4,6, 5]}) >>> g = df.groupby('A') Notice that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function passed to `apply` takes a DataFrame as its argument and returns a DataFrame. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x.max() - x.min()) B C A a 1 2 b 0 0 Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 """, series_examples=""" >>> s = pd.Series([0, 1, 2], index='a a b'.split()) >>> g = s.groupby(s.index) From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: The function passed to `apply` takes a Series as its argument and returns a Series. `apply` combines the result for each group together into a new Series: >>> g.apply(lambda x: x*2 if x.name == 'b' else x/2) 0 0.0 1 0.5 2 4.0 dtype: float64 Example 2: The function passed to `apply` takes a Series as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.max() - x.min()) a 1 b 0 dtype: int64 """) _pipe_template = """\ Apply a function `func` with arguments to this %(klass)s object and return the function's result. %(versionadded)s Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) which is much more readable. Parameters ---------- func : callable or tuple of (callable, string) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. args : iterable, optional positional arguments passed into `func`. kwargs : dict, optional a dictionary of keyword arguments passed into `func`. Returns ------- object : the return type of `func`. Notes ----- See more `here <http://pandas.pydata.org/pandas-docs/stable/groupby.html#piping-function-calls>`_ Examples -------- %(examples)s See Also -------- pandas.Series.pipe : Apply a function with arguments to a series pandas.DataFrame.pipe: Apply a function with arguments to a dataframe apply : Apply function to each group instead of to the full %(klass)s object. """ _transform_template = """ Call function producing a like-indexed %(klass)s on each group and return a %(klass)s having the same indexes as the original object filled with the transformed values Parameters ---------- f : function Function to apply to each group Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, f returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. Returns ------- %(klass)s See also -------- aggregate, transform Examples -------- # Same shape >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000 # Broadcastable >>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0 """ class GroupByPlot(PandasObject): def __init__(self, groupby): self._groupby = groupby def __call__(self, *args, **kwargs): def f(self): return self.plot(*args, **kwargs) f.__name__ = 'plot' return self._groupby.apply(f) def __getattr__(self, name): def attr(*args, **kwargs): def f(self): return getattr(self.plot, name)(*args, **kwargs) return self._groupby.apply(f) return attr @contextmanager def _group_selection_context(groupby): groupby._set_group_selection() yield groupby groupby._reset_group_selection() class _GroupBy(PandasObject, SelectionMixin): _group_selection = None _apply_whitelist = frozenset([]) def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): self._selection = selection if isinstance(obj, NDFrame): obj._consolidate_inplace() self.level = level if not as_index: if not isinstance(obj, DataFrame): raise TypeError('as_index=False only valid with DataFrame') if axis != 0: raise ValueError('as_index=False only valid for axis=0') self.as_index = as_index self.keys = keys self.sort = sort self.group_keys = group_keys self.squeeze = squeeze self.observed = observed self.mutated = kwargs.pop('mutated', False) if grouper is None: from pandas.core.groupby.grouper import _get_grouper grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, level=level, sort=sort, observed=observed, mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() # we accept no other args validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) def __unicode__(self): # TODO: Better unicode/repr for GroupBy object return object.__repr__(self) def _assure_grouper(self): pass @property def groups(self): self._assure_grouper() return self.grouper.groups @property def ngroups(self): self._assure_grouper() return self.grouper.ngroups @property def indices(self): self._assure_grouper() return self.grouper.indices def _get_indices(self, names): def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isinstance(s, (Timestamp, datetime.datetime)): return lambda key: Timestamp(key) elif isinstance(s, np.datetime64): return lambda key: Timestamp(key).asm8 else: return lambda key: key if len(names) == 0: return [] if len(self.indices) > 0: index_sample = next(iter(self.indices)) else: index_sample = None # Dummy sample name_sample = names[0] if isinstance(index_sample, tuple): if not isinstance(name_sample, tuple): msg = ("must supply a tuple to get_group with multiple" " grouping keys") raise ValueError(msg) if not len(name_sample) == len(index_sample): try: # If the original grouper was a tuple return [self.indices[name] for name in names] except KeyError: # turns out it wasn't a tuple msg = ("must supply a a same-length tuple to get_group" " with multiple grouping keys") raise ValueError(msg) converters = [get_converter(s) for s in index_sample] names = [tuple(f(n) for f, n in zip(converters, name)) for name in names] else: converter = get_converter(index_sample) names = [converter(name) for name in names] return [self.indices.get(name, []) for name in names] def _get_index(self, name): return self._get_indices([name])[0] @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, Series): if self._group_selection is not None: return self.obj[self._group_selection] return self.obj else: return self.obj[self._selection] def _reset_group_selection(self): if self._group_selection is not None: self._group_selection = None self._reset_cache('_selected_obj') def _set_group_selection(self): grp = self.grouper if not (self.as_index and getattr(grp, 'groupings', None) is not None and self.obj.ndim > 1 and self._group_selection is None): return ax = self.obj._info_axis groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis] if len(groupers): self._group_selection = ax.difference(Index(groupers)).tolist() self._reset_cache('_selected_obj') def _set_result_index_ordered(self, result): if not self.grouper.is_monotonic: index = Index(np.concatenate( self._get_indices(self.grouper.result_index))) result.set_axis(index, axis=self.axis, inplace=True) result = result.sort_index(axis=self.axis) result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True) return result def _dir_additions(self): return self.obj._dir_additions() | self._apply_whitelist def __getattr__(self, attr): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] if hasattr(self.obj, attr): return self._make_wrapper(attr) raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) @Substitution(klass='GroupBy', versionadded='.. versionadded:: 0.21.0', examples="""\ >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby('A').pipe(lambda x: x.max() - x.min()) B A a 2 b 2""") @Appender(_pipe_template) def pipe(self, func, *args, **kwargs): return com._pipe(self, func, *args, **kwargs) plot = property(GroupByPlot) def _make_wrapper(self, name): if name not in self._apply_whitelist: is_callable = callable(getattr(self._selected_obj, name, None)) kind = ' callable ' if is_callable else ' ' msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try " "using the 'apply' method".format(kind, name, type(self).__name__)) raise AttributeError(msg) self._set_group_selection() f = getattr(self._selected_obj, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) f = getattr(type(self._selected_obj), name) def wrapper(*args, **kwargs): kwargs_with_axis = kwargs.copy() if 'axis' not in kwargs_with_axis or \ kwargs_with_axis['axis'] is None: kwargs_with_axis['axis'] = self.axis def curried_with_axis(x): return f(x, *args, **kwargs_with_axis) def curried(x): return f(x, *args, **kwargs) curried.__name__ = curried_with_axis.__name__ = name if name in base.plotting_methods: return self.apply(curried) try: return self.apply(curried_with_axis) except Exception: try: return self.apply(curried) except Exception: # mark this column as an error try: return self._aggregate_item_by_item(name, *args, **kwargs) except (AttributeError): raise ValueError return wrapper def get_group(self, name, obj=None): if obj is None: obj = self._selected_obj inds = self._get_index(name) if not len(inds): raise KeyError(name) return obj._take(inds, axis=self.axis) def __iter__(self): return self.grouper.get_iterator(self.obj, axis=self.axis) @Appender(_apply_docs['template'] .format(input="dataframe", examples=_apply_docs['dataframe_examples'])) def apply(self, func, *args, **kwargs): func = self._is_builtin_func(func) # this is needed so we don't try and wrap strings. If we could if args or kwargs: if callable(func): @wraps(func) def f(g): with np.errstate(all='ignore'): return func(g, *args, **kwargs) else: raise ValueError('func must be a callable if args or ' 'kwargs are supplied') else: f = func # ignore SettingWithCopy here in case the user mutates with option_context('mode.chained_assignment', None): try: result = self._python_apply_general(f) except Exception: # gh-20949 # try again, with .apply acting as a filtering # operation, by excluding the grouping column # This would normally not be triggered # except if the udf is trying an operation that # fails on *some* columns, e.g. a numeric operation # on a string grouper column with _group_selection_context(self): return self._python_apply_general(f) return result def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) return self._wrap_applied_output( keys, values, not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self._selection_name, self._selected_obj def transform(self, func, *args, **kwargs): raise com.AbstractMethodError(self) def _cumcount_array(self, ascending=True): ids, _, ngroups = self.grouper.group_info sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: return np.empty(0, dtype=np.int64) run = np.r_[True, ids[:-1] != ids[1:]] rep = np.diff(np.r_[np.nonzero(run)[0], count]) out = (~run).cumsum() if ascending: out -= np.repeat(out[run], rep) else: out = np.repeat(out[np.r_[run[1:], True]], rep) - out rev = np.empty(count, dtype=np.intp) rev[sorter] = np.arange(count, dtype=np.intp) return out[rev].astype(np.int64, copy=False) def _try_cast(self, result, obj, numeric_only=False): if obj.ndim > 1: dtype = obj.values.dtype else: dtype = obj.dtype if not is_scalar(result): if numeric_only and is_numeric_dtype(dtype) or not numeric_only: result = maybe_downcast_to_dtype(result, dtype) return result def _transform_should_cast(self, func_nm): return (self.size().fillna(0) > 0).any() and ( func_nm not in base.cython_cast_blacklist) def _cython_transform(self, how, numeric_only=True, **kwargs): output = collections.OrderedDict() for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.transform(obj.values, how, **kwargs) except NotImplementedError: continue except AssertionError as e: raise GroupByError(str(e)) if self._transform_should_cast(how): output[name] = self._try_cast(result, obj) else: output[name] = result if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_transformed_output(output, names) def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1): output = {} for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.aggregate(obj.values, how, min_count=min_count) except AssertionError as e: raise GroupByError(str(e)) output[name] = self._try_cast(result, obj) if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_aggregated_output(output, names) def _python_agg_general(self, func, *args, **kwargs): func = self._is_builtin_func(func) f = lambda x: func(x, *args, **kwargs) # iterate through "columns" ex exclusions to populate output dict output = {} for name, obj in self._iterate_slices(): try: result, counts = self.grouper.agg_series(obj, f) output[name] = self._try_cast(result, obj, numeric_only=True) except TypeError: continue if len(output) == 0: return self._python_apply_general(f) if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 for name, result in compat.iteritems(output): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): values = ensure_float(values) output[name] = self._try_cast(values[mask], result) return self._wrap_aggregated_output(output) def _wrap_applied_output(self, *args, **kwargs): raise com.AbstractMethodError(self) def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.core.reshape.concat import concat def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in com._not_none(*values): ax = v._get_axis(self.axis) ax._reset_identity() return values if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) if isinstance(result, Series): result = result.reindex(ax) else: # this is a very unfortunate situation # we have a multi-index that is NOT lexsorted # and we have a result which is duplicated # we can't reindex, so we resort to this if isinstance(ax, MultiIndex) and not ax.is_unique: indexer = algorithms.unique1d( result.index.get_indexer_for(ax.values)) result = result.take(indexer, axis=self.axis) else: result = result.reindex(ax, axis=self.axis) elif self.group_keys: values = reset_identity(values) if self.as_index: group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names, sort=False) else: keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: values = reset_identity(values) result = concat(values, axis=self.axis) if (isinstance(result, Series) and getattr(self, '_selection_name', None) is not None): result.name = self._selection_name return result def _apply_filter(self, indices, dropna): if len(indices) == 0: indices = np.array([], dtype='int64') else: indices = np.sort(np.concatenate(indices)) if dropna: filtered = self._selected_obj.take(indices, axis=self.axis) else: mask = np.empty(len(self._selected_obj.index), dtype=bool) mask.fill(False) mask[indices.astype(int)] = True mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T filtered = self._selected_obj.where(mask) return filtered class GroupBy(_GroupBy): def _bool_agg(self, val_test, skipna): def objs_to_bool(vals): try: vals = vals.astype(np.bool) except ValueError: vals = np.array([bool(x) for x in vals]) return vals.view(np.uint8) def result_to_bool(result): return result.astype(np.bool, copy=False) return self._get_cythonized_result('group_any_all', self.grouper, aggregate=True, cython_dtype=np.uint8, needs_values=True, needs_mask=True, pre_processing=objs_to_bool, post_processing=result_to_bool, val_test=val_test, skipna=skipna) @Substitution(name='groupby') @Appender(_doc_template) def any(self, skipna=True): return self._bool_agg('any', skipna) @Substitution(name='groupby') @Appender(_doc_template) def all(self, skipna=True): return self._bool_agg('all', skipna) @Substitution(name='groupby') @Appender(_doc_template) def count(self): raise NotImplementedError @Substitution(name='groupby') @Appender(_doc_template) def mean(self, *args, **kwargs): nv.validate_groupby_func('mean', args, kwargs, ['numeric_only']) try: return self._cython_agg_general('mean', **kwargs) except GroupByError: raise except Exception: with _group_selection_context(self): f = lambda x: x.mean(axis=self.axis, **kwargs) return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def median(self, **kwargs): try: return self._cython_agg_general('median', **kwargs) except GroupByError: raise except Exception: def f(x): if isinstance(x, np.ndarray): x = Series(x) return x.median(axis=self.axis, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def std(self, ddof=1, *args, **kwargs): nv.validate_groupby_func('std', args, kwargs) return np.sqrt(self.var(ddof=ddof, **kwargs)) @Substitution(name='groupby') @Appender(_doc_template) def var(self, ddof=1, *args, **kwargs): nv.validate_groupby_func('var', args, kwargs) if ddof == 1: try: return self._cython_agg_general('var', **kwargs) except Exception: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) else: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def sem(self, ddof=1): return self.std(ddof=ddof) / np.sqrt(self.count()) @Substitution(name='groupby') @Appender(_doc_template) def size(self): result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result @classmethod def _add_numeric_operations(cls): def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)s of group values" @Substitution(name='groupby', f=name) @Appender(_doc_template) @Appender(_local_template) def f(self, **kwargs): if 'numeric_only' not in kwargs: kwargs['numeric_only'] = numeric_only if 'min_count' not in kwargs: kwargs['min_count'] = min_count self._set_group_selection() try: return self._cython_agg_general( alias, alt=npfunc, **kwargs) except AssertionError as e: raise SpecificationError(str(e)) except Exception: result = self.aggregate( lambda x: npfunc(x, axis=self.axis)) if _convert: result = result._convert(datetime=True) return result set_function_name(f, name, cls) return f def first_compat(x, axis=0): def first(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[0] if isinstance(x, DataFrame): return x.apply(first, axis=axis) else: return first(x) def last_compat(x, axis=0): def last(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[-1] if isinstance(x, DataFrame): return x.apply(last, axis=axis) else: return last(x) cls.sum = groupby_function('sum', 'add', np.sum, min_count=0) cls.prod = groupby_function('prod', 'prod', np.prod, min_count=0) cls.min = groupby_function('min', 'min', np.min, numeric_only=False) cls.max = groupby_function('max', 'max', np.max, numeric_only=False) cls.first = groupby_function('first', 'first', first_compat, numeric_only=False) cls.last = groupby_function('last', 'last', last_compat, numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def ohlc(self): return self._apply_to_column_groupbys( lambda x: x._cython_agg_general('ohlc')) @Appender(DataFrame.describe.__doc__) def describe(self, **kwargs): with _group_selection_context(self): result = self.apply(lambda x: x.describe(**kwargs)) if self.axis == 1: return result.T return result.unstack() @Substitution(name='groupby') @Appender(_doc_template) def resample(self, rule, *args, **kwargs): from pandas.core.resample import get_resampler_for_grouping return get_resampler_for_grouping(self, rule, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def rolling(self, *args, **kwargs): from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def expanding(self, *args, **kwargs): from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, *args, **kwargs) def _fill(self, direction, limit=None): if limit is None: limit = -1 return self._get_cythonized_result('group_fillna_indexer', self.grouper, needs_mask=True, cython_dtype=np.int64, result_is_index=True, direction=direction, limit=limit) @Substitution(name='groupby') def pad(self, limit=None): return self._fill('ffill', limit=limit) ffill = pad @Substitution(name='groupby') def backfill(self, limit=None): return self._fill('bfill', limit=limit) bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) def nth(self, n, dropna=None): if isinstance(n, int): nth_values = [n] elif isinstance(n, (set, list, tuple)): nth_values = list(set(n)) if dropna is not None: raise ValueError( "dropna option with a list of nth values is not supported") else: raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) self._set_group_selection() if not dropna: mask = np.in1d(self._cumcount_array(), nth_values) | \ np.in1d(self._cumcount_array(ascending=False) + 1, -nth_values) out = self._selected_obj[mask] if not self.as_index: return out ids, _, _ = self.grouper.group_info out.index = self.grouper.result_index[ids[mask]] return out.sort_index() if self.sort else out if dropna not in ['any', 'all']: if isinstance(self._selected_obj, Series) and dropna is True: warnings.warn("the dropna={dropna} keyword is deprecated," "use dropna='all' instead. " "For a Series groupby, dropna must be " "either None, 'any' or 'all'.".format( dropna=dropna), FutureWarning, stacklevel=2) dropna = 'all' else: # just returns NaN raise ValueError("For a DataFrame groupby, dropna must be " "either None, 'any' or 'all', " "(was passed %s)." % (dropna),) # old behaviour, but with all and any support for DataFrames. # modified in GH 7559 to have better perf max_len = n if n >= 0 else - 1 - n dropped = self.obj.dropna(how=dropna, axis=self.axis) # get a new grouper for our dropped obj if self.keys is None and self.level is None: # we don't have the grouper info available axis = self.grouper.axis grouper = axis[axis.isin(dropped.index)] else: from pandas.core.groupby.grouper import _get_grouper grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, sort=self.sort, mutated=self.mutated) grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) sizes, result = grb.size(), grb.nth(n) mask = (sizes < max_len).values if len(result) and mask.any(): result.loc[mask] = np.nan # reset/reindex to the original groups if len(self.obj) == len(dropped) or \ len(result) == len(self.grouper.result_index): result.index = self.grouper.result_index else: result = result.reindex(self.grouper.result_index) return result @Substitution(name='groupby') def ngroup(self, ascending=True): with _group_selection_context(self): index = self._selected_obj.index result = Series(self.grouper.group_info[0], index) if not ascending: result = self.ngroups - 1 - result return result @Substitution(name='groupby') def cumcount(self, ascending=True): with _group_selection_context(self): index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) return Series(cumcounts, index) @Substitution(name='groupby') @Appender(_doc_template) def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): if na_option not in {'keep', 'top', 'bottom'}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) return self._cython_transform('rank', numeric_only=False, ties_method=method, ascending=ascending, na_option=na_option, pct=pct, axis=axis) @Substitution(name='groupby') @Appender(_doc_template) def cumprod(self, axis=0, *args, **kwargs): nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, **kwargs)) return self._cython_transform('cumprod', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cumsum(self, axis=0, *args, **kwargs): nv.validate_groupby_func('cumsum', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumsum(axis=axis, **kwargs)) return self._cython_transform('cumsum', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cummin(self, axis=0, **kwargs): if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def cummax(self, axis=0, **kwargs): if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False) def _get_cythonized_result(self, how, grouper, aggregate=False, cython_dtype=None, needs_values=False, needs_mask=False, needs_ngroups=False, result_is_index=False, pre_processing=None, post_processing=None, **kwargs): if result_is_index and aggregate: raise ValueError("'result_is_index' and 'aggregate' cannot both " "be True!") if post_processing: if not callable(pre_processing): raise ValueError("'post_processing' must be a callable!") if pre_processing: if not callable(pre_processing): raise ValueError("'pre_processing' must be a callable!") if not needs_values: raise ValueError("Cannot use 'pre_processing' without " "specifying 'needs_values'!") labels, _, ngroups = grouper.group_info output = collections.OrderedDict() base_func = getattr(libgroupby, how) for name, obj in self._iterate_slices(): if aggregate: result_sz = ngroups else: result_sz = len(obj.values) if not cython_dtype: cython_dtype = obj.values.dtype result = np.zeros(result_sz, dtype=cython_dtype) func = partial(base_func, result, labels) if needs_values: vals = obj.values if pre_processing: vals = pre_processing(vals) func = partial(func, vals) if needs_mask: mask = isna(obj.values).view(np.uint8) func = partial(func, mask) if needs_ngroups: func = partial(func, ngroups) func(**kwargs) # Call func to modify indexer values in place if result_is_index: result = algorithms.take_nd(obj.values, result) if post_processing: result = post_processing(result) output[name] = result if aggregate: return self._wrap_aggregated_output(output) else: return self._wrap_transformed_output(output) @Substitution(name='groupby') @Appender(_doc_template) def shift(self, periods=1, freq=None, axis=0): if freq is not None or axis != 0: return self.apply(lambda x: x.shift(periods, freq, axis)) return self._get_cythonized_result('group_shift_indexer', self.grouper, cython_dtype=np.int64, needs_ngroups=True, result_is_index=True, periods=periods) @Substitution(name='groupby') @Appender(_doc_template) def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, axis=0): if freq is not None or axis != 0: return self.apply(lambda x: x.pct_change(periods=periods, fill_method=fill_method, limit=limit, freq=freq, axis=axis)) filled = getattr(self, fill_method)(limit=limit).drop( self.grouper.names, axis=1) shifted = filled.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @Substitution(name='groupby') @Appender(_doc_template) def head(self, n=5): self._reset_group_selection() mask = self._cumcount_array() < n return self._selected_obj[mask].dropna(subset=[self.keys]) @Substitution(name='groupby') @Appender(_doc_template) def tail(self, n=5): self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n return self._selected_obj[mask].dropna(subset=[self.keys]) GroupBy._add_numeric_operations() @Appender(GroupBy.__doc__) def groupby(obj, by, **kwds): if isinstance(obj, Series): from pandas.core.groupby.generic import SeriesGroupBy klass = SeriesGroupBy elif isinstance(obj, DataFrame): from pandas.core.groupby.generic import DataFrameGroupBy klass = DataFrameGroupBy else: # pragma: no cover raise TypeError('invalid type: %s' % type(obj)) return klass(obj, by, **kwds)

true

f71fef0c42afe5737fff68a898b0a1503169b16b

303

py

Python

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

valores = [[], []] val = 0 for c in range(1, 8): val = int(input(f'Digite o {c}° valor: ')) if val % 2 == 0: valores[0].append(val) else: valores[1].append(val) valores[0].sort() valores[1].sort() print(f'valores impares: {valores[1]}') print(f'valores pares: {valores[0]}')

23.307692

46

0.567657

valores = [[], []] val = 0 for c in range(1, 8): val = int(input(f'Digite o {c}° valor: ')) if val % 2 == 0: valores[0].append(val) else: valores[1].append(val) valores[0].sort() valores[1].sort() print(f'valores impares: {valores[1]}') print(f'valores pares: {valores[0]}')

true

f71fefeee470de4abd81815f4b20fd0e5aa1ae84

6,228

py

Python

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

1

2019-05-27T00:58:46.000Z

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

""" Base class for all posixish platforms """ from pypy.translator.platform import Platform, log, _run_subprocess from pypy.tool import autopath import py, os class BasePosix(Platform): exe_ext = '' def __init__(self, cc=None): if cc is None: cc = 'gcc' self.cc = cc def _libs(self, libraries): return ['-l%s' % (lib,) for lib in libraries] def _libdirs(self, library_dirs): return ['-L%s' % (ldir,) for ldir in library_dirs] def _includedirs(self, include_dirs): return ['-I%s' % (idir,) for idir in include_dirs] def _linkfiles(self, link_files): return list(link_files) def _compile_c_file(self, cc, cfile, compile_args): oname = cfile.new(ext='o') args = ['-c'] + compile_args + [str(cfile), '-o', str(oname)] self._execute_c_compiler(cc, args, oname) return oname def _link(self, cc, ofiles, link_args, standalone, exe_name): args = [str(ofile) for ofile in ofiles] + link_args args += ['-o', str(exe_name)] if not standalone: args = self._args_for_shared(args) self._execute_c_compiler(cc, args, exe_name) return exe_name def _preprocess_dirs(self, include_dirs): # hook for maemo return include_dirs def gen_makefile(self, cfiles, eci, exe_name=None, path=None): cfiles = [py.path.local(f) for f in cfiles] cfiles += [py.path.local(f) for f in eci.separate_module_files] if path is None: path = cfiles[0].dirpath() pypypath = py.path.local(autopath.pypydir) if exe_name is None: exe_name = cfiles[0].new(ext=self.exe_ext) m = GnuMakefile(path) m.exe_name = exe_name m.eci = eci def pypyrel(fpath): rel = py.path.local(fpath).relto(pypypath) if rel: return os.path.join('$(PYPYDIR)', rel) else: return fpath rel_cfiles = [m.pathrel(cfile) for cfile in cfiles] rel_ofiles = [rel_cfile[:-2]+'.o' for rel_cfile in rel_cfiles] m.cfiles = rel_cfiles rel_includedirs = [pypyrel(incldir) for incldir in self._preprocess_dirs(eci.include_dirs)] m.comment('automatically generated makefile') definitions = [ ('PYPYDIR', autopath.pypydir), ('TARGET', exe_name.basename), ('DEFAULT_TARGET', '$(TARGET)'), ('SOURCES', rel_cfiles), ('OBJECTS', rel_ofiles), ('LIBS', self._libs(eci.libraries)), ('LIBDIRS', self._libdirs(eci.library_dirs)), ('INCLUDEDIRS', self._includedirs(rel_includedirs)), ('CFLAGS', self.cflags + list(eci.compile_extra)), ('LDFLAGS', self.link_flags + list(eci.link_extra)), ('CC', self.cc) ] for args in definitions: m.definition(*args) rules = [ ('all', '$(DEFAULT_TARGET)', []), ('$(TARGET)', '$(OBJECTS)', '$(CC) $(LDFLAGS) -o $@ $(OBJECTS) $(LIBDIRS) $(LIBS)'), ('%.o', '%.c', '$(CC) $(CFLAGS) -o $@ -c $< $(INCLUDEDIRS)'), ] for rule in rules: m.rule(*rule) return m def execute_makefile(self, path_to_makefile): if isinstance(path_to_makefile, GnuMakefile): path = path_to_makefile.makefile_dir else: path = path_to_makefile log.execute('make in %s' % (path,)) returncode, stdout, stderr = _run_subprocess('make', ['-C', str(path)]) self._handle_error(returncode, stdout, stderr, path.join('make')) class Definition(object): def __init__(self, name, value): self.name = name self.value = value def write(self, f): def write_list(prefix, lst): for i, fn in enumerate(lst): print >> f, prefix, fn, if i < len(lst)-1: print >> f, '\\' else: print >> f prefix = ' ' * len(prefix) name, value = self.name, self.value if isinstance(value, str): f.write('%s = %s\n' % (name, value)) else: write_list('%s =' % (name,), value) if value: f.write('\n') class Rule(object): def __init__(self, target, deps, body): self.target = target self.deps = deps self.body = body def write(self, f): target, deps, body = self.target, self.deps, self.body if isinstance(deps, str): dep_s = deps else: dep_s = ' '.join(deps) f.write('%s: %s\n' % (target, dep_s)) if isinstance(body, str): f.write('\t%s\n' % body) elif body: f.write('\t%s\n' % '\n\t'.join(body)) f.write('\n') class Comment(object): def __init__(self, body): self.body = body def write(self, f): f.write('# %s\n' % (self.body,)) class GnuMakefile(object): def __init__(self, path=None): self.defs = {} self.lines = [] self.makefile_dir = py.path.local(path) def pathrel(self, fpath): if fpath.dirpath() == self.makefile_dir: return fpath.basename elif fpath.dirpath().dirpath() == self.makefile_dir.dirpath(): return '../' + fpath.relto(self.makefile_dir.dirpath()) else: return str(fpath) def definition(self, name, value): defs = self.defs defn = Definition(name, value) if name in defs: self.lines[defs[name]] = defn else: defs[name] = len(self.lines) self.lines.append(defn) def rule(self, target, deps, body): self.lines.append(Rule(target, deps, body)) def comment(self, body): self.lines.append(Comment(body)) def write(self, out=None): if out is None: f = self.makefile_dir.join('Makefile').open('w') else: f = out for line in self.lines: line.write(f) f.flush() if out is None: f.close()

30.985075

96

0.538536

from pypy.translator.platform import Platform, log, _run_subprocess from pypy.tool import autopath import py, os class BasePosix(Platform): exe_ext = '' def __init__(self, cc=None): if cc is None: cc = 'gcc' self.cc = cc def _libs(self, libraries): return ['-l%s' % (lib,) for lib in libraries] def _libdirs(self, library_dirs): return ['-L%s' % (ldir,) for ldir in library_dirs] def _includedirs(self, include_dirs): return ['-I%s' % (idir,) for idir in include_dirs] def _linkfiles(self, link_files): return list(link_files) def _compile_c_file(self, cc, cfile, compile_args): oname = cfile.new(ext='o') args = ['-c'] + compile_args + [str(cfile), '-o', str(oname)] self._execute_c_compiler(cc, args, oname) return oname def _link(self, cc, ofiles, link_args, standalone, exe_name): args = [str(ofile) for ofile in ofiles] + link_args args += ['-o', str(exe_name)] if not standalone: args = self._args_for_shared(args) self._execute_c_compiler(cc, args, exe_name) return exe_name def _preprocess_dirs(self, include_dirs): return include_dirs def gen_makefile(self, cfiles, eci, exe_name=None, path=None): cfiles = [py.path.local(f) for f in cfiles] cfiles += [py.path.local(f) for f in eci.separate_module_files] if path is None: path = cfiles[0].dirpath() pypypath = py.path.local(autopath.pypydir) if exe_name is None: exe_name = cfiles[0].new(ext=self.exe_ext) m = GnuMakefile(path) m.exe_name = exe_name m.eci = eci def pypyrel(fpath): rel = py.path.local(fpath).relto(pypypath) if rel: return os.path.join('$(PYPYDIR)', rel) else: return fpath rel_cfiles = [m.pathrel(cfile) for cfile in cfiles] rel_ofiles = [rel_cfile[:-2]+'.o' for rel_cfile in rel_cfiles] m.cfiles = rel_cfiles rel_includedirs = [pypyrel(incldir) for incldir in self._preprocess_dirs(eci.include_dirs)] m.comment('automatically generated makefile') definitions = [ ('PYPYDIR', autopath.pypydir), ('TARGET', exe_name.basename), ('DEFAULT_TARGET', '$(TARGET)'), ('SOURCES', rel_cfiles), ('OBJECTS', rel_ofiles), ('LIBS', self._libs(eci.libraries)), ('LIBDIRS', self._libdirs(eci.library_dirs)), ('INCLUDEDIRS', self._includedirs(rel_includedirs)), ('CFLAGS', self.cflags + list(eci.compile_extra)), ('LDFLAGS', self.link_flags + list(eci.link_extra)), ('CC', self.cc) ] for args in definitions: m.definition(*args) rules = [ ('all', '$(DEFAULT_TARGET)', []), ('$(TARGET)', '$(OBJECTS)', '$(CC) $(LDFLAGS) -o $@ $(OBJECTS) $(LIBDIRS) $(LIBS)'), ('%.o', '%.c', '$(CC) $(CFLAGS) -o $@ -c $< $(INCLUDEDIRS)'), ] for rule in rules: m.rule(*rule) return m def execute_makefile(self, path_to_makefile): if isinstance(path_to_makefile, GnuMakefile): path = path_to_makefile.makefile_dir else: path = path_to_makefile log.execute('make in %s' % (path,)) returncode, stdout, stderr = _run_subprocess('make', ['-C', str(path)]) self._handle_error(returncode, stdout, stderr, path.join('make')) class Definition(object): def __init__(self, name, value): self.name = name self.value = value def write(self, f): def write_list(prefix, lst): for i, fn in enumerate(lst): print >> f, prefix, fn, if i < len(lst)-1: print >> f, '\\' else: print >> f prefix = ' ' * len(prefix) name, value = self.name, self.value if isinstance(value, str): f.write('%s = %s\n' % (name, value)) else: write_list('%s =' % (name,), value) if value: f.write('\n') class Rule(object): def __init__(self, target, deps, body): self.target = target self.deps = deps self.body = body def write(self, f): target, deps, body = self.target, self.deps, self.body if isinstance(deps, str): dep_s = deps else: dep_s = ' '.join(deps) f.write('%s: %s\n' % (target, dep_s)) if isinstance(body, str): f.write('\t%s\n' % body) elif body: f.write('\t%s\n' % '\n\t'.join(body)) f.write('\n') class Comment(object): def __init__(self, body): self.body = body def write(self, f): f.write('# %s\n' % (self.body,)) class GnuMakefile(object): def __init__(self, path=None): self.defs = {} self.lines = [] self.makefile_dir = py.path.local(path) def pathrel(self, fpath): if fpath.dirpath() == self.makefile_dir: return fpath.basename elif fpath.dirpath().dirpath() == self.makefile_dir.dirpath(): return '../' + fpath.relto(self.makefile_dir.dirpath()) else: return str(fpath) def definition(self, name, value): defs = self.defs defn = Definition(name, value) if name in defs: self.lines[defs[name]] = defn else: defs[name] = len(self.lines) self.lines.append(defn) def rule(self, target, deps, body): self.lines.append(Rule(target, deps, body)) def comment(self, body): self.lines.append(Comment(body)) def write(self, out=None): if out is None: f = self.makefile_dir.join('Makefile').open('w') else: f = out for line in self.lines: line.write(f) f.flush() if out is None: f.close()

true

f71ff11e0a47dc49e9286ac8954a588df8c45b2e

64

py

Python

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

7

2020-07-24T17:07:58.000Z

2021-05-19T21:47:12.000Z

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

55

2020-07-20T16:56:27.000Z

2022-03-28T12:51:15.000Z

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

6

2020-10-15T18:38:35.000Z

2021-06-20T03:05:43.000Z

__mf_customization__ = 'test' tl_value = 42 __version__ = None

12.8

29

0.765625

__mf_customization__ = 'test' tl_value = 42 __version__ = None

true

f71ff1359f7c6ddeef12a05d12e6963d96fa007e

2,569

py

Python

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # modified from: # https://gist.github.com/rotemtam/88d9a4efae243fc77ed4a0f9917c8f6c import os import glob import click import pandas as pd import xml.etree.ElementTree as ET def xml_to_csv(path: str) -> pd.DataFrame: xml_list = [] for xml_file in glob.glob(path): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): bbx = member.find('bndbox') xmin = int(bbx.find('xmin').text) ymin = int(bbx.find('ymin').text) xmax = int(bbx.find('xmax').text) ymax = int(bbx.find('ymax').text) label = member.find('name').text # The columns are organized as the csv required by keras-retinanet # https://github.com/fizyr/keras-retinanet#csv-datasets # path/to/image.jpg,x1,y1,x2,y2,class_name value = (root.find('filename').text, # int(root.find('size')[0].text), # int(root.find('size')[1].text), xmin, ymin, xmax, ymax, label) xml_list.append(value) column_name = ['filename', # 'width', # 'height', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df def xml_to_csv_file(infile: str, outfile: str): xml_df = xml_to_csv(infile) print(xml_df) xml_df.to_csv(outfile, index=None) @click.group(help='Converts a pascal xml to csv') def cli(): pass @cli.command() @click.option('--dir', type=str, help='Name of source directory,' + ' will convert all xml files in it') @click.option('--out_dir', type=str, help='Name of the destination directory') def directory(dir, out_dir): files_convert = [x for x in os.listdir(dir) if x.endswith("xml")] for xml_file in files_convert: base = os.path.basename(xml_file) filename = os.path.splitext(base)[0] out_filename = filename + ".csv" out_path = os.path.join(out_dir, out_filename) xml_to_csv_file(os.path.join(dir, xml_file), out_path) @cli.command() @click.option('--file', type=str, help='File to be converted to csv') @click.option('--out', type=str, help='Name of the destination file') def xml(file, out): xml_to_csv_file(file, out) if __name__ == '__main__': cli()

29.528736

79

0.570261

import os import glob import click import pandas as pd import xml.etree.ElementTree as ET def xml_to_csv(path: str) -> pd.DataFrame: xml_list = [] for xml_file in glob.glob(path): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): bbx = member.find('bndbox') xmin = int(bbx.find('xmin').text) ymin = int(bbx.find('ymin').text) xmax = int(bbx.find('xmax').text) ymax = int(bbx.find('ymax').text) label = member.find('name').text value = (root.find('filename').text, xmin, ymin, xmax, ymax, label) xml_list.append(value) column_name = ['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df def xml_to_csv_file(infile: str, outfile: str): xml_df = xml_to_csv(infile) print(xml_df) xml_df.to_csv(outfile, index=None) @click.group(help='Converts a pascal xml to csv') def cli(): pass @cli.command() @click.option('--dir', type=str, help='Name of source directory,' + ' will convert all xml files in it') @click.option('--out_dir', type=str, help='Name of the destination directory') def directory(dir, out_dir): files_convert = [x for x in os.listdir(dir) if x.endswith("xml")] for xml_file in files_convert: base = os.path.basename(xml_file) filename = os.path.splitext(base)[0] out_filename = filename + ".csv" out_path = os.path.join(out_dir, out_filename) xml_to_csv_file(os.path.join(dir, xml_file), out_path) @cli.command() @click.option('--file', type=str, help='File to be converted to csv') @click.option('--out', type=str, help='Name of the destination file') def xml(file, out): xml_to_csv_file(file, out) if __name__ == '__main__': cli()

true

f71ff15ecd2f2844f2c2b918043ff8217bac2c9b

3,796

py

Python

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

1

2020-03-18T08:51:44.000Z

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

null

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[8]: ## Advanced Course in Machine Learning ## Week 6 ## Exercise 2 / Random forest import numpy as np import scipy import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import matplotlib.animation as animation from numpy import linalg as LA from sklearn import decomposition from sklearn.preprocessing import StandardScaler from sklearn.metrics import pairwise_distances from sklearn.manifold import TSNE import math import sys import mnist from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score sns.set_style("darkgrid") # In[4]: x_train, t_train, x_test, t_test = mnist.load() # In[48]: x_train = x_train[0:50000,:] t_train = t_train[0:50000] # In[49]: print(x_train.shape) print(t_train.shape) print(x_test.shape) print(t_test.shape) # In[69]: startTestIx = 0 endTestIx = 100 # clf.classes_ # clf.feature_importances_ # print(clf.max_features_) # print(clf.n_classes_) # print(clf.n_features_) # print(clf.n_outputs_) # #clf.tree_ # In[70]: # Randomly select the samples and features for the tree def sample(n, k, x_train, t_train): idx = np.random.randint(x_train.shape[0], size=n) fidx = np.random.randint(x_train.shape[1], size=k) x = x_train[idx, :] x = x[:, fidx] y = t_train[idx] return x, y, idx, fidx #print("Rows: ", idx, ", features ", fidx) #print(x.shape) #print(y.shape) # In[71]: def trainTree(x_train, t_train): clf = DecisionTreeClassifier(random_state=0) clf = clf.fit(x_train, t_train) return clf #cross_val_score(clf, x_train, t_train, cv=10) # In[72]: def ensureAllClasses(newPred, clf): for i in range(10): if i not in clf.classes_: newPred = np.insert(newPred, i, 0, axis=1) return newPred # In[75]: # Main loop def main(M, n, k): pred = np.zeros(shape = (endTestIx - startTestIx, 10), dtype = 'float32') for m in range(M): x, y, idx, fidx = sample(n, k, x_train, t_train) clf = trainTree(x, y) newPred = clf.predict_proba(x_test[startTestIx:endTestIx,fidx]) newPred = ensureAllClasses(newPred, clf) pred = np.add(pred, newPred) pred_classes = np.argmax(pred, axis=1) correct = pred_classes == t_test[startTestIx:endTestIx] acc = sum(correct)/len(correct) #print(pred_classes) #print (acc) return acc # In[85]: Mmax = 100 n = 1000 k = 20 accs = list() for m in range(1, Mmax): accs.append(main(m, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,Mmax), accs) plt.xlabel('Number of trees (M)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of trees vs. accuracy, n = {0}, k = {1}'.format(n, k)) plt.show() # In[80]: M = 100 n = 1000 kmax = 200 accs = list() for k in range(1, kmax, 10): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,kmax,10), accs) plt.xlabel('Number of features per tree (k)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of features per tree vs. accuracy, M = {0}, n = {1}'.format(M, n)) plt.show() # In[81]: M = 100 nmax = 5000 k = 50 accs = list() for n in range(1, nmax, 100): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1, nmax, 100), accs) plt.xlabel('Number of samples per tree (n)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of samples per tree vs. accuracy, M = {0}, k = {1}'.format(M, k)) plt.show() # In[84]: M = 100 n = 1000 k = 50 repeats = 50 accs = list() for i in range(50): accs.append(main(M, n, k)) avAcc = sum(accs)/len(accs) print(avAcc)

19.171717

84

0.663593

sns import matplotlib.pyplot as plt import matplotlib.animation as animation from numpy import linalg as LA from sklearn import decomposition from sklearn.preprocessing import StandardScaler from sklearn.metrics import pairwise_distances from sklearn.manifold import TSNE import math import sys import mnist from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score sns.set_style("darkgrid") x_train, t_train, x_test, t_test = mnist.load() x_train = x_train[0:50000,:] t_train = t_train[0:50000] print(x_train.shape) print(t_train.shape) print(x_test.shape) print(t_test.shape) startTestIx = 0 endTestIx = 100 sample(n, k, x_train, t_train): idx = np.random.randint(x_train.shape[0], size=n) fidx = np.random.randint(x_train.shape[1], size=k) x = x_train[idx, :] x = x[:, fidx] y = t_train[idx] return x, y, idx, fidx def trainTree(x_train, t_train): clf = DecisionTreeClassifier(random_state=0) clf = clf.fit(x_train, t_train) return clf def ensureAllClasses(newPred, clf): for i in range(10): if i not in clf.classes_: newPred = np.insert(newPred, i, 0, axis=1) return newPred def main(M, n, k): pred = np.zeros(shape = (endTestIx - startTestIx, 10), dtype = 'float32') for m in range(M): x, y, idx, fidx = sample(n, k, x_train, t_train) clf = trainTree(x, y) newPred = clf.predict_proba(x_test[startTestIx:endTestIx,fidx]) newPred = ensureAllClasses(newPred, clf) pred = np.add(pred, newPred) pred_classes = np.argmax(pred, axis=1) correct = pred_classes == t_test[startTestIx:endTestIx] acc = sum(correct)/len(correct) return acc Mmax = 100 n = 1000 k = 20 accs = list() for m in range(1, Mmax): accs.append(main(m, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,Mmax), accs) plt.xlabel('Number of trees (M)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of trees vs. accuracy, n = {0}, k = {1}'.format(n, k)) plt.show() M = 100 n = 1000 kmax = 200 accs = list() for k in range(1, kmax, 10): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,kmax,10), accs) plt.xlabel('Number of features per tree (k)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of features per tree vs. accuracy, M = {0}, n = {1}'.format(M, n)) plt.show() M = 100 nmax = 5000 k = 50 accs = list() for n in range(1, nmax, 100): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1, nmax, 100), accs) plt.xlabel('Number of samples per tree (n)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of samples per tree vs. accuracy, M = {0}, k = {1}'.format(M, k)) plt.show() M = 100 n = 1000 k = 50 repeats = 50 accs = list() for i in range(50): accs.append(main(M, n, k)) avAcc = sum(accs)/len(accs) print(avAcc)

true

f71ff26a2521e900d339ce88a0f2dc11b89982ac

8,979

py

Python

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

null

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

null

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

1

2020-10-10T23:31:50.000Z

import torch import torch.nn as nn from threading import Thread from models.layers.mesh_union import MeshUnion import numpy as np from heapq import heappop, heapify class MeshPool(nn.Module): def __init__(self, target, multi_thread=False): super(MeshPool, self).__init__() self.__out_target = target self.__multi_thread = multi_thread self.__fe = None self.__updated_fe = None self.__meshes = None self.__merge_edges = [-1, -1] def __call__(self, fe, meshes): return self.forward(fe, meshes) def forward(self, fe, meshes): self.__updated_fe = [[] for _ in range(len(meshes))] pool_threads = [] self.__fe = fe self.__meshes = meshes # iterate over batch for mesh_index in range(len(meshes)): if self.__multi_thread: pool_threads.append(Thread(target=self.__pool_main, args=(mesh_index,))) pool_threads[-1].start() else: self.__pool_main(mesh_index) if self.__multi_thread: for mesh_index in range(len(meshes)): pool_threads[mesh_index].join() out_features = torch.cat(self.__updated_fe).view(len(meshes), -1, self.__out_target) return out_features def __pool_main(self, mesh_index): mesh = self.__meshes[mesh_index] queue = self.__build_queue(self.__fe[mesh_index, :, :mesh.edges_count], mesh.edges_count) # recycle = [] # last_queue_len = len(queue) last_count = mesh.edges_count + 1 mask = np.ones(mesh.edges_count, dtype=np.uint8) edge_groups = MeshUnion(mesh.edges_count, self.__fe.device) while mesh.edges_count > self.__out_target: value, edge_id = heappop(queue) edge_id = int(edge_id) if mask[edge_id]: self.__pool_edge(mesh, edge_id, mask, edge_groups) mesh.clean(mask, edge_groups) fe = edge_groups.rebuild_features(self.__fe[mesh_index], mask, self.__out_target) self.__updated_fe[mesh_index] = fe def __pool_edge(self, mesh, edge_id, mask, edge_groups): if self.has_boundaries(mesh, edge_id): return False elif self.__clean_side(mesh, edge_id, mask, edge_groups, 0)\ and self.__clean_side(mesh, edge_id, mask, edge_groups, 2) \ and self.__is_one_ring_valid(mesh, edge_id): self.__merge_edges[0] = self.__pool_side(mesh, edge_id, mask, edge_groups, 0) self.__merge_edges[1] = self.__pool_side(mesh, edge_id, mask, edge_groups, 2) mesh.merge_vertices(edge_id) mask[edge_id] = False MeshPool.__remove_group(mesh, edge_groups, edge_id) mesh.edges_count -= 1 return True else: return False def __clean_side(self, mesh, edge_id, mask, edge_groups, side): if mesh.edges_count <= self.__out_target: return False invalid_edges = MeshPool.__get_invalids(mesh, edge_id, edge_groups, side) while len(invalid_edges) != 0 and mesh.edges_count > self.__out_target: self.__remove_triplete(mesh, mask, edge_groups, invalid_edges) if mesh.edges_count <= self.__out_target: return False if self.has_boundaries(mesh, edge_id): return False invalid_edges = self.__get_invalids(mesh, edge_id, edge_groups, side) return True @staticmethod def has_boundaries(mesh, edge_id): for edge in mesh.gemm_edges[edge_id]: if edge == -1 or -1 in mesh.gemm_edges[edge]: return True return False @staticmethod def __is_one_ring_valid(mesh, edge_id): v_a = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 0]]].reshape(-1)) v_b = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 1]]].reshape(-1)) shared = v_a & v_b - set(mesh.edges[edge_id]) return len(shared) == 2 def __pool_side(self, mesh, edge_id, mask, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, _, other_side_b, _, other_keys_b = info self.__redirect_edges(mesh, key_a, side_a - side_a % 2, other_keys_b[0], mesh.sides[key_b, other_side_b]) self.__redirect_edges(mesh, key_a, side_a - side_a % 2 + 1, other_keys_b[1], mesh.sides[key_b, other_side_b + 1]) MeshPool.__union_groups(mesh, edge_groups, key_b, key_a) MeshPool.__union_groups(mesh, edge_groups, edge_id, key_a) mask[key_b] = False MeshPool.__remove_group(mesh, edge_groups, key_b) mesh.remove_edge(key_b) mesh.edges_count -= 1 return key_a @staticmethod def __get_invalids(mesh, edge_id, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b = info shared_items = MeshPool.__get_shared_items(other_keys_a, other_keys_b) if len(shared_items) == 0: return [] else: assert (len(shared_items) == 2) middle_edge = other_keys_a[shared_items[0]] update_key_a = other_keys_a[1 - shared_items[0]] update_key_b = other_keys_b[1 - shared_items[1]] update_side_a = mesh.sides[key_a, other_side_a + 1 - shared_items[0]] update_side_b = mesh.sides[key_b, other_side_b + 1 - shared_items[1]] MeshPool.__redirect_edges(mesh, edge_id, side, update_key_a, update_side_a) MeshPool.__redirect_edges(mesh, edge_id, side + 1, update_key_b, update_side_b) MeshPool.__redirect_edges(mesh, update_key_a, MeshPool.__get_other_side(update_side_a), update_key_b, MeshPool.__get_other_side(update_side_b)) MeshPool.__union_groups(mesh, edge_groups, key_a, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_b, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_a, update_key_a) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_a) MeshPool.__union_groups(mesh, edge_groups, key_b, update_key_b) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_b) return [key_a, key_b, middle_edge] @staticmethod def __redirect_edges(mesh, edge_a_key, side_a, edge_b_key, side_b): mesh.gemm_edges[edge_a_key, side_a] = edge_b_key mesh.gemm_edges[edge_b_key, side_b] = edge_a_key mesh.sides[edge_a_key, side_a] = side_b mesh.sides[edge_b_key, side_b] = side_a @staticmethod def __get_shared_items(list_a, list_b): shared_items = [] for i in range(len(list_a)): for j in range(len(list_b)): if list_a[i] == list_b[j]: shared_items.extend([i, j]) return shared_items @staticmethod def __get_other_side(side): return side + 1 - 2 * (side % 2) @staticmethod def __get_face_info(mesh, edge_id, side): key_a = mesh.gemm_edges[edge_id, side] key_b = mesh.gemm_edges[edge_id, side + 1] side_a = mesh.sides[edge_id, side] side_b = mesh.sides[edge_id, side + 1] other_side_a = (side_a - (side_a % 2) + 2) % 4 other_side_b = (side_b - (side_b % 2) + 2) % 4 other_keys_a = [mesh.gemm_edges[key_a, other_side_a], mesh.gemm_edges[key_a, other_side_a + 1]] other_keys_b = [mesh.gemm_edges[key_b, other_side_b], mesh.gemm_edges[key_b, other_side_b + 1]] return key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b @staticmethod def __remove_triplete(mesh, mask, edge_groups, invalid_edges): vertex = set(mesh.edges[invalid_edges[0]]) for edge_key in invalid_edges: vertex &= set(mesh.edges[edge_key]) mask[edge_key] = False MeshPool.__remove_group(mesh, edge_groups, edge_key) mesh.edges_count -= 3 vertex = list(vertex) assert(len(vertex) == 1) mesh.remove_vertex(vertex[0]) def __build_queue(self, features, edges_count): # delete edges with smallest norm squared_magnitude = torch.sum(features * features, 0) if squared_magnitude.shape[-1] != 1: squared_magnitude = squared_magnitude.unsqueeze(-1) edge_ids = torch.arange(edges_count, device=squared_magnitude.device, dtype=torch.float32).unsqueeze(-1) heap = torch.cat((squared_magnitude, edge_ids), dim=-1).tolist() heapify(heap) return heap @staticmethod def __union_groups(mesh, edge_groups, source, target): edge_groups.union(source, target) mesh.union_groups(source, target) @staticmethod def __remove_group(mesh, edge_groups, index): edge_groups.remove_group(index) mesh.remove_group(index)

44.014706

155

0.646397

import torch import torch.nn as nn from threading import Thread from models.layers.mesh_union import MeshUnion import numpy as np from heapq import heappop, heapify class MeshPool(nn.Module): def __init__(self, target, multi_thread=False): super(MeshPool, self).__init__() self.__out_target = target self.__multi_thread = multi_thread self.__fe = None self.__updated_fe = None self.__meshes = None self.__merge_edges = [-1, -1] def __call__(self, fe, meshes): return self.forward(fe, meshes) def forward(self, fe, meshes): self.__updated_fe = [[] for _ in range(len(meshes))] pool_threads = [] self.__fe = fe self.__meshes = meshes for mesh_index in range(len(meshes)): if self.__multi_thread: pool_threads.append(Thread(target=self.__pool_main, args=(mesh_index,))) pool_threads[-1].start() else: self.__pool_main(mesh_index) if self.__multi_thread: for mesh_index in range(len(meshes)): pool_threads[mesh_index].join() out_features = torch.cat(self.__updated_fe).view(len(meshes), -1, self.__out_target) return out_features def __pool_main(self, mesh_index): mesh = self.__meshes[mesh_index] queue = self.__build_queue(self.__fe[mesh_index, :, :mesh.edges_count], mesh.edges_count) last_count = mesh.edges_count + 1 mask = np.ones(mesh.edges_count, dtype=np.uint8) edge_groups = MeshUnion(mesh.edges_count, self.__fe.device) while mesh.edges_count > self.__out_target: value, edge_id = heappop(queue) edge_id = int(edge_id) if mask[edge_id]: self.__pool_edge(mesh, edge_id, mask, edge_groups) mesh.clean(mask, edge_groups) fe = edge_groups.rebuild_features(self.__fe[mesh_index], mask, self.__out_target) self.__updated_fe[mesh_index] = fe def __pool_edge(self, mesh, edge_id, mask, edge_groups): if self.has_boundaries(mesh, edge_id): return False elif self.__clean_side(mesh, edge_id, mask, edge_groups, 0)\ and self.__clean_side(mesh, edge_id, mask, edge_groups, 2) \ and self.__is_one_ring_valid(mesh, edge_id): self.__merge_edges[0] = self.__pool_side(mesh, edge_id, mask, edge_groups, 0) self.__merge_edges[1] = self.__pool_side(mesh, edge_id, mask, edge_groups, 2) mesh.merge_vertices(edge_id) mask[edge_id] = False MeshPool.__remove_group(mesh, edge_groups, edge_id) mesh.edges_count -= 1 return True else: return False def __clean_side(self, mesh, edge_id, mask, edge_groups, side): if mesh.edges_count <= self.__out_target: return False invalid_edges = MeshPool.__get_invalids(mesh, edge_id, edge_groups, side) while len(invalid_edges) != 0 and mesh.edges_count > self.__out_target: self.__remove_triplete(mesh, mask, edge_groups, invalid_edges) if mesh.edges_count <= self.__out_target: return False if self.has_boundaries(mesh, edge_id): return False invalid_edges = self.__get_invalids(mesh, edge_id, edge_groups, side) return True @staticmethod def has_boundaries(mesh, edge_id): for edge in mesh.gemm_edges[edge_id]: if edge == -1 or -1 in mesh.gemm_edges[edge]: return True return False @staticmethod def __is_one_ring_valid(mesh, edge_id): v_a = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 0]]].reshape(-1)) v_b = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 1]]].reshape(-1)) shared = v_a & v_b - set(mesh.edges[edge_id]) return len(shared) == 2 def __pool_side(self, mesh, edge_id, mask, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, _, other_side_b, _, other_keys_b = info self.__redirect_edges(mesh, key_a, side_a - side_a % 2, other_keys_b[0], mesh.sides[key_b, other_side_b]) self.__redirect_edges(mesh, key_a, side_a - side_a % 2 + 1, other_keys_b[1], mesh.sides[key_b, other_side_b + 1]) MeshPool.__union_groups(mesh, edge_groups, key_b, key_a) MeshPool.__union_groups(mesh, edge_groups, edge_id, key_a) mask[key_b] = False MeshPool.__remove_group(mesh, edge_groups, key_b) mesh.remove_edge(key_b) mesh.edges_count -= 1 return key_a @staticmethod def __get_invalids(mesh, edge_id, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b = info shared_items = MeshPool.__get_shared_items(other_keys_a, other_keys_b) if len(shared_items) == 0: return [] else: assert (len(shared_items) == 2) middle_edge = other_keys_a[shared_items[0]] update_key_a = other_keys_a[1 - shared_items[0]] update_key_b = other_keys_b[1 - shared_items[1]] update_side_a = mesh.sides[key_a, other_side_a + 1 - shared_items[0]] update_side_b = mesh.sides[key_b, other_side_b + 1 - shared_items[1]] MeshPool.__redirect_edges(mesh, edge_id, side, update_key_a, update_side_a) MeshPool.__redirect_edges(mesh, edge_id, side + 1, update_key_b, update_side_b) MeshPool.__redirect_edges(mesh, update_key_a, MeshPool.__get_other_side(update_side_a), update_key_b, MeshPool.__get_other_side(update_side_b)) MeshPool.__union_groups(mesh, edge_groups, key_a, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_b, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_a, update_key_a) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_a) MeshPool.__union_groups(mesh, edge_groups, key_b, update_key_b) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_b) return [key_a, key_b, middle_edge] @staticmethod def __redirect_edges(mesh, edge_a_key, side_a, edge_b_key, side_b): mesh.gemm_edges[edge_a_key, side_a] = edge_b_key mesh.gemm_edges[edge_b_key, side_b] = edge_a_key mesh.sides[edge_a_key, side_a] = side_b mesh.sides[edge_b_key, side_b] = side_a @staticmethod def __get_shared_items(list_a, list_b): shared_items = [] for i in range(len(list_a)): for j in range(len(list_b)): if list_a[i] == list_b[j]: shared_items.extend([i, j]) return shared_items @staticmethod def __get_other_side(side): return side + 1 - 2 * (side % 2) @staticmethod def __get_face_info(mesh, edge_id, side): key_a = mesh.gemm_edges[edge_id, side] key_b = mesh.gemm_edges[edge_id, side + 1] side_a = mesh.sides[edge_id, side] side_b = mesh.sides[edge_id, side + 1] other_side_a = (side_a - (side_a % 2) + 2) % 4 other_side_b = (side_b - (side_b % 2) + 2) % 4 other_keys_a = [mesh.gemm_edges[key_a, other_side_a], mesh.gemm_edges[key_a, other_side_a + 1]] other_keys_b = [mesh.gemm_edges[key_b, other_side_b], mesh.gemm_edges[key_b, other_side_b + 1]] return key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b @staticmethod def __remove_triplete(mesh, mask, edge_groups, invalid_edges): vertex = set(mesh.edges[invalid_edges[0]]) for edge_key in invalid_edges: vertex &= set(mesh.edges[edge_key]) mask[edge_key] = False MeshPool.__remove_group(mesh, edge_groups, edge_key) mesh.edges_count -= 3 vertex = list(vertex) assert(len(vertex) == 1) mesh.remove_vertex(vertex[0]) def __build_queue(self, features, edges_count): squared_magnitude = torch.sum(features * features, 0) if squared_magnitude.shape[-1] != 1: squared_magnitude = squared_magnitude.unsqueeze(-1) edge_ids = torch.arange(edges_count, device=squared_magnitude.device, dtype=torch.float32).unsqueeze(-1) heap = torch.cat((squared_magnitude, edge_ids), dim=-1).tolist() heapify(heap) return heap @staticmethod def __union_groups(mesh, edge_groups, source, target): edge_groups.union(source, target) mesh.union_groups(source, target) @staticmethod def __remove_group(mesh, edge_groups, index): edge_groups.remove_group(index) mesh.remove_group(index)

true

f71ff2713f88d105f4975f0cee61ae6ef8e14fed

896

py

Python

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

""" $lic$ Copyright (C) 2016-2019 by The Board of Trustees of Stanford University This program is free software: you can redistribute it and/or modify it under the terms of the Modified BSD-3 License as published by the Open Source Initiative. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the BSD-3 License for more details. You should have received a copy of the Modified BSD-3 License along with this program. If not, see <https://opensource.org/licenses/BSD-3-Clause>. """ from nn_dataflow.core import Network from nn_dataflow.core import InputLayer, FCLayer ''' MLP-S PRIME, 2016 ''' NN = Network('MLP-S') NN.set_input_layer(InputLayer(784, 1)) NN.add('fc1', FCLayer(784, 500)) NN.add('fc2', FCLayer(500, 250)) NN.add('fc3', FCLayer(250, 10))

27.151515

79

0.756696

from nn_dataflow.core import Network from nn_dataflow.core import InputLayer, FCLayer NN = Network('MLP-S') NN.set_input_layer(InputLayer(784, 1)) NN.add('fc1', FCLayer(784, 500)) NN.add('fc2', FCLayer(500, 250)) NN.add('fc3', FCLayer(250, 10))

true

f71ff2d8ade82949986924ccc275d50947e1700f

3,753

py

Python

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

7

2020-06-30T08:15:53.000Z

2022-03-22T10:28:09.000Z

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

1

2021-09-27T03:24:46.000Z

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

1

2020-12-07T14:55:30.000Z

import base64 import re from dataclasses import dataclass from enum import Enum from typing import Union, List from kubernetes import client, config from kubernetes.client import V1ConfigMapList, V1SecretList, CoreV1Api, V1Secret, V1ConfigMap from nexuscasc.logger import Logger class ResourceType(Enum): SECRET, CONFIGMAP = range(2) @dataclass class WatchedResource: name: str version: str type: ResourceType class K8sConfigHandler: v1: CoreV1Api = None watch_list: List[WatchedResource] = list() def __init__(self, local: bool = False): if local: config.load_kube_config() else: config.load_incluster_config() self.v1 = client.CoreV1Api() @staticmethod def filter_resources( resources: Union[V1ConfigMapList, V1SecretList], label_value: str = None ) -> List[Union[V1ConfigMap, V1Secret]]: matches = list() for res in resources.items: if label_value is None: matches.append(res) elif len(list(filter(lambda x: res.metadata.labels[x] == label_value, res.metadata.labels.keys()))) > 0: matches.append(res) return matches def find_config_maps(self, namespace: str, label: str, label_value: str = None) -> List[V1ConfigMap]: config_maps = self.v1.list_namespaced_config_map(namespace=namespace, label_selector=label) return self.filter_resources(config_maps, label_value) def find_secrets(self, namespace: str, label: str, label_value: str = None) -> List[V1Secret]: secrets = self.v1.list_namespaced_secret(namespace=namespace, label_selector=label) return self.filter_resources(secrets, label_value) @staticmethod def extract_yaml_strings_from_resources(resources: List[Union[V1ConfigMap, V1Secret]]) -> List[str]: yaml_str = list() for res in resources: for k in filter(lambda key: re.search("\\.yml|\\.yaml$", key), res.data.keys()): if type(res) == V1Secret: Logger.debug(f"Found yaml in key '{k}' for secret '{res.metadata.name}'") yaml_str.append(base64.b64decode(res.data[k]).decode()) else: Logger.debug(f"Found yaml in key '{k}' for configmap '{res.metadata.name}'") yaml_str.append(res.data[k]) return yaml_str def any_resource_has_changed(self, resources: List[Union[V1ConfigMap, V1Secret]]) -> bool: has_changed = False if len(self.watch_list) == 0: has_changed = True for res in resources: self.watch_resource(res) else: for res in resources: r_name = res.metadata.name r_type = ResourceType.SECRET if type(res) == V1Secret else ResourceType.CONFIGMAP watched_resource = next(filter(lambda r: r_name == r.name and r_type == r.type, self.watch_list), None) if watched_resource is None: self.watch_resource(res) has_changed = True break elif watched_resource.version != res.metadata.resource_version: watched_resource.version = res.metadata.resource_version has_changed = True break return has_changed def watch_resource(self, resource: Union[V1ConfigMap, V1Secret]): self.watch_list.append( WatchedResource( name=resource.metadata.name, version=resource.metadata.resource_version, type=ResourceType.SECRET if type(resource) == V1Secret else ResourceType.CONFIGMAP ))

38.295918

119

0.625633

import base64 import re from dataclasses import dataclass from enum import Enum from typing import Union, List from kubernetes import client, config from kubernetes.client import V1ConfigMapList, V1SecretList, CoreV1Api, V1Secret, V1ConfigMap from nexuscasc.logger import Logger class ResourceType(Enum): SECRET, CONFIGMAP = range(2) @dataclass class WatchedResource: name: str version: str type: ResourceType class K8sConfigHandler: v1: CoreV1Api = None watch_list: List[WatchedResource] = list() def __init__(self, local: bool = False): if local: config.load_kube_config() else: config.load_incluster_config() self.v1 = client.CoreV1Api() @staticmethod def filter_resources( resources: Union[V1ConfigMapList, V1SecretList], label_value: str = None ) -> List[Union[V1ConfigMap, V1Secret]]: matches = list() for res in resources.items: if label_value is None: matches.append(res) elif len(list(filter(lambda x: res.metadata.labels[x] == label_value, res.metadata.labels.keys()))) > 0: matches.append(res) return matches def find_config_maps(self, namespace: str, label: str, label_value: str = None) -> List[V1ConfigMap]: config_maps = self.v1.list_namespaced_config_map(namespace=namespace, label_selector=label) return self.filter_resources(config_maps, label_value) def find_secrets(self, namespace: str, label: str, label_value: str = None) -> List[V1Secret]: secrets = self.v1.list_namespaced_secret(namespace=namespace, label_selector=label) return self.filter_resources(secrets, label_value) @staticmethod def extract_yaml_strings_from_resources(resources: List[Union[V1ConfigMap, V1Secret]]) -> List[str]: yaml_str = list() for res in resources: for k in filter(lambda key: re.search("\\.yml|\\.yaml$", key), res.data.keys()): if type(res) == V1Secret: Logger.debug(f"Found yaml in key '{k}' for secret '{res.metadata.name}'") yaml_str.append(base64.b64decode(res.data[k]).decode()) else: Logger.debug(f"Found yaml in key '{k}' for configmap '{res.metadata.name}'") yaml_str.append(res.data[k]) return yaml_str def any_resource_has_changed(self, resources: List[Union[V1ConfigMap, V1Secret]]) -> bool: has_changed = False if len(self.watch_list) == 0: has_changed = True for res in resources: self.watch_resource(res) else: for res in resources: r_name = res.metadata.name r_type = ResourceType.SECRET if type(res) == V1Secret else ResourceType.CONFIGMAP watched_resource = next(filter(lambda r: r_name == r.name and r_type == r.type, self.watch_list), None) if watched_resource is None: self.watch_resource(res) has_changed = True break elif watched_resource.version != res.metadata.resource_version: watched_resource.version = res.metadata.resource_version has_changed = True break return has_changed def watch_resource(self, resource: Union[V1ConfigMap, V1Secret]): self.watch_list.append( WatchedResource( name=resource.metadata.name, version=resource.metadata.resource_version, type=ResourceType.SECRET if type(resource) == V1Secret else ResourceType.CONFIGMAP ))

true

f71ff2dfd267b7ca272fcc6a2a50017e19cd8ff1

3,423

py

Python

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnb-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

5

2021-04-02T20:49:19.000Z

2021-06-11T06:22:24.000Z

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnb-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

2

2021-06-13T20:22:09.000Z

2021-06-14T23:09:14.000Z

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnc-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

null

import datetime import os from unittest.mock import Mock from bnc.cli import cli from bnc.utils.utils import json_to_str from tests.commands.common import read_json_test_file, get_headers from tests.commands.common_fixtures import * def get_json_filename(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'res', 'new_order.json') @pytest.fixture(scope='session') def data(): return read_json_test_file(get_json_filename()) @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-ncoid', "custom_id"], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_client_order_id', "custom_id"], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-iq', 0.20], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--iceberg_qty', 0.20] ]) def test_new_order_limit_return_full_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_full'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_full']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'ACK'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'ACK'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_ack'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_ack']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'RESULT'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'RESULT'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_result'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_result']) + '\n'

48.211268

138

0.650891

import datetime import os from unittest.mock import Mock from bnc.cli import cli from bnc.utils.utils import json_to_str from tests.commands.common import read_json_test_file, get_headers from tests.commands.common_fixtures import * def get_json_filename(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'res', 'new_order.json') @pytest.fixture(scope='session') def data(): return read_json_test_file(get_json_filename()) @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-ncoid', "custom_id"], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_client_order_id', "custom_id"], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-iq', 0.20], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--iceberg_qty', 0.20] ]) def test_new_order_limit_return_full_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_full'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_full']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'ACK'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'ACK'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_ack'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_ack']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'RESULT'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'RESULT'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_result'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_result']) + '\n'

true

f71ff3f9334377468b2b24b73aa343bc3c717efb

2,218

py

Python

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

2

2016-06-23T15:35:29.000Z

2022-01-11T00:55:21.000Z

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

27

2016-06-24T12:28:01.000Z

2022-01-13T00:37:25.000Z

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.2 on 2016-09-02 20:22 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0008_auto_20160801_1937'), ] operations = [ migrations.CreateModel( name='Chapter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=250)), ('number', models.PositiveSmallIntegerField()), ('date', models.DateField(blank=True, null=True)), ('date_created', models.DateField(auto_now_add=True)), ('date_modified', models.DateField(auto_now=True)), ], ), migrations.CreateModel( name='Tariff', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('index', models.PositiveSmallIntegerField(blank=True, null=True)), ('sub_index', models.PositiveSmallIntegerField(blank=True, null=True)), ('name', models.CharField(blank=True, max_length=250)), ('variable_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo variable')), ('fixed_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo fijo')), ('workshop_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo taller')), ('total_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo total')), ('fees', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Honorarios')), ('total_tariff', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Total arancel')), ('chapter', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Chapter')), ], ), ]

50.409091

131

0.625338

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0008_auto_20160801_1937'), ] operations = [ migrations.CreateModel( name='Chapter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=250)), ('number', models.PositiveSmallIntegerField()), ('date', models.DateField(blank=True, null=True)), ('date_created', models.DateField(auto_now_add=True)), ('date_modified', models.DateField(auto_now=True)), ], ), migrations.CreateModel( name='Tariff', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('index', models.PositiveSmallIntegerField(blank=True, null=True)), ('sub_index', models.PositiveSmallIntegerField(blank=True, null=True)), ('name', models.CharField(blank=True, max_length=250)), ('variable_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo variable')), ('fixed_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo fijo')), ('workshop_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo taller')), ('total_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo total')), ('fees', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Honorarios')), ('total_tariff', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Total arancel')), ('chapter', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Chapter')), ], ), ]

true

f71ff457dfbaab5f3f5847da75668942d7052f7a

3,330

py

Python

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

null

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

9

2019-12-04T23:29:22.000Z

2022-02-10T12:19:23.000Z

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

null

""" Django settings for dockerdjango project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'gc(_1u30_&as3g*xqy8k@u$aj22*@_#d$ylw@s8cjfjmz%ukq(' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dockerdjango.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dockerdjango.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default':{ 'ENGINE': 'django.db.backends.mysql', 'NAME': 'my-app-db', 'USER': 'root', 'PASSWORD': 'password', 'HOST': 'db', 'PORT': 3306 } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

25.419847

91

0.678378

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'gc(_1u30_&as3g*xqy8k@u$aj22*@_#d$ylw@s8cjfjmz%ukq(' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dockerdjango.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dockerdjango.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default':{ 'ENGINE': 'django.db.backends.mysql', 'NAME': 'my-app-db', 'USER': 'root', 'PASSWORD': 'password', 'HOST': 'db', 'PORT': 3306 } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

true

f71ff4f192b3057091ebc889bf78714a2e21a9d6

2,455

py

Python

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

9,136

2015-01-02T00:41:45.000Z

2022-03-31T15:30:02.000Z

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

2,424

2015-01-05T08:55:58.000Z

2022-03-30T19:34:55.000Z

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

2,921

2015-01-02T10:19:30.000Z

2022-03-31T02:48:42.000Z

import pybullet as p import pybullet_data p.connect(p.SHARED_MEMORY) p.setAdditionalSearchPath(pybullet_data.getDataPath()) objects = [ p.loadURDF("plane.urdf", 0.000000, 0.000000, -.300000, 0.000000, 0.000000, 0.000000, 1.000000) ] objects = [ p.loadURDF("quadruped/minitaur.urdf", [-0.000046, -0.000068, 0.200774], [-0.000701, 0.000387, -0.000252, 1.000000], useFixedBase=False) ] ob = objects[0] jointPositions = [ 0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000, -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193, 0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517 ] for ji in range(p.getNumJoints(ob)): p.resetJointState(ob, ji, jointPositions[ji]) p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0) cid0 = p.createConstraint(1, 3, 1, 6, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid0, maxForce=500.000000) cid1 = p.createConstraint(1, 16, 1, 19, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid1, maxForce=500.000000) cid2 = p.createConstraint(1, 9, 1, 12, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid2, maxForce=500.000000) cid3 = p.createConstraint(1, 22, 1, 25, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid3, maxForce=500.000000) p.setGravity(0.000000, 0.000000, 0.000000) p.stepSimulation() p.disconnect()

51.145833

98

0.608147

import pybullet as p import pybullet_data p.connect(p.SHARED_MEMORY) p.setAdditionalSearchPath(pybullet_data.getDataPath()) objects = [ p.loadURDF("plane.urdf", 0.000000, 0.000000, -.300000, 0.000000, 0.000000, 0.000000, 1.000000) ] objects = [ p.loadURDF("quadruped/minitaur.urdf", [-0.000046, -0.000068, 0.200774], [-0.000701, 0.000387, -0.000252, 1.000000], useFixedBase=False) ] ob = objects[0] jointPositions = [ 0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000, -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193, 0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517 ] for ji in range(p.getNumJoints(ob)): p.resetJointState(ob, ji, jointPositions[ji]) p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0) cid0 = p.createConstraint(1, 3, 1, 6, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid0, maxForce=500.000000) cid1 = p.createConstraint(1, 16, 1, 19, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid1, maxForce=500.000000) cid2 = p.createConstraint(1, 9, 1, 12, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid2, maxForce=500.000000) cid3 = p.createConstraint(1, 22, 1, 25, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid3, maxForce=500.000000) p.setGravity(0.000000, 0.000000, 0.000000) p.stepSimulation() p.disconnect()

true

f71ff582921b55fc70764b796e2441625d14a39b

3,212

py

Python

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

2

2021-01-30T21:20:36.000Z

2021-01-30T22:15:07.000Z

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

null

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

1

2021-01-18T23:12:18.000Z

from itertools import chain from pathlib import Path import nltk import torchtext from quati.dataset.fields.words import WordsField from quati.dataset.fields.tags import TagsField from quati.dataset.corpora.corpus import Corpus def create_single_file_for_pos_and_neg(corpus_path): new_file_path = Path(corpus_path, 'data.txt') # do not create this file again if it is already there if not new_file_path.exists(): neg_files = sorted(Path(corpus_path, 'neg').glob('*.txt')) pos_files = sorted(Path(corpus_path, 'pos').glob('*.txt')) paths = chain(neg_files, pos_files) new_file = new_file_path.open('w', encoding='utf8') for file_path in paths: content = file_path.read_text().strip() content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') label = '1' if 'pos' in str(file_path) else '0' new_file.write(label + ' ' + content + '\n') new_file.seek(0) new_file.close() return new_file_path class IMDBCorpus(Corpus): task = 'doc' @staticmethod def create_fields_tuples(): # if you choose tokenizer='spacy', please install the en package: # python3 -m spacy download en tokenizer = nltk.WordPunctTokenizer() # tokenizer = nltk.TreebankWordTokenizer() fields_tuples = [ ('words', WordsField(tokenize=tokenizer.tokenize)), ('target', TagsField()) ] return fields_tuples def read(self, corpus_path): """ First, read the positive and negative examples, which are located in different folders: `pos/` and `neg/`. Second, split the ` ` tags from other tokens. Third, save a new file called `data.txt` in the root directory, with the following structure: label_0 text_0 label_1 text_1 ... label_M text_M Args: corpus_path: path to the root directory where `pos/` and `neg/` are located. """ new_file_path = create_single_file_for_pos_and_neg(corpus_path) self.corpus_path = str(new_file_path) self.open(self.corpus_path) if self.lazy is True: return self else: return list(self) def _read(self, file): for line in file: line = line.strip().split() if line: label = line[0] text = ' '.join(line[1:]) yield self.make_torchtext_example(text, label) def make_torchtext_example(self, text, label=None): ex = {'words': text, 'target': label} if 'target' not in self.fields_dict.keys(): del ex['target'] assert ex.keys() == self.fields_dict.keys() return torchtext.data.Example.fromdict(ex, self.fields_dict) if __name__ == '__main__': from quati.dataset.corpora.test_corpus import quick_test quick_test( IMDBCorpus, '../../../data/corpus/imdb/test/', lazy=True, )

33.113402

76

0.59589

from itertools import chain from pathlib import Path import nltk import torchtext from quati.dataset.fields.words import WordsField from quati.dataset.fields.tags import TagsField from quati.dataset.corpora.corpus import Corpus def create_single_file_for_pos_and_neg(corpus_path): new_file_path = Path(corpus_path, 'data.txt') if not new_file_path.exists(): neg_files = sorted(Path(corpus_path, 'neg').glob('*.txt')) pos_files = sorted(Path(corpus_path, 'pos').glob('*.txt')) paths = chain(neg_files, pos_files) new_file = new_file_path.open('w', encoding='utf8') for file_path in paths: content = file_path.read_text().strip() content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') label = '1' if 'pos' in str(file_path) else '0' new_file.write(label + ' ' + content + '\n') new_file.seek(0) new_file.close() return new_file_path class IMDBCorpus(Corpus): task = 'doc' @staticmethod def create_fields_tuples(): tokenizer = nltk.WordPunctTokenizer() fields_tuples = [ ('words', WordsField(tokenize=tokenizer.tokenize)), ('target', TagsField()) ] return fields_tuples def read(self, corpus_path): new_file_path = create_single_file_for_pos_and_neg(corpus_path) self.corpus_path = str(new_file_path) self.open(self.corpus_path) if self.lazy is True: return self else: return list(self) def _read(self, file): for line in file: line = line.strip().split() if line: label = line[0] text = ' '.join(line[1:]) yield self.make_torchtext_example(text, label) def make_torchtext_example(self, text, label=None): ex = {'words': text, 'target': label} if 'target' not in self.fields_dict.keys(): del ex['target'] assert ex.keys() == self.fields_dict.keys() return torchtext.data.Example.fromdict(ex, self.fields_dict) if __name__ == '__main__': from quati.dataset.corpora.test_corpus import quick_test quick_test( IMDBCorpus, '../../../data/corpus/imdb/test/', lazy=True, )

true

f71ff58b38aa884ffd4cc5997958ad2274a7a77b

491

py

Python

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

from memstatsbeat import BaseTest import os class Test(BaseTest): def test_base(self): """ Basic test with exiting Memstatsbeat normally """ self.render_config_template( path=os.path.abspath(self.working_dir) + "/log/*" ) memstatsbeat_proc = self.start_beat() self.wait_until(lambda: self.log_contains("memstatsbeat is running")) exit_code = memstatsbeat_proc.kill_and_wait() assert exit_code == 0

24.55

77

0.641548

from memstatsbeat import BaseTest import os class Test(BaseTest): def test_base(self): self.render_config_template( path=os.path.abspath(self.working_dir) + "/log/*" ) memstatsbeat_proc = self.start_beat() self.wait_until(lambda: self.log_contains("memstatsbeat is running")) exit_code = memstatsbeat_proc.kill_and_wait() assert exit_code == 0

true

f71ff5e2374591301bbf01a9ea272bd250502167

7,448

py

Python

RestPy/ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/instruction.py

ralfjon/IxNetwork

c0c834fbc465af69c12fd6b7cee4628baba7fff1

[ "MIT" ]

null

RestPy/ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/instruction.py

ralfjon/IxNetwork

c0c834fbc465af69c12fd6b7cee4628baba7fff1

[ "MIT" ]

null

RestPy/ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/instruction.py

ralfjon/IxNetwork

c0c834fbc465af69c12fd6b7cee4628baba7fff1

[ "MIT" ]

null

# Copyright 1997 - 2018 by IXIA Keysight # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files class Instruction(Base): """The Instruction class encapsulates a user managed instruction node in the ixnetwork hierarchy. An instance of the class can be obtained by accessing the Instruction property from a parent instance. The internal properties list will be empty when the property is accessed and is populated from the server using the find method. The internal properties list can be managed by the user by using the add and remove methods. """ _SDM_NAME = 'instruction' def __init__(self, parent): super(Instruction, self).__init__(parent) @property def Actions(self): """An instance of the Actions class. Returns: obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.actions.Actions) Raises: NotFoundError: The requested resource does not exist on the server ServerError: The server has encountered an uncategorized error condition """ from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.actions import Actions return Actions(self) @property def Field(self): """An instance of the Field class. Returns: obj(ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.field.Field) Raises: NotFoundError: The requested resource does not exist on the server ServerError: The server has encountered an uncategorized error condition """ from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.field import Field return Field(self) @property def Count(self): """Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group Returns: number """ return self._get_attribute('count') @property def Description(self): """Description of the field. Returns: str """ return self._get_attribute('description') @Description.setter def Description(self, value): self._set_attribute('description', value) @property def DisplayName(self): """Display name used by GUI. Returns: str """ return self._get_attribute('displayName') @property def IsEditable(self): """Information on the requirement of the field. Returns: bool """ return self._get_attribute('isEditable') @IsEditable.setter def IsEditable(self, value): self._set_attribute('isEditable', value) @property def IsEnabled(self): """Enables disables the field. Returns: bool """ return self._get_attribute('isEnabled') @IsEnabled.setter def IsEnabled(self, value): self._set_attribute('isEnabled', value) @property def IsRequired(self): """Information on the requirement of the field. Returns: bool """ return self._get_attribute('isRequired') @IsRequired.setter def IsRequired(self, value): self._set_attribute('isRequired', value) @property def Name(self): """Name of packet field Returns: str """ return self._get_attribute('name') @Name.setter def Name(self, value): self._set_attribute('name', value) def add(self, Description=None, IsEditable=None, IsEnabled=None, IsRequired=None, Name=None): """Adds a new instruction node on the server and retrieves it in this instance. Args: Description (str): Description of the field. IsEditable (bool): Information on the requirement of the field. IsEnabled (bool): Enables disables the field. IsRequired (bool): Information on the requirement of the field. Name (str): Name of packet field Returns: self: This instance with all currently retrieved instruction data using find and the newly added instruction data available through an iterator or index Raises: ServerError: The server has encountered an uncategorized error condition """ return self._create(locals()) def remove(self): """Deletes all the instruction data in this instance from server. Raises: NotFoundError: The requested resource does not exist on the server ServerError: The server has encountered an uncategorized error condition """ self._delete() def find(self, Count=None, Description=None, DisplayName=None, IsEditable=None, IsEnabled=None, IsRequired=None, Name=None): """Finds and retrieves instruction data from the server. All named parameters support regex and can be used to selectively retrieve instruction data from the server. By default the find method takes no parameters and will retrieve all instruction data from the server. Args: Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group Description (str): Description of the field. DisplayName (str): Display name used by GUI. IsEditable (bool): Information on the requirement of the field. IsEnabled (bool): Enables disables the field. IsRequired (bool): Information on the requirement of the field. Name (str): Name of packet field Returns: self: This instance with matching instruction data retrieved from the server available through an iterator or index Raises: ServerError: The server has encountered an uncategorized error condition """ return self._select(locals()) def read(self, href): """Retrieves a single instance of instruction data from the server. Args: href (str): An href to the instance to be retrieved Returns: self: This instance with the instruction data from the server available through an iterator or index Raises: NotFoundError: The requested resource does not exist on the server ServerError: The server has encountered an uncategorized error condition """ return self._read(href) def AddAction(self, Arg2): """Executes the addAction operation on the server. Adds an Action item. Args: Arg1 (str(None|/api/v1/sessions/1/ixnetwork/topology)): The method internally sets Arg1 to the current href for this instance Arg2 (str): Raises: NotFoundError: The requested resource does not exist on the server ServerError: The server has encountered an uncategorized error condition """ Arg1 = self.href return self._execute('AddAction', payload=locals(), response_object=None)

33.102222

156

0.733217

from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files class Instruction(Base): _SDM_NAME = 'instruction' def __init__(self, parent): super(Instruction, self).__init__(parent) @property def Actions(self): from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.actions import Actions return Actions(self) @property def Field(self): from ixnetwork_restpy.testplatform.sessions.ixnetwork.topology.field import Field return Field(self) @property def Count(self): return self._get_attribute('count') @property def Description(self): return self._get_attribute('description') @Description.setter def Description(self, value): self._set_attribute('description', value) @property def DisplayName(self): return self._get_attribute('displayName') @property def IsEditable(self): return self._get_attribute('isEditable') @IsEditable.setter def IsEditable(self, value): self._set_attribute('isEditable', value) @property def IsEnabled(self): return self._get_attribute('isEnabled') @IsEnabled.setter def IsEnabled(self, value): self._set_attribute('isEnabled', value) @property def IsRequired(self): return self._get_attribute('isRequired') @IsRequired.setter def IsRequired(self, value): self._set_attribute('isRequired', value) @property def Name(self): return self._get_attribute('name') @Name.setter def Name(self, value): self._set_attribute('name', value) def add(self, Description=None, IsEditable=None, IsEnabled=None, IsRequired=None, Name=None): return self._create(locals()) def remove(self): self._delete() def find(self, Count=None, Description=None, DisplayName=None, IsEditable=None, IsEnabled=None, IsRequired=None, Name=None): return self._select(locals()) def read(self, href): return self._read(href) def AddAction(self, Arg2): Arg1 = self.href return self._execute('AddAction', payload=locals(), response_object=None)

true

f71ff7f5175b2552338f6b41f9f1520efde5ebe9

1,203

py

Python

pytorch_basic_template/model/model_entry.py

ldylab/deep_learning_with_pytorch

c86a2e24ee94ade1a78b66f10eb69b6e1fdd4463

[ "MIT" ]

null

pytorch_basic_template/model/model_entry.py

ldylab/deep_learning_with_pytorch

c86a2e24ee94ade1a78b66f10eb69b6e1fdd4463

[ "MIT" ]

null

pytorch_basic_template/model/model_entry.py

ldylab/deep_learning_with_pytorch

c86a2e24ee94ade1a78b66f10eb69b6e1fdd4463

[ "MIT" ]

null

# from model.base.fcn import CustomFcn # from model.best.fcn import DeepLabv3Fcn # from model.better.fcn import Resnet101Fcn # from model.sota.fcn import LightFcn from model.alexnet.alexnet_model import AlexNet from model.lenet5.lenet_5_model import LeNet5 from model.vggnet.vggnet16 import VGG16 from model.densenet.densenet_model import DenseNet121 from model.resnet.resnet34_model import resnet34 from model.resnet.resnet101_model import resnet101, resnet50 from model.cotnet.cotnet_model import cotnet50 import torch.nn as nn def select_model(args): type2model = { 'alexnet_fcn': AlexNet(args), 'lenet5_fcn': LeNet5(args), 'vggnet16_fcn': VGG16(args), 'densenet121_fcn': DenseNet121(num_classes=args.classes_num, grayscale=False), 'resnet34_fcn': resnet34(num_classes=args.classes_num), 'resnet101_fcn': resnet101(num_classes=args.classes_num), 'resnet50_fcn': resnet50(num_classes=args.classes_num), 'cotnet50_fcn': cotnet50(num_classes=args.classes_num) } model = type2model[args.model_type] return model def equip_multi_gpu(model, args): model = nn.DataParallel(model, device_ids=args.gpus) return model

36.454545

86

0.758936

from model.alexnet.alexnet_model import AlexNet from model.lenet5.lenet_5_model import LeNet5 from model.vggnet.vggnet16 import VGG16 from model.densenet.densenet_model import DenseNet121 from model.resnet.resnet34_model import resnet34 from model.resnet.resnet101_model import resnet101, resnet50 from model.cotnet.cotnet_model import cotnet50 import torch.nn as nn def select_model(args): type2model = { 'alexnet_fcn': AlexNet(args), 'lenet5_fcn': LeNet5(args), 'vggnet16_fcn': VGG16(args), 'densenet121_fcn': DenseNet121(num_classes=args.classes_num, grayscale=False), 'resnet34_fcn': resnet34(num_classes=args.classes_num), 'resnet101_fcn': resnet101(num_classes=args.classes_num), 'resnet50_fcn': resnet50(num_classes=args.classes_num), 'cotnet50_fcn': cotnet50(num_classes=args.classes_num) } model = type2model[args.model_type] return model def equip_multi_gpu(model, args): model = nn.DataParallel(model, device_ids=args.gpus) return model

true

f71ff8d04b827e68cb215f95a82095cadf50e4ca

1,094

py

Python

data/p4VQE/R4/benchmark/startPyquil330.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R4/benchmark/startPyquil330.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p4VQE/R4/benchmark/startPyquil330.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=4 # total number=10 import pyquil from pyquil.api import local_forest_runtime, QVMConnection from pyquil import Program, get_qc from pyquil.gates import * import numpy as np conn = QVMConnection() def make_circuit()-> Program: prog = Program() # circuit begin prog += H(1) # number=2 prog += H(2) # number=3 prog += H(3) # number=4 prog += Y(3) # number=5 prog += SWAP(1,0) # number=6 prog += Y(2) # number=8 prog += Y(2) # number=9 # circuit end return prog def summrise_results(bitstrings) -> dict: d = {} for l in bitstrings: if d.get(l) is None: d[l] = 1 else: d[l] = d[l] + 1 return d if __name__ == '__main__': prog = make_circuit() qvm = get_qc('4q-qvm') results = qvm.run_and_measure(prog,1024) bitstrings = np.vstack([results[i] for i in qvm.qubits()]).T bitstrings = [''.join(map(str, l)) for l in bitstrings] writefile = open("../data/startPyquil330.csv","w") print(summrise_results(bitstrings),file=writefile) writefile.close()

22.326531

64

0.608775

import pyquil from pyquil.api import local_forest_runtime, QVMConnection from pyquil import Program, get_qc from pyquil.gates import * import numpy as np conn = QVMConnection() def make_circuit()-> Program: prog = Program() prog += H(1) prog += H(2) prog += H(3) prog += Y(3) prog += SWAP(1,0) prog += Y(2) prog += Y(2) return prog def summrise_results(bitstrings) -> dict: d = {} for l in bitstrings: if d.get(l) is None: d[l] = 1 else: d[l] = d[l] + 1 return d if __name__ == '__main__': prog = make_circuit() qvm = get_qc('4q-qvm') results = qvm.run_and_measure(prog,1024) bitstrings = np.vstack([results[i] for i in qvm.qubits()]).T bitstrings = [''.join(map(str, l)) for l in bitstrings] writefile = open("../data/startPyquil330.csv","w") print(summrise_results(bitstrings),file=writefile) writefile.close()

true

f71ff98e011cc1d66aab1506e6db05f161b7b1cb

150

py

Python

src/blog/views.py

master-stm/blog-dhango-ar

dd904e2af9bc6b7f85da6063f2abcaf12d572b47

[ "bzip2-1.0.6" ]

null

src/blog/views.py

master-stm/blog-dhango-ar

dd904e2af9bc6b7f85da6063f2abcaf12d572b47

[ "bzip2-1.0.6" ]

null

src/blog/views.py

master-stm/blog-dhango-ar

dd904e2af9bc6b7f85da6063f2abcaf12d572b47

[ "bzip2-1.0.6" ]

null

from django.shortcuts import render # Create your views here. def home(request): return render(request, 'blog/index.html', {'title': 'Home'})

16.666667

64

0.7

from django.shortcuts import render def home(request): return render(request, 'blog/index.html', {'title': 'Home'})

true

f71ffa32ecb22bbfb515cf38fa8b15f86b7fb720

5,517

py

Python

atron_cli/atron.py

atron-cc/atron

49244fbd5ca3d372f6e3e74cda388ddea3acf00e

[ "MIT" ]

4

2019-05-11T01:21:15.000Z

2020-02-08T18:00:39.000Z

atron_cli/atron.py

atron-cc/atron

49244fbd5ca3d372f6e3e74cda388ddea3acf00e

[ "MIT" ]

null

atron_cli/atron.py

atron-cc/atron

49244fbd5ca3d372f6e3e74cda388ddea3acf00e

[ "MIT" ]

null

import click import time import platform import os from minifier import minify from .board import Board, BoardException, DirectoryExistsError from .board import PyboardError _board = None @click.group() @click.option( "--port", "-p", envvar="ATRON_PORT", default="", type=click.STRING, help="Name of serial port for connected board. Can optionally specify with ATRON_PORT environment variable.", metavar="PORT", ) @click.option( "--baud", "-b", envvar="ATRON_BAUD", default=115200, type=click.INT, help="Baud rate for the serial connection (default 115200). Can optionally specify with ATRON_BAUD environment variable.", metavar="BAUD", ) @click.version_option() def cli(port, baud): global _board if platform.system() == "Windows": if port == '': click.secho('you have to choose a COM port.', bold=True, fg='red') return if not re.match("^COM(\d+)$", port): click.secho('invalid port {}'.format(port), fg='red') return else: if port == '': port = '/dev/ttyUSB0' seconds = 1 while True: try: _board = Board(port, baud) break except BoardException as error: click.secho(str(error), bold=True, fg='yellow') click.secho( 'reonnecting to board after {} seconds. press ctrl+c to cancel'.format(seconds), fg='green') time.sleep(seconds) seconds *= 2 @cli.command() @click.option( "-h", "--hard", "hard", is_flag=True, default=False, help="Perform a hard reboot, including running init.py", ) def reset(hard): if not hard: _board.soft_reset() return # TODO: Hard reset is not implemented. @cli.command() def raw_command(): click.secho( 'the raw-command is under construction and may have some bugs.', fg='yellow') click.secho('entering raw-command mode ...', fg='green') _board.soft_reset() time.sleep(1) _board.board.enter_raw_repl() try: while True: command = raw_input(">>> ") result = _board.board.exec_raw(command) if result[0]: print(result[0]) finally: _board.board.exit_raw_repl() _board.soft_reset() @cli.command() @click.argument("remote_folder") def rmdir(remote_folder): _board.files.rmdir(remote_folder) @cli.command() @click.argument( "local", default="main.py", ) @click.argument( "remote", default="main.py", ) def upload(local, remote): if remote is None: remote = os.path.basename(os.path.abspath(local)) _board.files.put(remote, minify(local)) @cli.command() @click.argument( "local", default="main.py", ) @click.argument( "remote", required=False, ) def put(local, remote): if remote is None: remote = os.path.basename(os.path.abspath(local)) if os.path.isdir(local): board_files = _board.files for parent, child_dirs, child_files in os.walk(local): remote_parent = posixpath.normpath( posixpath.join(remote, os.path.relpath(parent, local)) ) try: board_files.mkdir(remote_parent) for filename in child_files: with open(os.path.join(parent, filename), "rb") as infile: remote_filename = posixpath.join( remote_parent, filename) board_files.put(remote_filename, infile.read()) except DirectoryExistsError: pass else: with open(local, "rb") as infile: _board.files.put(remote, infile.read()) @cli.command() @click.argument("remote_file") def rm(remote_file): _board.files.rm(remote_file) @cli.command() @click.argument("local_file") @click.option( "--no-output", "-n", is_flag=True, help="Run the code without waiting for it to finish and print output. Use this when running code with main loops that never return.", ) def run(local_file, no_output): try: output = _board.files.run(local_file, not no_output) if output is not None: click.secho(output.decode("utf-8")) except IOError: click.echo( "Failed to find or read input file: {0}".format(local_file), err=True ) @cli.command() @click.argument("directory", default="/") @click.option( "--long_format", "-l", is_flag=True, help="Print long format info including size of files. Note the size of directories is not supported and will show 0 values.", ) @click.option( "--recursive", "-r", is_flag=True, help="recursively list all files and (empty) directories.", ) def ls(directory, long_format, recursive): try: files = _board.files.ls(directory, long_format=long_format, recursive=recursive) except PyboardError as err: click.secho('PyBoard Exception.', fg='red') click.secho(str(err), fg='yellow') return for f in files: if not long_format: click.secho( f, fg='green' if os.path.splitext(f)[1].lower() == '.py' else 'white', ) else: click.echo(f) if __name__ == '__main__': try: cli() finally: if _board is not None: try: _board.close() except: pass

25.780374

138

0.589813

import click import time import platform import os from minifier import minify from .board import Board, BoardException, DirectoryExistsError from .board import PyboardError _board = None @click.group() @click.option( "--port", "-p", envvar="ATRON_PORT", default="", type=click.STRING, help="Name of serial port for connected board. Can optionally specify with ATRON_PORT environment variable.", metavar="PORT", ) @click.option( "--baud", "-b", envvar="ATRON_BAUD", default=115200, type=click.INT, help="Baud rate for the serial connection (default 115200). Can optionally specify with ATRON_BAUD environment variable.", metavar="BAUD", ) @click.version_option() def cli(port, baud): global _board if platform.system() == "Windows": if port == '': click.secho('you have to choose a COM port.', bold=True, fg='red') return if not re.match("^COM(\d+)$", port): click.secho('invalid port {}'.format(port), fg='red') return else: if port == '': port = '/dev/ttyUSB0' seconds = 1 while True: try: _board = Board(port, baud) break except BoardException as error: click.secho(str(error), bold=True, fg='yellow') click.secho( 'reonnecting to board after {} seconds. press ctrl+c to cancel'.format(seconds), fg='green') time.sleep(seconds) seconds *= 2 @cli.command() @click.option( "-h", "--hard", "hard", is_flag=True, default=False, help="Perform a hard reboot, including running init.py", ) def reset(hard): if not hard: _board.soft_reset() return @cli.command() def raw_command(): click.secho( 'the raw-command is under construction and may have some bugs.', fg='yellow') click.secho('entering raw-command mode ...', fg='green') _board.soft_reset() time.sleep(1) _board.board.enter_raw_repl() try: while True: command = raw_input(">>> ") result = _board.board.exec_raw(command) if result[0]: print(result[0]) finally: _board.board.exit_raw_repl() _board.soft_reset() @cli.command() @click.argument("remote_folder") def rmdir(remote_folder): _board.files.rmdir(remote_folder) @cli.command() @click.argument( "local", default="main.py", ) @click.argument( "remote", default="main.py", ) def upload(local, remote): if remote is None: remote = os.path.basename(os.path.abspath(local)) _board.files.put(remote, minify(local)) @cli.command() @click.argument( "local", default="main.py", ) @click.argument( "remote", required=False, ) def put(local, remote): if remote is None: remote = os.path.basename(os.path.abspath(local)) if os.path.isdir(local): board_files = _board.files for parent, child_dirs, child_files in os.walk(local): remote_parent = posixpath.normpath( posixpath.join(remote, os.path.relpath(parent, local)) ) try: board_files.mkdir(remote_parent) for filename in child_files: with open(os.path.join(parent, filename), "rb") as infile: remote_filename = posixpath.join( remote_parent, filename) board_files.put(remote_filename, infile.read()) except DirectoryExistsError: pass else: with open(local, "rb") as infile: _board.files.put(remote, infile.read()) @cli.command() @click.argument("remote_file") def rm(remote_file): _board.files.rm(remote_file) @cli.command() @click.argument("local_file") @click.option( "--no-output", "-n", is_flag=True, help="Run the code without waiting for it to finish and print output. Use this when running code with main loops that never return.", ) def run(local_file, no_output): try: output = _board.files.run(local_file, not no_output) if output is not None: click.secho(output.decode("utf-8")) except IOError: click.echo( "Failed to find or read input file: {0}".format(local_file), err=True ) @cli.command() @click.argument("directory", default="/") @click.option( "--long_format", "-l", is_flag=True, help="Print long format info including size of files. Note the size of directories is not supported and will show 0 values.", ) @click.option( "--recursive", "-r", is_flag=True, help="recursively list all files and (empty) directories.", ) def ls(directory, long_format, recursive): try: files = _board.files.ls(directory, long_format=long_format, recursive=recursive) except PyboardError as err: click.secho('PyBoard Exception.', fg='red') click.secho(str(err), fg='yellow') return for f in files: if not long_format: click.secho( f, fg='green' if os.path.splitext(f)[1].lower() == '.py' else 'white', ) else: click.echo(f) if __name__ == '__main__': try: cli() finally: if _board is not None: try: _board.close() except: pass

true

f71ffa36ec9cfc1d58a5f56d219d392194ca7a79

331

py

Python

Projetos/surf05.py

anderson-br-ti/python

d65d851f0934267dff9256dfdac09b100efb3b45

[ "MIT" ]

null

Projetos/surf05.py

anderson-br-ti/python

d65d851f0934267dff9256dfdac09b100efb3b45

[ "MIT" ]

null

Projetos/surf05.py

anderson-br-ti/python

d65d851f0934267dff9256dfdac09b100efb3b45

[ "MIT" ]

null

f = open('surf.txt') notas = [] nomes = [] for linha in f: nome, pontos = linha.split() notas.append(float(pontos)) nomes.append(nome) f.close() notas.sort(reverse=True) nomes.sort(reverse=True) print ('%s %4.2f' %(nomes[0], notas[0])) print ('%s %4.2f' %(nomes[1], notas[1])) print ('%s %4.2f' %(nomes[2], notas[2]))

22.066667

40

0.601208

f = open('surf.txt') notas = [] nomes = [] for linha in f: nome, pontos = linha.split() notas.append(float(pontos)) nomes.append(nome) f.close() notas.sort(reverse=True) nomes.sort(reverse=True) print ('%s %4.2f' %(nomes[0], notas[0])) print ('%s %4.2f' %(nomes[1], notas[1])) print ('%s %4.2f' %(nomes[2], notas[2]))

true

f71ffa96a121e4b599332fda00ecec1c3e395215

381

py

Python

sayhello/__init__.py

IshunChin/cfn-tutorial

0a282fe3a2affa60c5c46702206128bb19e60869

[ "MIT" ]

null

sayhello/__init__.py

IshunChin/cfn-tutorial

0a282fe3a2affa60c5c46702206128bb19e60869

[ "MIT" ]

null

sayhello/__init__.py

IshunChin/cfn-tutorial

0a282fe3a2affa60c5c46702206128bb19e60869

[ "MIT" ]

null

from flask import Flask from flask_bootstrap import Bootstrap from flask_moment import Moment from flask_sqlalchemy import SQLAlchemy app = Flask('sayhello') app.config.from_pyfile('settings.py') app.jinja_env.trim_blocks = True app.jinja_env.lstrip_blocks = True db = SQLAlchemy(app) bootstrap = Bootstrap(app) moment = Moment(app) from sayhello import views, errors, commands

23.8125

44

0.808399

from flask import Flask from flask_bootstrap import Bootstrap from flask_moment import Moment from flask_sqlalchemy import SQLAlchemy app = Flask('sayhello') app.config.from_pyfile('settings.py') app.jinja_env.trim_blocks = True app.jinja_env.lstrip_blocks = True db = SQLAlchemy(app) bootstrap = Bootstrap(app) moment = Moment(app) from sayhello import views, errors, commands

true

f71ffb327a8627122eb965383d8d9493e4611a68

23,768

py

Python

examples/sentence_similarity/gensen_train.py

gohanlon/nlp

7b07109a2066eb2152c370ef38600230668a9c8d

[ "MIT" ]

4,407

2019-10-29T21:35:19.000Z

2022-03-31T13:56:37.000Z

examples/sentence_similarity/gensen_train.py

shubham9g17/nlp-recipes

a5cd2303187239799ae0b1597a7c16eb99a97108

[ "MIT" ]

134

2019-10-30T23:38:59.000Z

2022-03-01T11:42:53.000Z

examples/sentence_similarity/gensen_train.py

shubham9g17/nlp-recipes

a5cd2303187239799ae0b1597a7c16eb99a97108

[ "MIT" ]

726

2019-10-31T15:21:52.000Z

2022-03-31T10:18:22.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. """ The GenSen training process follows the steps: 1. Create or load the dataset vocabulary 2. Train on the training dataset for each batch epoch (batch size = 48 updates) 3. Evaluate on the validation dataset for every 10 epoches 4. Find the local minimum point on validation loss 5. Save the best model and stop the training process AzureML provides AI Compute to train the model and track the performance. This training process is based on GPU only. """ import argparse import json import logging import os import time import horovod.torch as hvd import mlflow import numpy as np import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.functional as f import torch.optim as optim from utils_nlp.models.gensen.multi_task_model import MultitaskModel from utils_nlp.models.gensen.utils import ( BufferedDataIterator, NLIIterator, compute_validation_loss, ) cudnn.benchmark = True logger = logging.getLogger(__name__) hvd.init() if torch.cuda.is_available(): # Horovod: pin GPU to local rank. torch.cuda.set_device(hvd.local_rank()) def metric_average(value, name): """ Sync the validation loss with nodes. :param value: :param name: :return: """ tensor = torch.tensor(value) avg_tensor = hvd.allreduce(tensor, name=name) return avg_tensor.item() def setup_horovod(model, learning_rate): """ Setup for Horovod usage. Args: model(MultitaskModel): The MultitaskModel object. learning_rate(float): Learning rate for the model. Returns: hvd.DistributedOptimizer: Optimizer to use for computing gradients and applying updates. """ # Horovod: scale learning rate by the number of GPUs. optimizer = optim.Adam(model.parameters(), lr=learning_rate * hvd.size()) # Horovod: broadcast parameters & optimizer state. hvd.broadcast_parameters(model.state_dict(), root_rank=0) hvd.broadcast_optimizer_state(optimizer, root_rank=0) # Horovod: (optional) compression algorithm. compression = hvd.Compression.fp16 # Horovod: wrap optimizer with DistributedOptimizer. optimizer = hvd.DistributedOptimizer( optimizer, named_parameters=model.named_parameters(), compression=compression, ) return optimizer def setup_logging(config): logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", filename="log/%s" % (config["data"]["task"]), filemode="w", ) console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") console.setFormatter(formatter) logging.getLogger("").addHandler(console) def log_config(config): logging.info("Model Parameters : ") logging.info("Task : %s " % (config["data"]["task"])) logging.info( "Source Word Embedding Dim : %s" % (config["model"]["dim_word_src"]) ) logging.info( "Target Word Embedding Dim : %s" % (config["model"]["dim_word_trg"]) ) logging.info("Source RNN Hidden Dim : %s" % (config["model"]["dim_src"])) logging.info("Target RNN Hidden Dim : %s" % (config["model"]["dim_trg"])) logging.info( "Source RNN Bidirectional : %s" % (config["model"]["bidirectional"]) ) logging.info("Batch Size : %d " % (config["training"]["batch_size"])) logging.info("Optimizer : %s " % (config["training"]["optimizer"])) logging.info("Learning Rate : %f " % (config["training"]["lrate"])) def evaluate( config, train_iterator, model, loss_criterion, monitor_epoch, min_val_loss, min_val_loss_epoch, save_dir, starting_time, model_state, max_epoch, ): """ Function to validate the model. Args: max_epoch(int): Limit training to specified number of epochs. model_state(dict): Saved model weights. config(dict): Config object. train_iterator(BufferedDataIterator): BufferedDataIterator object. model(MultitaskModel): The MultitaskModel object. loss_criterion(nn.CrossEntropyLoss): Cross entropy loss. monitor_epoch(int): Current epoch count. min_val_loss(float): Minimum validation loss min_val_loss_epoch(int): Epoch where the minimum validation loss was seen. save_dir(str): Directory path to save the model dictionary. starting_time(time.Time): Starting time of the training. Returns: bool: Whether to continue training or not. """ break_flag = 0 for task_idx, task in enumerate(train_iterator.tasknames): if "skipthought" in task: continue validation_loss = compute_validation_loss( config, model, train_iterator, loss_criterion, task_idx, lowercase=True, ) validation_loss = metric_average(validation_loss, "val_loss") logging.info("%s Validation Loss : %.3f" % (task, validation_loss)) # Horovod: print output only on first rank. if hvd.rank() == 0: # log the best val accuracy to AML run logging.info( "Best Validation Loss: {}".format(np.float(validation_loss)) ) # If the validation loss is small enough, and it starts to go up. # Should stop training. # Small is defined by the number of epochs it lasts. if validation_loss < min_val_loss: min_val_loss = validation_loss min_val_loss_epoch = monitor_epoch model_state = model.state_dict() logging.info( "Monitor epoch: %d Validation Loss: %.3f Min Validation Epoch: " "%d Loss : %.3f " % ( monitor_epoch, validation_loss, min_val_loss_epoch, min_val_loss, ) ) if (monitor_epoch - min_val_loss_epoch) > config["training"][ "stop_patience" ] or (max_epoch is not None and monitor_epoch >= max_epoch): logging.info("Saving model ...") # Save the name with validation loss. torch.save( model_state, open(os.path.join(save_dir, "best_model.model"), "wb"), ) # Let the training end. break_flag = 1 break if break_flag == 1: logging.info("##### Training stopped at ##### %f" % min_val_loss) logging.info( "##### Training Time ##### %f seconds" % (time.time() - starting_time) ) return True, min_val_loss_epoch, min_val_loss, model_state else: return False, min_val_loss_epoch, min_val_loss, model_state def evaluate_nli(nli_iterator, model, batch_size, n_gpus): """ Args: nli_iterator(NLIIterator): NLIIterator object. model(MultitaskModel): Multitask model object. batch_size(int): Batch size. n_gpus(int): Number of gpus """ n_correct = 0.0 n_wrong = 0.0 for j in range(0, len(nli_iterator.dev_lines), batch_size * n_gpus): minibatch = nli_iterator.get_parallel_minibatch( j, batch_size * n_gpus, "dev" ) class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) class_preds = ( f.softmax(class_logits).data.cpu().numpy().argmax(axis=-1) ) labels = minibatch["labels"].data.cpu().numpy() for pred, label in zip(class_preds, labels): if pred == label: n_correct += 1.0 else: n_wrong += 1.0 logging.info("NLI Dev Acc : %.5f" % (n_correct / (n_correct + n_wrong))) n_correct = 0.0 n_wrong = 0.0 for j in range(0, len(nli_iterator.test_lines), batch_size * n_gpus): minibatch = nli_iterator.get_parallel_minibatch( j, batch_size * n_gpus, "test" ) class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) class_preds = ( f.softmax(class_logits).data.cpu().numpy().argmax(axis=-1) ) labels = minibatch["labels"].data.cpu().numpy() for pred, label in zip(class_preds, labels): if pred == label: n_correct += 1.0 else: n_wrong += 1.0 logging.info("NLI Test Acc : %.5f" % (n_correct / (n_correct + n_wrong))) logging.info("******************************************************") def train(config, data_folder, learning_rate=0.0001, max_epoch=None): """ Train the Gensen model. Args: max_epoch(int): Limit training to specified number of epochs. config(dict): Loaded json file as a python object. data_folder(str): Path to the folder containing the data. learning_rate(float): Learning rate for the model. """ owd = os.getcwd() os.chdir(data_folder) try: with mlflow.start_run(): save_dir = config["data"]["save_dir"] if not os.path.exists("./log"): os.makedirs("./log") os.makedirs(save_dir, exist_ok=True) setup_logging(config) batch_size = config["training"]["batch_size"] src_vocab_size = config["model"]["n_words_src"] trg_vocab_size = config["model"]["n_words_trg"] max_len_src = config["data"]["max_src_length"] max_len_trg = config["data"]["max_trg_length"] model_state = {} train_src = [item["train_src"] for item in config["data"]["paths"]] train_trg = [item["train_trg"] for item in config["data"]["paths"]] tasknames = [item["taskname"] for item in config["data"]["paths"]] # Keep track of indicies to train forward and backward jointly if ( "skipthought_next" in tasknames and "skipthought_previous" in tasknames ): skipthought_idx = tasknames.index("skipthought_next") skipthought_backward_idx = tasknames.index( "skipthought_previous" ) paired_tasks = { skipthought_idx: skipthought_backward_idx, skipthought_backward_idx: skipthought_idx, } else: paired_tasks = None skipthought_idx = None skipthought_backward_idx = None train_iterator = BufferedDataIterator( train_src, train_trg, src_vocab_size, trg_vocab_size, tasknames, save_dir, buffer_size=1e6, lowercase=True, seed=(hvd.rank() + 1) * 12345, ) nli_iterator = NLIIterator( train=config["data"]["nli_train"], dev=config["data"]["nli_dev"], test=config["data"]["nli_test"], vocab_size=-1, vocab=os.path.join(save_dir, "src_vocab.pkl"), seed=(hvd.rank() + 1) * 12345, ) src_vocab_size = len(train_iterator.src[0]["word2id"]) trg_vocab_size = len(train_iterator.trg[0]["word2id"]) # Logging set up. logging.info("Finished creating iterator ...") log_config(config) logging.info( "Found %d words in source : " % (len(train_iterator.src[0]["id2word"])) ) for idx, taskname in enumerate(tasknames): logging.info( "Found %d target words in task %s " % (len(train_iterator.trg[idx]["id2word"]), taskname) ) logging.info("Found %d words in src " % src_vocab_size) logging.info("Found %d words in trg " % trg_vocab_size) weight_mask = torch.ones(trg_vocab_size).cuda() weight_mask[train_iterator.trg[0]["word2id"]["<pad>"]] = 0 loss_criterion = nn.CrossEntropyLoss(weight=weight_mask).cuda() nli_criterion = nn.CrossEntropyLoss().cuda() model = MultitaskModel( src_emb_dim=config["model"]["dim_word_src"], trg_emb_dim=config["model"]["dim_word_trg"], src_vocab_size=src_vocab_size, trg_vocab_size=trg_vocab_size, src_hidden_dim=config["model"]["dim_src"], trg_hidden_dim=config["model"]["dim_trg"], bidirectional=config["model"]["bidirectional"], pad_token_src=train_iterator.src[0]["word2id"]["<pad>"], pad_token_trg=train_iterator.trg[0]["word2id"]["<pad>"], nlayers_src=config["model"]["n_layers_src"], dropout=config["model"]["dropout"], num_tasks=len(train_iterator.src), paired_tasks=paired_tasks, ).cuda() optimizer = setup_horovod(model, learning_rate=learning_rate) logging.info(model) n_gpus = config["training"]["n_gpus"] model = torch.nn.DataParallel(model, device_ids=range(n_gpus)) task_losses = [[] for _ in tasknames] task_idxs = [0 for _ in tasknames] nli_losses = [] updates = 0 nli_ctr = 0 nli_epoch = 0 monitor_epoch = 0 nli_mbatch_ctr = 0 mbatch_times = [] min_val_loss = 10000000 min_val_loss_epoch = -1 rng_num_tasks = ( len(tasknames) - 1 if paired_tasks else len(tasknames) ) logging.info("OS Environ: \n {} \n\n".format(os.environ)) mlflow.log_param("learning_rate", learning_rate) logging.info("Commencing Training ...") start = time.time() while True: batch_start_time = time.time() # Train NLI once every 10 minibatches of other tasks if nli_ctr % 10 == 0: minibatch = nli_iterator.get_parallel_minibatch( nli_mbatch_ctr, batch_size * n_gpus ) optimizer.zero_grad() class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) loss = nli_criterion( class_logits.contiguous().view( -1, class_logits.size(1) ), minibatch["labels"].contiguous().view(-1), ) # nli_losses.append(loss.data[0]) nli_losses.append(loss.item()) loss.backward() torch.nn.utils.clip_grad_norm(model.parameters(), 1.0) optimizer.step() nli_mbatch_ctr += batch_size * n_gpus if nli_mbatch_ctr >= len(nli_iterator.train_lines): nli_mbatch_ctr = 0 nli_epoch += 1 else: # Sample a random task task_idx = np.random.randint(low=0, high=rng_num_tasks) # Get a minibatch corresponding to the sampled task minibatch = train_iterator.get_parallel_minibatch( task_idx, task_idxs[task_idx], batch_size * n_gpus, max_len_src, max_len_trg, ) """Increment pointer into task and if current buffer is exhausted, fetch new buffer. """ task_idxs[task_idx] += batch_size * n_gpus if task_idxs[task_idx] >= train_iterator.buffer_size: train_iterator.fetch_buffer(task_idx) task_idxs[task_idx] = 0 if task_idx == skipthought_idx: minibatch_back = train_iterator.get_parallel_minibatch( skipthought_backward_idx, task_idxs[skipthought_backward_idx], batch_size * n_gpus, max_len_src, max_len_trg, ) task_idxs[skipthought_backward_idx] += ( batch_size * n_gpus ) if ( task_idxs[skipthought_backward_idx] >= train_iterator.buffer_size ): train_iterator.fetch_buffer( skipthought_backward_idx ) task_idxs[skipthought_backward_idx] = 0 optimizer.zero_grad() decoder_logit, decoder_logit_2 = model( minibatch, task_idx, paired_trg=minibatch_back["input_trg"], ) loss_f = loss_criterion( decoder_logit.contiguous().view( -1, decoder_logit.size(2) ), minibatch["output_trg"].contiguous().view(-1), ) loss_b = loss_criterion( decoder_logit_2.contiguous().view( -1, decoder_logit_2.size(2) ), minibatch_back["output_trg"].contiguous().view(-1), ) task_losses[task_idx].append(loss_f.data[0]) task_losses[skipthought_backward_idx].append( loss_b.data[0] ) loss = loss_f + loss_b else: optimizer.zero_grad() decoder_logit = model(minibatch, task_idx) loss = loss_criterion( decoder_logit.contiguous().view( -1, decoder_logit.size(2) ), minibatch["output_trg"].contiguous().view(-1), ) task_losses[task_idx].append(loss.item()) loss.backward() # For distributed optimizer need to sync before gradient # clipping. optimizer.synchronize() torch.nn.utils.clip_grad_norm(model.parameters(), 1.0) optimizer.step() end = time.time() mbatch_times.append(end - batch_start_time) # Validations if ( updates % config["management"]["monitor_loss"] == 0 and updates != 0 ): monitor_epoch += 1 for idx, task in enumerate(tasknames): logging.info( "Seq2Seq Examples Processed : %d %s Loss : %.5f Num %s " "minibatches : %d" % ( updates, task, np.mean(task_losses[idx]), task, len(task_losses[idx]), ) ) mlflow.log_metric( "validation_loss", np.mean(task_losses[idx]), step=monitor_epoch, ) logging.info( "Round: %d NLI Epoch : %d NLI Examples Processed : %d NLI " "Loss : %.5f " % ( nli_ctr, nli_epoch, nli_mbatch_ctr, np.mean(nli_losses), ) ) mlflow.log_metric( "nli_loss", np.mean(nli_losses), step=nli_epoch ) logging.info( "Average time per minibatch : %.5f" % (np.mean(mbatch_times)) ) mlflow.log_metric( "minibatch_avg_duration", np.mean(mbatch_times) ) task_losses = [[] for _ in tasknames] mbatch_times = [] nli_losses = [] # For validate and break if done. logging.info("############################") logging.info("##### Evaluating model #####") logging.info("############################") training_complete, min_val_loss_epoch, min_val_loss, model_state = evaluate( config=config, train_iterator=train_iterator, model=model, loss_criterion=loss_criterion, monitor_epoch=monitor_epoch, min_val_loss=min_val_loss, min_val_loss_epoch=min_val_loss_epoch, save_dir=save_dir, starting_time=start, model_state=model_state, max_epoch=max_epoch, ) if training_complete: mlflow.log_metric("min_val_loss", float(min_val_loss)) mlflow.log_metric("learning_rate", learning_rate) break logging.info("Evaluating on NLI") evaluate_nli( nli_iterator=nli_iterator, model=model, n_gpus=n_gpus, batch_size=batch_size, ) updates += batch_size * n_gpus nli_ctr += 1 logging.info("Updates: %d" % updates) finally: os.chdir(owd) def read_config(json_file): """Read JSON config.""" json_object = json.load(open(json_file, "r", encoding="utf-8")) return json_object if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", help="path to json config", required=True) parser.add_argument("--data_folder", type=str, help="data folder") # Add learning rate to tune model. parser.add_argument( "--learning_rate", type=float, default=0.0001, help="learning rate" ) parser.add_argument( "--max_epoch", type=int, default=None, help="Limit training to specified number of epochs.", ) args = parser.parse_args() data_path = args.data_folder lr = args.learning_rate config_file_path = args.config max_epoch = args.max_epoch config_obj = read_config(config_file_path) train(config_obj, data_path, lr, max_epoch)

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import argparse import json import logging import os import time import horovod.torch as hvd import mlflow import numpy as np import torch import torch.backends.cudnn as cudnn import torch.nn as nn import torch.nn.functional as f import torch.optim as optim from utils_nlp.models.gensen.multi_task_model import MultitaskModel from utils_nlp.models.gensen.utils import ( BufferedDataIterator, NLIIterator, compute_validation_loss, ) cudnn.benchmark = True logger = logging.getLogger(__name__) hvd.init() if torch.cuda.is_available(): torch.cuda.set_device(hvd.local_rank()) def metric_average(value, name): tensor = torch.tensor(value) avg_tensor = hvd.allreduce(tensor, name=name) return avg_tensor.item() def setup_horovod(model, learning_rate): optimizer = optim.Adam(model.parameters(), lr=learning_rate * hvd.size()) hvd.broadcast_parameters(model.state_dict(), root_rank=0) hvd.broadcast_optimizer_state(optimizer, root_rank=0) compression = hvd.Compression.fp16 optimizer = hvd.DistributedOptimizer( optimizer, named_parameters=model.named_parameters(), compression=compression, ) return optimizer def setup_logging(config): logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", filename="log/%s" % (config["data"]["task"]), filemode="w", ) console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s") console.setFormatter(formatter) logging.getLogger("").addHandler(console) def log_config(config): logging.info("Model Parameters : ") logging.info("Task : %s " % (config["data"]["task"])) logging.info( "Source Word Embedding Dim : %s" % (config["model"]["dim_word_src"]) ) logging.info( "Target Word Embedding Dim : %s" % (config["model"]["dim_word_trg"]) ) logging.info("Source RNN Hidden Dim : %s" % (config["model"]["dim_src"])) logging.info("Target RNN Hidden Dim : %s" % (config["model"]["dim_trg"])) logging.info( "Source RNN Bidirectional : %s" % (config["model"]["bidirectional"]) ) logging.info("Batch Size : %d " % (config["training"]["batch_size"])) logging.info("Optimizer : %s " % (config["training"]["optimizer"])) logging.info("Learning Rate : %f " % (config["training"]["lrate"])) def evaluate( config, train_iterator, model, loss_criterion, monitor_epoch, min_val_loss, min_val_loss_epoch, save_dir, starting_time, model_state, max_epoch, ): break_flag = 0 for task_idx, task in enumerate(train_iterator.tasknames): if "skipthought" in task: continue validation_loss = compute_validation_loss( config, model, train_iterator, loss_criterion, task_idx, lowercase=True, ) validation_loss = metric_average(validation_loss, "val_loss") logging.info("%s Validation Loss : %.3f" % (task, validation_loss)) if hvd.rank() == 0: logging.info( "Best Validation Loss: {}".format(np.float(validation_loss)) ) if validation_loss < min_val_loss: min_val_loss = validation_loss min_val_loss_epoch = monitor_epoch model_state = model.state_dict() logging.info( "Monitor epoch: %d Validation Loss: %.3f Min Validation Epoch: " "%d Loss : %.3f " % ( monitor_epoch, validation_loss, min_val_loss_epoch, min_val_loss, ) ) if (monitor_epoch - min_val_loss_epoch) > config["training"][ "stop_patience" ] or (max_epoch is not None and monitor_epoch >= max_epoch): logging.info("Saving model ...") torch.save( model_state, open(os.path.join(save_dir, "best_model.model"), "wb"), ) break_flag = 1 break if break_flag == 1: logging.info("##### Training stopped at ##### %f" % min_val_loss) logging.info( "##### Training Time ##### %f seconds" % (time.time() - starting_time) ) return True, min_val_loss_epoch, min_val_loss, model_state else: return False, min_val_loss_epoch, min_val_loss, model_state def evaluate_nli(nli_iterator, model, batch_size, n_gpus): n_correct = 0.0 n_wrong = 0.0 for j in range(0, len(nli_iterator.dev_lines), batch_size * n_gpus): minibatch = nli_iterator.get_parallel_minibatch( j, batch_size * n_gpus, "dev" ) class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) class_preds = ( f.softmax(class_logits).data.cpu().numpy().argmax(axis=-1) ) labels = minibatch["labels"].data.cpu().numpy() for pred, label in zip(class_preds, labels): if pred == label: n_correct += 1.0 else: n_wrong += 1.0 logging.info("NLI Dev Acc : %.5f" % (n_correct / (n_correct + n_wrong))) n_correct = 0.0 n_wrong = 0.0 for j in range(0, len(nli_iterator.test_lines), batch_size * n_gpus): minibatch = nli_iterator.get_parallel_minibatch( j, batch_size * n_gpus, "test" ) class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) class_preds = ( f.softmax(class_logits).data.cpu().numpy().argmax(axis=-1) ) labels = minibatch["labels"].data.cpu().numpy() for pred, label in zip(class_preds, labels): if pred == label: n_correct += 1.0 else: n_wrong += 1.0 logging.info("NLI Test Acc : %.5f" % (n_correct / (n_correct + n_wrong))) logging.info("******************************************************") def train(config, data_folder, learning_rate=0.0001, max_epoch=None): owd = os.getcwd() os.chdir(data_folder) try: with mlflow.start_run(): save_dir = config["data"]["save_dir"] if not os.path.exists("./log"): os.makedirs("./log") os.makedirs(save_dir, exist_ok=True) setup_logging(config) batch_size = config["training"]["batch_size"] src_vocab_size = config["model"]["n_words_src"] trg_vocab_size = config["model"]["n_words_trg"] max_len_src = config["data"]["max_src_length"] max_len_trg = config["data"]["max_trg_length"] model_state = {} train_src = [item["train_src"] for item in config["data"]["paths"]] train_trg = [item["train_trg"] for item in config["data"]["paths"]] tasknames = [item["taskname"] for item in config["data"]["paths"]] if ( "skipthought_next" in tasknames and "skipthought_previous" in tasknames ): skipthought_idx = tasknames.index("skipthought_next") skipthought_backward_idx = tasknames.index( "skipthought_previous" ) paired_tasks = { skipthought_idx: skipthought_backward_idx, skipthought_backward_idx: skipthought_idx, } else: paired_tasks = None skipthought_idx = None skipthought_backward_idx = None train_iterator = BufferedDataIterator( train_src, train_trg, src_vocab_size, trg_vocab_size, tasknames, save_dir, buffer_size=1e6, lowercase=True, seed=(hvd.rank() + 1) * 12345, ) nli_iterator = NLIIterator( train=config["data"]["nli_train"], dev=config["data"]["nli_dev"], test=config["data"]["nli_test"], vocab_size=-1, vocab=os.path.join(save_dir, "src_vocab.pkl"), seed=(hvd.rank() + 1) * 12345, ) src_vocab_size = len(train_iterator.src[0]["word2id"]) trg_vocab_size = len(train_iterator.trg[0]["word2id"]) logging.info("Finished creating iterator ...") log_config(config) logging.info( "Found %d words in source : " % (len(train_iterator.src[0]["id2word"])) ) for idx, taskname in enumerate(tasknames): logging.info( "Found %d target words in task %s " % (len(train_iterator.trg[idx]["id2word"]), taskname) ) logging.info("Found %d words in src " % src_vocab_size) logging.info("Found %d words in trg " % trg_vocab_size) weight_mask = torch.ones(trg_vocab_size).cuda() weight_mask[train_iterator.trg[0]["word2id"]["<pad>"]] = 0 loss_criterion = nn.CrossEntropyLoss(weight=weight_mask).cuda() nli_criterion = nn.CrossEntropyLoss().cuda() model = MultitaskModel( src_emb_dim=config["model"]["dim_word_src"], trg_emb_dim=config["model"]["dim_word_trg"], src_vocab_size=src_vocab_size, trg_vocab_size=trg_vocab_size, src_hidden_dim=config["model"]["dim_src"], trg_hidden_dim=config["model"]["dim_trg"], bidirectional=config["model"]["bidirectional"], pad_token_src=train_iterator.src[0]["word2id"]["<pad>"], pad_token_trg=train_iterator.trg[0]["word2id"]["<pad>"], nlayers_src=config["model"]["n_layers_src"], dropout=config["model"]["dropout"], num_tasks=len(train_iterator.src), paired_tasks=paired_tasks, ).cuda() optimizer = setup_horovod(model, learning_rate=learning_rate) logging.info(model) n_gpus = config["training"]["n_gpus"] model = torch.nn.DataParallel(model, device_ids=range(n_gpus)) task_losses = [[] for _ in tasknames] task_idxs = [0 for _ in tasknames] nli_losses = [] updates = 0 nli_ctr = 0 nli_epoch = 0 monitor_epoch = 0 nli_mbatch_ctr = 0 mbatch_times = [] min_val_loss = 10000000 min_val_loss_epoch = -1 rng_num_tasks = ( len(tasknames) - 1 if paired_tasks else len(tasknames) ) logging.info("OS Environ: \n {} \n\n".format(os.environ)) mlflow.log_param("learning_rate", learning_rate) logging.info("Commencing Training ...") start = time.time() while True: batch_start_time = time.time() if nli_ctr % 10 == 0: minibatch = nli_iterator.get_parallel_minibatch( nli_mbatch_ctr, batch_size * n_gpus ) optimizer.zero_grad() class_logits = model( minibatch, -1, return_hidden=False, paired_trg=None ) loss = nli_criterion( class_logits.contiguous().view( -1, class_logits.size(1) ), minibatch["labels"].contiguous().view(-1), ) nli_losses.append(loss.item()) loss.backward() torch.nn.utils.clip_grad_norm(model.parameters(), 1.0) optimizer.step() nli_mbatch_ctr += batch_size * n_gpus if nli_mbatch_ctr >= len(nli_iterator.train_lines): nli_mbatch_ctr = 0 nli_epoch += 1 else: task_idx = np.random.randint(low=0, high=rng_num_tasks) minibatch = train_iterator.get_parallel_minibatch( task_idx, task_idxs[task_idx], batch_size * n_gpus, max_len_src, max_len_trg, ) """Increment pointer into task and if current buffer is exhausted, fetch new buffer. """ task_idxs[task_idx] += batch_size * n_gpus if task_idxs[task_idx] >= train_iterator.buffer_size: train_iterator.fetch_buffer(task_idx) task_idxs[task_idx] = 0 if task_idx == skipthought_idx: minibatch_back = train_iterator.get_parallel_minibatch( skipthought_backward_idx, task_idxs[skipthought_backward_idx], batch_size * n_gpus, max_len_src, max_len_trg, ) task_idxs[skipthought_backward_idx] += ( batch_size * n_gpus ) if ( task_idxs[skipthought_backward_idx] >= train_iterator.buffer_size ): train_iterator.fetch_buffer( skipthought_backward_idx ) task_idxs[skipthought_backward_idx] = 0 optimizer.zero_grad() decoder_logit, decoder_logit_2 = model( minibatch, task_idx, paired_trg=minibatch_back["input_trg"], ) loss_f = loss_criterion( decoder_logit.contiguous().view( -1, decoder_logit.size(2) ), minibatch["output_trg"].contiguous().view(-1), ) loss_b = loss_criterion( decoder_logit_2.contiguous().view( -1, decoder_logit_2.size(2) ), minibatch_back["output_trg"].contiguous().view(-1), ) task_losses[task_idx].append(loss_f.data[0]) task_losses[skipthought_backward_idx].append( loss_b.data[0] ) loss = loss_f + loss_b else: optimizer.zero_grad() decoder_logit = model(minibatch, task_idx) loss = loss_criterion( decoder_logit.contiguous().view( -1, decoder_logit.size(2) ), minibatch["output_trg"].contiguous().view(-1), ) task_losses[task_idx].append(loss.item()) loss.backward() optimizer.synchronize() torch.nn.utils.clip_grad_norm(model.parameters(), 1.0) optimizer.step() end = time.time() mbatch_times.append(end - batch_start_time) if ( updates % config["management"]["monitor_loss"] == 0 and updates != 0 ): monitor_epoch += 1 for idx, task in enumerate(tasknames): logging.info( "Seq2Seq Examples Processed : %d %s Loss : %.5f Num %s " "minibatches : %d" % ( updates, task, np.mean(task_losses[idx]), task, len(task_losses[idx]), ) ) mlflow.log_metric( "validation_loss", np.mean(task_losses[idx]), step=monitor_epoch, ) logging.info( "Round: %d NLI Epoch : %d NLI Examples Processed : %d NLI " "Loss : %.5f " % ( nli_ctr, nli_epoch, nli_mbatch_ctr, np.mean(nli_losses), ) ) mlflow.log_metric( "nli_loss", np.mean(nli_losses), step=nli_epoch ) logging.info( "Average time per minibatch : %.5f" % (np.mean(mbatch_times)) ) mlflow.log_metric( "minibatch_avg_duration", np.mean(mbatch_times) ) task_losses = [[] for _ in tasknames] mbatch_times = [] nli_losses = [] logging.info("############################") logging.info("##### Evaluating model #####") logging.info("############################") training_complete, min_val_loss_epoch, min_val_loss, model_state = evaluate( config=config, train_iterator=train_iterator, model=model, loss_criterion=loss_criterion, monitor_epoch=monitor_epoch, min_val_loss=min_val_loss, min_val_loss_epoch=min_val_loss_epoch, save_dir=save_dir, starting_time=start, model_state=model_state, max_epoch=max_epoch, ) if training_complete: mlflow.log_metric("min_val_loss", float(min_val_loss)) mlflow.log_metric("learning_rate", learning_rate) break logging.info("Evaluating on NLI") evaluate_nli( nli_iterator=nli_iterator, model=model, n_gpus=n_gpus, batch_size=batch_size, ) updates += batch_size * n_gpus nli_ctr += 1 logging.info("Updates: %d" % updates) finally: os.chdir(owd) def read_config(json_file): json_object = json.load(open(json_file, "r", encoding="utf-8")) return json_object if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", help="path to json config", required=True) parser.add_argument("--data_folder", type=str, help="data folder") parser.add_argument( "--learning_rate", type=float, default=0.0001, help="learning rate" ) parser.add_argument( "--max_epoch", type=int, default=None, help="Limit training to specified number of epochs.", ) args = parser.parse_args() data_path = args.data_folder lr = args.learning_rate config_file_path = args.config max_epoch = args.max_epoch config_obj = read_config(config_file_path) train(config_obj, data_path, lr, max_epoch)

true

f71ffb8011fd1e0f44abc963af4799e254cbef2d

6,006

py

Python

rb_missions/scripts/acoustic_docking.py

vanttec/vanttec_usv

5c7b45a61728404b4c957028eac7bc361f1b2077

[ "MIT" ]

13

2020-08-18T18:47:11.000Z

2022-03-30T08:07:25.000Z

rb_missions/scripts/acoustic_docking.py

vanttec/vanttec_usv

5c7b45a61728404b4c957028eac7bc361f1b2077

[ "MIT" ]

2

2021-05-07T03:56:11.000Z

2021-08-10T04:18:21.000Z

rb_missions/scripts/acoustic_docking.py

vanttec/vanttec_usv

5c7b45a61728404b4c957028eac7bc361f1b2077

[ "MIT" ]

5

2020-12-21T17:29:29.000Z

2022-02-15T07:51:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' ---------------------------------------------------------- @file: acoustic_docking.py @date: Wed Jun 3, 2020 @author: Alejandro Gonzalez Garcia @e-mail: alexglzg97@gmail.com @brief: Motion planning. ROS node to follow an acoustic signal for autonomous docking. @version: 1.0 Open source --------------------------------------------------------- ''' import math import time import os import numpy as np import rospy from geometry_msgs.msg import Pose, Pose2D, PoseArray from std_msgs.msg import Int32, Float32MultiArray, Float64, String from visualization_msgs.msg import Marker, MarkerArray class AcousticDocking: def __init__(self): self.ned_x = 0 self.ned_y = 0 self.yaw = 0 self.activated = True self.distance = 0 self.signal_angle = 0 self.x1 = 0 self.y1 = 0 self.x2 = 0 self.y2 = 0 self.x_body_origin = 0 self.y_body_origin = 0 self.correction_distance = 2 # ROS Subscribers rospy.Subscriber("/vectornav/ins_2d/NED_pose", Pose2D, self.ins_pose_callback) rospy.Subscriber("/usv_perception/hydrophones/acoustic_signal", Float64, self.signal_callback) rospy.Subscriber("/usv_perception/lidar_detector/dock", PoseArray, self.dock_callback) # ROS Publishers self.path_pub = rospy.Publisher("/mission/waypoints", Float32MultiArray, queue_size=10) self.status_pub = rospy.Publisher("/mission/status", Int32, queue_size=10) self.test = rospy.Publisher("/mission/state", Int32, queue_size=10) def ins_pose_callback(self,pose): self.ned_x = pose.x self.ned_y = pose.y self.yaw = pose.theta def signal_callback(self, signal): self.signal_angle = signal.data def dock_callback(self, dock): self.x1 = dock.poses[0].position.x self.y1 = dock.poses[0].position.y self.x2 = dock.poses[1].position.x self.y2 = dock.poses[1].position.y def calculate_distance_to_dock(self): ''' @name: calculate_distance_to_dock @brief: Calculates the distance between the USV and the dock. @param: -- @return: -- ''' xc = min([self.x1,self.x2]) + abs(self.x1 - self.x2)/2 yc = min([self.y1,self.y2]) + abs(self.y1 - self.y2)/2 self.distance = math.pow(xc*xc + yc*yc, 0.5) def dock(self): ''' @name: dock @brief: Calculates the intersection point between the USV and the pinger location at the dock. Returns two waypoints as desired positions. The first waypoint is perpendicularly in front of the pinger to straighten the path. the second waypoint is the location of the pinger in the dock, for docking. @param: -- @return: -- ''' if self.y1 < self.y2: yl = self.y1 xl = self.x1 yr = self.y2 xr = self.x2 else: yl = self.y2 xl = self.x2 yr = self.y1 xr = self.x1 yd = yl - yr xd = xl - xr alpha = math.atan2(yd,xd) + math.pi/2 if (abs(alpha) > (math.pi)): alpha = (alpha/abs(alpha))*(abs(alpha) - 2*math.pi) x_beta, y_beta = self.aux_to_body(1,0,self.signal_angle,self.x_body_origin,self.y_body_origin) common_denominator = (xl - xr)*(self.y_body_origin - y_beta) - (yl - yr)*(self.x_body_origin - x_beta) x_pinger = ((xl*yr-yl*xr)*(self.x_body_origin-x_beta)-(xl-xr)*(self.x_body_origin*y_beta-self.y_body_origin*x_beta)) / common_denominator y_pinger = ((xl*yr-yl*xr)*(self.y_body_origin-y_beta)-(yl-yr)*(self.x_body_origin*y_beta-self.y_body_origin*x_beta)) / common_denominator x_aux, y_aux = self.aux_to_body(-self.correction_distance,0,alpha,x_pinger,y_pinger) path_array = Float32MultiArray() path_array.layout.data_offset = 5 path_array.data = [x_aux, y_aux, x_pinger, y_pinger, 2] self.desired(path_array) def aux_to_body(self, aux_x2, aux_y2, alpha, body_x1, body_y1): ''' @name: aux_to_body @brief: Coordinate transformation between auxiliary and body reference frames. @param: aux_x2: target x coordinate in aux reference frame aux_y2: target y coordinate in aux reference frame alpha: angle between aux and body reference frames body_x1: aux x coordinate in body reference frame body_y1: aux y coordinate in body reference frame @return: body_x2: target x coordinate in body reference frame body_y2: target y coordinate in body reference frame ''' p = np.array([[aux_x2],[aux_y2]]) J = self.rotation_matrix(alpha) n = J.dot(p) body_x2 = n[0] + body_x1 body_y2 = n[1] + body_y1 return (body_x2, body_y2) def rotation_matrix(self, angle): ''' @name: rotation_matrix @brief: Transformation matrix template. @param: angle: angle of rotation @return: J: transformation matrix ''' J = np.array([[math.cos(angle), -1*math.sin(angle)], [math.sin(angle), math.cos(angle)]]) return (J) def desired(self, path): self.path_pub.publish(path) def main(): rospy.init_node("acoustic_docking", anonymous=False) rate = rospy.Rate(20) acousticDocking = AcousticDocking() last_detection = [] while not rospy.is_shutdown() and acousticDocking.activated: acousticDocking.calculate_distance_to_dock() if (acousticDocking.distance >= 5): acousticDocking.dock() else: acousticDocking.status_pub.publish(1) rate.sleep() rospy.spin() if __name__ == "__main__": try: main() except rospy.ROSInterruptException: pass

33.741573

145

0.604729

import math import time import os import numpy as np import rospy from geometry_msgs.msg import Pose, Pose2D, PoseArray from std_msgs.msg import Int32, Float32MultiArray, Float64, String from visualization_msgs.msg import Marker, MarkerArray class AcousticDocking: def __init__(self): self.ned_x = 0 self.ned_y = 0 self.yaw = 0 self.activated = True self.distance = 0 self.signal_angle = 0 self.x1 = 0 self.y1 = 0 self.x2 = 0 self.y2 = 0 self.x_body_origin = 0 self.y_body_origin = 0 self.correction_distance = 2 rospy.Subscriber("/vectornav/ins_2d/NED_pose", Pose2D, self.ins_pose_callback) rospy.Subscriber("/usv_perception/hydrophones/acoustic_signal", Float64, self.signal_callback) rospy.Subscriber("/usv_perception/lidar_detector/dock", PoseArray, self.dock_callback) self.path_pub = rospy.Publisher("/mission/waypoints", Float32MultiArray, queue_size=10) self.status_pub = rospy.Publisher("/mission/status", Int32, queue_size=10) self.test = rospy.Publisher("/mission/state", Int32, queue_size=10) def ins_pose_callback(self,pose): self.ned_x = pose.x self.ned_y = pose.y self.yaw = pose.theta def signal_callback(self, signal): self.signal_angle = signal.data def dock_callback(self, dock): self.x1 = dock.poses[0].position.x self.y1 = dock.poses[0].position.y self.x2 = dock.poses[1].position.x self.y2 = dock.poses[1].position.y def calculate_distance_to_dock(self): xc = min([self.x1,self.x2]) + abs(self.x1 - self.x2)/2 yc = min([self.y1,self.y2]) + abs(self.y1 - self.y2)/2 self.distance = math.pow(xc*xc + yc*yc, 0.5) def dock(self): if self.y1 < self.y2: yl = self.y1 xl = self.x1 yr = self.y2 xr = self.x2 else: yl = self.y2 xl = self.x2 yr = self.y1 xr = self.x1 yd = yl - yr xd = xl - xr alpha = math.atan2(yd,xd) + math.pi/2 if (abs(alpha) > (math.pi)): alpha = (alpha/abs(alpha))*(abs(alpha) - 2*math.pi) x_beta, y_beta = self.aux_to_body(1,0,self.signal_angle,self.x_body_origin,self.y_body_origin) common_denominator = (xl - xr)*(self.y_body_origin - y_beta) - (yl - yr)*(self.x_body_origin - x_beta) x_pinger = ((xl*yr-yl*xr)*(self.x_body_origin-x_beta)-(xl-xr)*(self.x_body_origin*y_beta-self.y_body_origin*x_beta)) / common_denominator y_pinger = ((xl*yr-yl*xr)*(self.y_body_origin-y_beta)-(yl-yr)*(self.x_body_origin*y_beta-self.y_body_origin*x_beta)) / common_denominator x_aux, y_aux = self.aux_to_body(-self.correction_distance,0,alpha,x_pinger,y_pinger) path_array = Float32MultiArray() path_array.layout.data_offset = 5 path_array.data = [x_aux, y_aux, x_pinger, y_pinger, 2] self.desired(path_array) def aux_to_body(self, aux_x2, aux_y2, alpha, body_x1, body_y1): p = np.array([[aux_x2],[aux_y2]]) J = self.rotation_matrix(alpha) n = J.dot(p) body_x2 = n[0] + body_x1 body_y2 = n[1] + body_y1 return (body_x2, body_y2) def rotation_matrix(self, angle): J = np.array([[math.cos(angle), -1*math.sin(angle)], [math.sin(angle), math.cos(angle)]]) return (J) def desired(self, path): self.path_pub.publish(path) def main(): rospy.init_node("acoustic_docking", anonymous=False) rate = rospy.Rate(20) acousticDocking = AcousticDocking() last_detection = [] while not rospy.is_shutdown() and acousticDocking.activated: acousticDocking.calculate_distance_to_dock() if (acousticDocking.distance >= 5): acousticDocking.dock() else: acousticDocking.status_pub.publish(1) rate.sleep() rospy.spin() if __name__ == "__main__": try: main() except rospy.ROSInterruptException: pass

true

f71ffbe737f15846e14c72d6820690ac7cf93d67

4,873

py

Python

lula/util.py

wiseodd/lula

a52b27c118ed136a62d8d7d1a898067d5ac685fb

[ "MIT" ]

15

2021-06-07T14:25:35.000Z

2021-12-26T16:41:01.000Z

lula/util.py

wiseodd/lula

a52b27c118ed136a62d8d7d1a898067d5ac685fb

[ "MIT" ]

1

2022-03-11T01:03:12.000Z

lula/util.py

wiseodd/lula

a52b27c118ed136a62d8d7d1a898067d5ac685fb

[ "MIT" ]

2

2021-06-19T05:41:05.000Z

2022-03-23T11:51:06.000Z

import numpy as np import torch from torch import nn from torch.nn import functional as F class MaskedLinear(nn.Module): def __init__(self, base_layer, m_in, m_out): """ The standard nn.Linear layer, but with gradient masking to enforce the LULA construction. """ super(MaskedLinear, self).__init__() # Extend the weight matrix W_base = base_layer.weight.data.clone() # (n_out, n_in) n_out, n_in = W_base.shape W = torch.randn(n_out+m_out, n_in+m_in) W[0:n_out, 0:n_in] = W_base.clone() W[0:n_out, n_in:] = 0 # Upper-right quadrant self.weight = nn.Parameter(W) # Extend the bias vector if base_layer.bias is not None: b_base = base_layer.bias.data.clone() b = torch.randn(n_out+m_out) b[:n_out] = b_base.clone() self.bias = nn.Parameter(b) else: self.bias = None # Apply gradient mask to the weight and bias self.mask_w = torch.zeros(n_out+m_out, n_in+m_in) self.mask_w[n_out:, :] = 1 # Lower half self.mask_b = torch.zeros(n_out+m_out) self.mask_b[n_out:] = 1 self.switch_grad_mask(True) # For safekeeping self.W_base, self.b_base = W_base, b_base self.n_out, self.n_in = n_out, n_in self.m_out, self.m_in = m_out, m_in def forward(self, x): return F.linear(x, self.weight, self.bias) def switch_grad_mask(self, on=True): if on: self.grad_handle_w = self.weight.register_hook(lambda grad: grad.mul_(self.mask_w)) self.grad_handle_b = self.bias.register_hook(lambda grad: grad.mul_(self.mask_b)) else: self.grad_handle_w.remove() self.grad_handle_b.remove() def to_gpu(self): self.mask_w = self.mask_w.cuda() self.mask_b = self.mask_b.cuda() def to_unmasked(self): lin = nn.Linear(self.n_in+self.m_in, self.n_out+self.m_out) lin.weight = self.weight lin.bias = self.bias return lin def extra_repr(self): return 'in_features={}, out_features={}, bias={}'.format( self.n_in+self.m_in, self.n_out+self.m_out, self.bias is not None ) class MaskedConv2d(nn.Module): def __init__(self, base_layer, m_in, m_out): """ The standard nn.Conv2d layer, but with gradient masking to enforce the LULA construction. """ super(MaskedConv2d, self).__init__() self.kernel_size = base_layer.kernel_size self.stride = base_layer.stride self.padding = base_layer.padding self.dilation = base_layer.dilation self.groups = base_layer.groups # Extend the weight matrix W_base = base_layer.weight.data.clone() # (n_out, n_in, k, k) n_out, n_in, k, _ = W_base.shape # Num of channels W = torch.randn(n_out+m_out, n_in+m_in, k, k) W[0:n_out, 0:n_in, :, :] = W_base.clone() W[0:n_out, n_in:, :, :] = 0 # Upper-right quadrant self.weight = nn.Parameter(W) # Extend the bias vector if base_layer.bias is not None: b_base = base_layer.bias.data.clone() b = torch.randn(n_out+m_out) b[:n_out] = b_base.clone() self.bias = nn.Parameter(b) else: self.bias = None # Apply gradient mask to the weight and bias self.mask_w = torch.zeros(n_out+m_out, n_in+m_in, k, k) self.mask_w[n_out:, :, :, :] = 1 # Lower half self.mask_b = torch.zeros(n_out+m_out) self.mask_b[n_out:] = 1 self.switch_grad_mask(True) # For safekeeping self.W_base, self.b_base = W_base, b_base self.n_out, self.n_in = n_out, n_in self.m_out, self.m_in = m_out, m_in def forward(self, x): return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) def switch_grad_mask(self, on=True): if on: self.grad_handle_w = self.weight.register_hook(lambda grad: grad.mul_(self.mask_w)) self.grad_handle_b = self.bias.register_hook(lambda grad: grad.mul_(self.mask_b)) else: self.grad_handle_w.remove() self.grad_handle_b.remove() def to_gpu(self): self.mask_w = self.mask_w.cuda() self.mask_b = self.mask_b.cuda() def to_unmasked(self): conv = nn.Conv2d(self.n_in+self.m_in, self.n_out+self.m_out, self.kernel_size, self.stride, self.padding, self.dilation, self.groups) conv.weight = self.weight conv.bias = self.bias return conv def extra_repr(self): return 'in_channels={}, out_channels={}, bias={}'.format( self.n_in+self.m_in, self.n_out+self.m_out, self.bias is not None )

32.059211

141

0.60353

import numpy as np import torch from torch import nn from torch.nn import functional as F class MaskedLinear(nn.Module): def __init__(self, base_layer, m_in, m_out): super(MaskedLinear, self).__init__() W_base = base_layer.weight.data.clone() n_out, n_in = W_base.shape W = torch.randn(n_out+m_out, n_in+m_in) W[0:n_out, 0:n_in] = W_base.clone() W[0:n_out, n_in:] = 0 self.weight = nn.Parameter(W) if base_layer.bias is not None: b_base = base_layer.bias.data.clone() b = torch.randn(n_out+m_out) b[:n_out] = b_base.clone() self.bias = nn.Parameter(b) else: self.bias = None self.mask_w = torch.zeros(n_out+m_out, n_in+m_in) self.mask_w[n_out:, :] = 1 self.mask_b = torch.zeros(n_out+m_out) self.mask_b[n_out:] = 1 self.switch_grad_mask(True) self.W_base, self.b_base = W_base, b_base self.n_out, self.n_in = n_out, n_in self.m_out, self.m_in = m_out, m_in def forward(self, x): return F.linear(x, self.weight, self.bias) def switch_grad_mask(self, on=True): if on: self.grad_handle_w = self.weight.register_hook(lambda grad: grad.mul_(self.mask_w)) self.grad_handle_b = self.bias.register_hook(lambda grad: grad.mul_(self.mask_b)) else: self.grad_handle_w.remove() self.grad_handle_b.remove() def to_gpu(self): self.mask_w = self.mask_w.cuda() self.mask_b = self.mask_b.cuda() def to_unmasked(self): lin = nn.Linear(self.n_in+self.m_in, self.n_out+self.m_out) lin.weight = self.weight lin.bias = self.bias return lin def extra_repr(self): return 'in_features={}, out_features={}, bias={}'.format( self.n_in+self.m_in, self.n_out+self.m_out, self.bias is not None ) class MaskedConv2d(nn.Module): def __init__(self, base_layer, m_in, m_out): super(MaskedConv2d, self).__init__() self.kernel_size = base_layer.kernel_size self.stride = base_layer.stride self.padding = base_layer.padding self.dilation = base_layer.dilation self.groups = base_layer.groups W_base = base_layer.weight.data.clone() n_out, n_in, k, _ = W_base.shape W = torch.randn(n_out+m_out, n_in+m_in, k, k) W[0:n_out, 0:n_in, :, :] = W_base.clone() W[0:n_out, n_in:, :, :] = 0 self.weight = nn.Parameter(W) if base_layer.bias is not None: b_base = base_layer.bias.data.clone() b = torch.randn(n_out+m_out) b[:n_out] = b_base.clone() self.bias = nn.Parameter(b) else: self.bias = None self.mask_w = torch.zeros(n_out+m_out, n_in+m_in, k, k) self.mask_w[n_out:, :, :, :] = 1 self.mask_b = torch.zeros(n_out+m_out) self.mask_b[n_out:] = 1 self.switch_grad_mask(True) self.W_base, self.b_base = W_base, b_base self.n_out, self.n_in = n_out, n_in self.m_out, self.m_in = m_out, m_in def forward(self, x): return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) def switch_grad_mask(self, on=True): if on: self.grad_handle_w = self.weight.register_hook(lambda grad: grad.mul_(self.mask_w)) self.grad_handle_b = self.bias.register_hook(lambda grad: grad.mul_(self.mask_b)) else: self.grad_handle_w.remove() self.grad_handle_b.remove() def to_gpu(self): self.mask_w = self.mask_w.cuda() self.mask_b = self.mask_b.cuda() def to_unmasked(self): conv = nn.Conv2d(self.n_in+self.m_in, self.n_out+self.m_out, self.kernel_size, self.stride, self.padding, self.dilation, self.groups) conv.weight = self.weight conv.bias = self.bias return conv def extra_repr(self): return 'in_channels={}, out_channels={}, bias={}'.format( self.n_in+self.m_in, self.n_out+self.m_out, self.bias is not None )

true

f71ffe7210fe58d0bbb802af2106a7f260b2e296

5,184

py

Python

line6.py

ChrBarth/pypod

4dccf6e5f5f3584672e2bab5281220a15ee51de5

[ "MIT" ]

4

2021-04-26T07:24:27.000Z

2022-01-17T23:10:47.000Z

line6.py

ChrBarth/pypod

4dccf6e5f5f3584672e2bab5281220a15ee51de5

[ "MIT" ]

null

line6.py

ChrBarth/pypod

4dccf6e5f5f3584672e2bab5281220a15ee51de5

[ "MIT" ]

null

#!/usr/bin/env python3 # Some useful POD-Variables # The Program names: PROGRAMS = [ "1A", "1B", "1C", "1D", "2A", "2B", "2C", "2D", "3A", "3B", "3C", "3D", "4A", "4B", "4C", "4D", "5A", "5B", "5C", "5D", "6A", "6B", "6C", "6D", "7A", "7B", "7C", "7D", "8A", "8B", "8C", "8D", "9A", "9B", "9C", "9D" ] # The Amp Models: amp_names = [ 'Tube Preamp', 'POD Clean Line 6', 'POD Crunch Line 6', 'POD Drive Line 6', 'POD Layer Line 6', 'Small Tweed', 'Tweed Blues', 'Black Panel', 'Modern Class A', 'Brit Class A', 'Brit Blues', 'Brit Classic', 'Brit Hi Gain', 'Rectified ’94', 'Modern Hi Gain', 'Fuzz Box', 'Jazz Clean', 'Boutique #1', 'Boutique #2', 'Brit Class A #2', 'Brit Class A #3', 'Small Tweed #2', 'Black Panel #2', 'Boutique #3', 'California Crunch #1', 'California Crunch #2', 'Rectified #2', 'Modern Hi Gain #2', 'Line 6 Twang', 'Line 6 Crunch #2', 'Line 6 Blues', 'Line 6 Insane' ] # The Cab names: cab_names = [ "1x 8 ’60 Fender Tweed Champ", "1x12 ’52 Fender Tweed Deluxe", "1x12 ’60 Vox AC15", "1x12 ’64 Fender Blackface Deluxe", "1x12 ’98 Line 6 Flextone", "2x12 ’65 Fender Blackface Twin", "2x12 ’67 VOX AC30", "2x12 ’95 Matchless Chieftain", "2x12 ’98 Pod custom 2x12", "4x10 ’59 Fender Bassman", "4x10 ’98 Pod custom 4x10 cab", "4x12 ’96 Marshall with V30s", "4x12 ’78 Marshall with 70s", "4x12 ’97 Marshall off axis", "4x12 ’98 Pod custom 4x12", "No Cabinet" ] # The effect types: fx_names = [ "Chorus2", "Flanger1", "Rotary", "Flanger2", "Delay/Chorus1", "Delay/Tremolo", "Delay", "Delay/Comp", "Chorus1", "Tremolo", "Bypass", "Compressor", "Delay/Chorus2", "Delay/Flanger1", "Delay/Swell", "Delay/Flanger2" ] cc_commands = { "AmpModel (0-32)": 12, # 0-32 (0=Tube Preamp,...) "Drive": 13, # 0-127 "Bass": 14, # 0-127 "Mid": 15, # 0-127 "Treble": 16, # 0-127 "BrightSwitch (0-63: OFF, 64-127: ON)": 73, # 0-63: OFF, 64-127: ON "Channel Vol": 17, # 0-127 "Presence": 21, # 0-127 "Noise Gate (0-63: OFF, 64-127: ON)": 22, # 0-63: OFF, 64-127: ON "GateThreshhold": 23, # 0-127 "GateDecay": 24, # 0-127 "Effect": 19, # 0-15 (0=Bypass,...) "EffectTweak": 1, # 0-127 "Distortion (0-63: OFF, 64-127: ON)": 25, # 0-63: OFF, 64-127: ON "DriveBoost (0-63: OFF, 64-127: ON)": 26, # 0-63: OFF, 64-127: ON "Presence (0-63: OFF, 64-127: ON)": 27, # 0-63: OFF, 64-127: ON "Delay (0-63: OFF, 64-127: ON)": 28, # 0-63: OFF, 64-127: ON "DelayTime": 30, # 0-127 = 0-3150ms "DelayTime2": 62, # 0-127 (Extra precision (???)) "DelayRepeats": 32, # 0-127 "DelayLevel": 34, # 0-127 "Reverb (0-63: OFF, 64-127: ON)": 36, # 0-63: OFF; 64-127: ON "ReverbType (0-63: Spring, 64-127: Hall)": 37, # 0-63: SPRING, 64-127: HALL "ReverbDecay": 38, # 0-127 "ReverbTone": 39, # 0-127 "ReverbDiffusion": 40, # 0-127 "ReverbDensity": 41, # 0-127 "ReverbLevel": 18, # 0-127 "CompressionRatio": 42, # 0-21=OFF, 22-44=1.4:1, 45-67=2:1, 68-90=3:1, 91-113=6:1, 114-127=INF "Wah (0-63: OFF, 64-127: ON)": 43, # 0-63: OFF, 64-127: ON "WahPedal": 4, # 0-127 (Pedal Position) "WahBottom": 44, # 0-127 (Bottom frequency) "WahTop": 45, # 0-127 (Top frequency) "Volume": 7, # 0-127 (Volume Pedal) "VolumeMin": 46, # 0-127 ??? "VolumePrePost (0-63: Pre Tube, 64-127: Post Tube)": 47, # 0-63: PRE TUBE, 64-127: POST TUBE "VolSwell (0-63: OFF, 64-127: ON)": 48, # 0-63: OFF, 64-127: ON "VolSwellRamp": 49, # 0-127 #"TapTempo": 64, # 64-127 = A TAP (=sending 2 in a second sets to 120bpm?) "Modulation (0-63: OFF, 64-127: ON)": 50, # 0-63: OFF, 64-127: ON (Chorus/Rotary/Tremolo) "Speed": 51, # 0-127 (Chorus/Flanger) "Depth": 52, # 0-127 (Chorus/Flanger) "Feedback": 53, # 0-63: NEGATIVE: 64-127: POSITIVE "ChorusPreDelay": 54, # 0-127 "RotarySpeed": 55, # 0-127 "RotaryMaxSpeed": 56, # 0-127 "RotaryMinSpeed": 57, # 0-127 "TremoloSpeed": 58, # 0-127 "TremoloDepth": 59, # 0-127 "CabinetType (0-15)": 71, # 0-15 (0=No Cab, ...) "AIRAmbienceLevel": 72 # 0-127 } compression_values = [ [ 0, "Off" ], [ 22, "1.4:1" ], [ 45, "2:1" ], [ 68, "3:1" ], [ 91, "6:1" ], [ 114, "INF" ] ] reverb_types = [ [ 0, "Spring" ], [ 64, "Hall" ] ] volume_pos = [ [ 0, "Pre-Tube" ], [ 64, "Post-Tube" ] ]

33.882353

103

0.470486

PROGRAMS = [ "1A", "1B", "1C", "1D", "2A", "2B", "2C", "2D", "3A", "3B", "3C", "3D", "4A", "4B", "4C", "4D", "5A", "5B", "5C", "5D", "6A", "6B", "6C", "6D", "7A", "7B", "7C", "7D", "8A", "8B", "8C", "8D", "9A", "9B", "9C", "9D" ] amp_names = [ 'Tube Preamp', 'POD Clean Line 6', 'POD Crunch Line 6', 'POD Drive Line 6', 'POD Layer Line 6', 'Small Tweed', 'Tweed Blues', 'Black Panel', 'Modern Class A', 'Brit Class A', 'Brit Blues', 'Brit Classic', 'Brit Hi Gain', 'Rectified ’94', 'Modern Hi Gain', 'Fuzz Box', 'Jazz Clean', 'Boutique #1', 'Boutique #2', 'Brit Class A #2', 'Brit Class A #3', 'Small Tweed #2', 'Black Panel #2', 'Boutique #3', 'California Crunch #1', 'California Crunch #2', 'Rectified #2', 'Modern Hi Gain #2', 'Line 6 Twang', 'Line 6 Crunch #2', 'Line 6 Blues', 'Line 6 Insane' ] cab_names = [ "1x 8 ’60 Fender Tweed Champ", "1x12 ’52 Fender Tweed Deluxe", "1x12 ’60 Vox AC15", "1x12 ’64 Fender Blackface Deluxe", "1x12 ’98 Line 6 Flextone", "2x12 ’65 Fender Blackface Twin", "2x12 ’67 VOX AC30", "2x12 ’95 Matchless Chieftain", "2x12 ’98 Pod custom 2x12", "4x10 ’59 Fender Bassman", "4x10 ’98 Pod custom 4x10 cab", "4x12 ’96 Marshall with V30s", "4x12 ’78 Marshall with 70s", "4x12 ’97 Marshall off axis", "4x12 ’98 Pod custom 4x12", "No Cabinet" ] fx_names = [ "Chorus2", "Flanger1", "Rotary", "Flanger2", "Delay/Chorus1", "Delay/Tremolo", "Delay", "Delay/Comp", "Chorus1", "Tremolo", "Bypass", "Compressor", "Delay/Chorus2", "Delay/Flanger1", "Delay/Swell", "Delay/Flanger2" ] cc_commands = { "AmpModel (0-32)": 12, "Drive": 13, "Bass": 14, "Mid": 15, "Treble": 16, "BrightSwitch (0-63: OFF, 64-127: ON)": 73, "Channel Vol": 17, "Presence": 21, "Noise Gate (0-63: OFF, 64-127: ON)": 22, "GateThreshhold": 23, "GateDecay": 24, "Effect": 19, "EffectTweak": 1, "Distortion (0-63: OFF, 64-127: ON)": 25, "DriveBoost (0-63: OFF, 64-127: ON)": 26, "Presence (0-63: OFF, 64-127: ON)": 27, "Delay (0-63: OFF, 64-127: ON)": 28, "DelayTime": 30, "DelayTime2": 62, "DelayRepeats": 32, "DelayLevel": 34, "Reverb (0-63: OFF, 64-127: ON)": 36, "ReverbType (0-63: Spring, 64-127: Hall)": 37, "ReverbDecay": 38, "ReverbTone": 39, "ReverbDiffusion": 40, "ReverbDensity": 41, "ReverbLevel": 18, "CompressionRatio": 42, "Wah (0-63: OFF, 64-127: ON)": 43, "WahPedal": 4, "WahBottom": 44, "WahTop": 45, "Volume": 7, "VolumeMin": 46, "VolumePrePost (0-63: Pre Tube, 64-127: Post Tube)": 47, "VolSwell (0-63: OFF, 64-127: ON)": 48, "VolSwellRamp": 49, "Speed": 51, "Depth": 52, "Feedback": 53, "ChorusPreDelay": 54, "RotarySpeed": 55, "RotaryMaxSpeed": 56, "RotaryMinSpeed": 57, "TremoloSpeed": 58, "TremoloDepth": 59, "CabinetType (0-15)": 71, "AIRAmbienceLevel": 72 } compression_values = [ [ 0, "Off" ], [ 22, "1.4:1" ], [ 45, "2:1" ], [ 68, "3:1" ], [ 91, "6:1" ], [ 114, "INF" ] ] reverb_types = [ [ 0, "Spring" ], [ 64, "Hall" ] ] volume_pos = [ [ 0, "Pre-Tube" ], [ 64, "Post-Tube" ] ]

true

f71fff32cfd2c5d668696fc8401b13ffda826fad

118

py

Python

commands/enlist.py

SirChopwood/Arma-3-Bot

5aa751beb6a362af1fcefe3c8b1d2572b3ffc76f

[ "MIT" ]

1

2020-10-30T18:37:39.000Z

commands/enlist.py

SirChopwood/Requisitions-Officer-Bot

5aa751beb6a362af1fcefe3c8b1d2572b3ffc76f

[ "MIT" ]

null

commands/enlist.py

SirChopwood/Requisitions-Officer-Bot

5aa751beb6a362af1fcefe3c8b1d2572b3ffc76f

[ "MIT" ]

null

async def Main(self, message, command, arguments): await self.run_file("section_slot_assign", message, arguments)

39.333333

66

0.771186

async def Main(self, message, command, arguments): await self.run_file("section_slot_assign", message, arguments)

true

f71fffe42a961cd5e75a8c0d5975af883c4d0f2f

2,763

py

Python

_preflight_hook_experiment.py

BapeHiks/pythonista_startup

060c355e9ecefa069227ae80c061cf532f9148e1

[ "MIT" ]

22

2016-04-05T14:56:11.000Z

2022-02-03T02:52:23.000Z

_preflight_hook_experiment.py

BapeHiks/pythonista_startup

060c355e9ecefa069227ae80c061cf532f9148e1

[ "MIT" ]

2

2016-04-28T08:45:16.000Z

2017-04-24T21:55:37.000Z

_preflight_hook_experiment.py

BapeHiks/pythonista_startup

060c355e9ecefa069227ae80c061cf532f9148e1

[ "MIT" ]

3

2017-04-23T16:47:33.000Z

2020-08-05T16:14:49.000Z

"""Highly unreliable way to register "preflight hooks", which are run every time you run a script (but not an editor action).""" from __future__ import absolute_import, division, print_function def run(): print(u"Installing preflight hooks...") # There's no official way to add hooks that run before every script run. # However Pythonista's preflight code imports pythonista_startup once to check what names it contains. # So we hack __import__ to run all functions in preflight_hooks whenever pythonista_startup is imported by specific bytecodes. try: import builtins except ImportError: import __builtin__ as builtins preflight_hooks = [] def _make_new_import(): import sys _real_import = builtins.__import__ def __import__(name, *args, **kwargs): if name == "pythonista_startup": try: f = sys._getframe(1) except ValueError: pass else: # These blobs are the bytecodes of the main function of Pythonista's preflight code (from Pythonista 2 and 3 respectively), which is run once before every script run. if f.f_code.co_code in ( b'y\x0e\x00d\x00\x00d\x01\x00l\x00\x00TWn\x07\x00\x01\x01\x01n\x01\x00Xd\x02\x00S', ##b'y\x1c\x00d\x01\x00d\x00\x00l\x00\x00}\x00\x00t\x01\x00|\x00\x00\x83\x01\x00}\x01\x00Wn\x0e\x00\x01\x01\x01g\x00\x00}\x01\x00Yn\x01\x00Xy\x15\x00t\x02\x00\x83\x00\x00\x01t\x03\x00|\x01\x00\x83\x01\x00\x01Wn\x08\x00\x01\x01\x01Yn\x01\x00Xd\x00\x00S', b'y\x1c\x00d\x01\x00d\x00\x00l\x00\x00}\x00\x00t\x01\x00|\x00\x00\x83\x01\x00}\x01\x00Wn\x0e\x00\x01\x01\x01g\x00\x00}\x01\x00Yn\x01\x00Xyy\x00d\x01\x00d\x00\x00l\x02\x00}\x02\x00d\x01\x00d\x00\x00l\x03\x00}\x03\x00d\x01\x00d\x00\x00l\x04\x00}\x04\x00d\x01\x00d\x00\x00l\x05\x00}\x05\x00|\x02\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x03\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x04\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x05\x00j\x06\x00d\x00\x00\x83\x01\x00\x01t\x07\x00\x83\x00\x00\x01t\x08\x00|\x01\x00\x83\x01\x00\x01Wn\x08\x00\x01\x01\x01Yn\x01\x00Xd\x00\x00S', ): for hook in preflight_hooks: hook() return _real_import(name, *args, **kwargs) __import__.patched = True return __import__ if not getattr(builtins.__import__, "patched", False): builtins.__import__ = _make_new_import() del builtins del _make_new_import print(u"Done installing preflight hooks.") if __name__ == "__main__": run()

49.339286

587

0.631198

from __future__ import absolute_import, division, print_function def run(): print(u"Installing preflight hooks...") # However Pythonista's preflight code imports pythonista_startup once to check what names it contains. try: import builtins except ImportError: import __builtin__ as builtins preflight_hooks = [] def _make_new_import(): import sys _real_import = builtins.__import__ def __import__(name, *args, **kwargs): if name == "pythonista_startup": try: f = sys._getframe(1) except ValueError: pass else: if f.f_code.co_code in ( b'y\x0e\x00d\x00\x00d\x01\x00l\x00\x00TWn\x07\x00\x01\x01\x01n\x01\x00Xd\x02\x00S', ##b'y\x1c\x00d\x01\x00d\x00\x00l\x00\x00}\x00\x00t\x01\x00|\x00\x00\x83\x01\x00}\x01\x00Wn\x0e\x00\x01\x01\x01g\x00\x00}\x01\x00Yn\x01\x00Xy\x15\x00t\x02\x00\x83\x00\x00\x01t\x03\x00|\x01\x00\x83\x01\x00\x01Wn\x08\x00\x01\x01\x01Yn\x01\x00Xd\x00\x00S', b'y\x1c\x00d\x01\x00d\x00\x00l\x00\x00}\x00\x00t\x01\x00|\x00\x00\x83\x01\x00}\x01\x00Wn\x0e\x00\x01\x01\x01g\x00\x00}\x01\x00Yn\x01\x00Xyy\x00d\x01\x00d\x00\x00l\x02\x00}\x02\x00d\x01\x00d\x00\x00l\x03\x00}\x03\x00d\x01\x00d\x00\x00l\x04\x00}\x04\x00d\x01\x00d\x00\x00l\x05\x00}\x05\x00|\x02\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x03\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x04\x00j\x06\x00d\x00\x00\x83\x01\x00\x01|\x05\x00j\x06\x00d\x00\x00\x83\x01\x00\x01t\x07\x00\x83\x00\x00\x01t\x08\x00|\x01\x00\x83\x01\x00\x01Wn\x08\x00\x01\x01\x01Yn\x01\x00Xd\x00\x00S', ): for hook in preflight_hooks: hook() return _real_import(name, *args, **kwargs) __import__.patched = True return __import__ if not getattr(builtins.__import__, "patched", False): builtins.__import__ = _make_new_import() del builtins del _make_new_import print(u"Done installing preflight hooks.") if __name__ == "__main__": run()

true

f7200016b3e4bb76f1473df3974edfb197cc475a

2,321

py

Python

tests/hazmat/backends/test_commoncrypto.py

balabit-deps/balabit-os-6-python-cryptography

c31d184a56a18bad89a6444313367be71b5b0877

[ "Apache-2.0", "BSD-3-Clause" ]

1

2015-09-25T16:03:32.000Z

tests/hazmat/backends/test_commoncrypto.py

balabit-deps/balabit-os-6-python-cryptography

c31d184a56a18bad89a6444313367be71b5b0877

[ "Apache-2.0", "BSD-3-Clause" ]

null

tests/hazmat/backends/test_commoncrypto.py

balabit-deps/balabit-os-6-python-cryptography

c31d184a56a18bad89a6444313367be71b5b0877

[ "Apache-2.0", "BSD-3-Clause" ]

1

2020-07-17T12:26:45.000Z

# This file is dual licensed under the terms of the Apache License, Version # 2.0, and the BSD License. See the LICENSE file in the root of this repository # for complete details. from __future__ import absolute_import, division, print_function import pytest from cryptography import utils from cryptography.exceptions import InternalError, _Reasons from cryptography.hazmat.backends import _available_backends from cryptography.hazmat.primitives.ciphers import Cipher, CipherAlgorithm from cryptography.hazmat.primitives.ciphers.algorithms import AES from cryptography.hazmat.primitives.ciphers.modes import CBC, GCM from ...utils import raises_unsupported_algorithm @utils.register_interface(CipherAlgorithm) class DummyCipher(object): name = "dummy-cipher" block_size = None key_size = None @pytest.mark.skipif("commoncrypto" not in [i.name for i in _available_backends()], reason="CommonCrypto not available") class TestCommonCrypto(object): def test_supports_cipher(self): from cryptography.hazmat.backends.commoncrypto.backend import backend assert backend.cipher_supported(None, None) is False def test_register_duplicate_cipher_adapter(self): from cryptography.hazmat.backends.commoncrypto.backend import backend with pytest.raises(ValueError): backend._register_cipher_adapter( AES, backend._lib.kCCAlgorithmAES128, CBC, backend._lib.kCCModeCBC ) def test_handle_response(self): from cryptography.hazmat.backends.commoncrypto.backend import backend with pytest.raises(ValueError): backend._check_cipher_response(backend._lib.kCCAlignmentError) with pytest.raises(InternalError): backend._check_cipher_response(backend._lib.kCCMemoryFailure) with pytest.raises(InternalError): backend._check_cipher_response(backend._lib.kCCDecodeError) def test_nonexistent_aead_cipher(self): from cryptography.hazmat.backends.commoncrypto.backend import Backend b = Backend() cipher = Cipher( DummyCipher(), GCM(b"fake_iv_here"), backend=b, ) with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_CIPHER): cipher.encryptor()

37.435484

79

0.734166

from __future__ import absolute_import, division, print_function import pytest from cryptography import utils from cryptography.exceptions import InternalError, _Reasons from cryptography.hazmat.backends import _available_backends from cryptography.hazmat.primitives.ciphers import Cipher, CipherAlgorithm from cryptography.hazmat.primitives.ciphers.algorithms import AES from cryptography.hazmat.primitives.ciphers.modes import CBC, GCM from ...utils import raises_unsupported_algorithm @utils.register_interface(CipherAlgorithm) class DummyCipher(object): name = "dummy-cipher" block_size = None key_size = None @pytest.mark.skipif("commoncrypto" not in [i.name for i in _available_backends()], reason="CommonCrypto not available") class TestCommonCrypto(object): def test_supports_cipher(self): from cryptography.hazmat.backends.commoncrypto.backend import backend assert backend.cipher_supported(None, None) is False def test_register_duplicate_cipher_adapter(self): from cryptography.hazmat.backends.commoncrypto.backend import backend with pytest.raises(ValueError): backend._register_cipher_adapter( AES, backend._lib.kCCAlgorithmAES128, CBC, backend._lib.kCCModeCBC ) def test_handle_response(self): from cryptography.hazmat.backends.commoncrypto.backend import backend with pytest.raises(ValueError): backend._check_cipher_response(backend._lib.kCCAlignmentError) with pytest.raises(InternalError): backend._check_cipher_response(backend._lib.kCCMemoryFailure) with pytest.raises(InternalError): backend._check_cipher_response(backend._lib.kCCDecodeError) def test_nonexistent_aead_cipher(self): from cryptography.hazmat.backends.commoncrypto.backend import Backend b = Backend() cipher = Cipher( DummyCipher(), GCM(b"fake_iv_here"), backend=b, ) with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_CIPHER): cipher.encryptor()

true

f720016578b11272f4a943f87114d1bf4673f739

15,319

py

Python

sdk/python/pulumi_azure_nextgen/storagesync/latest/get_registered_server.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

31

2020-09-21T09:41:01.000Z

2021-02-26T13:21:59.000Z

sdk/python/pulumi_azure_nextgen/storagesync/latest/get_registered_server.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

231

2020-09-21T09:38:45.000Z

2021-03-01T11:16:03.000Z

sdk/python/pulumi_azure_nextgen/storagesync/latest/get_registered_server.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

4

2020-09-29T14:14:59.000Z

2021-02-10T20:38:16.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'GetRegisteredServerResult', 'AwaitableGetRegisteredServerResult', 'get_registered_server', ] warnings.warn("""The 'latest' version is deprecated. Please migrate to the function in the top-level module: 'azure-nextgen:storagesync:getRegisteredServer'.""", DeprecationWarning) @pulumi.output_type class GetRegisteredServerResult: """ Registered Server resource. """ def __init__(__self__, agent_version=None, cluster_id=None, cluster_name=None, discovery_endpoint_uri=None, friendly_name=None, id=None, last_heart_beat=None, last_operation_name=None, last_workflow_id=None, management_endpoint_uri=None, monitoring_configuration=None, monitoring_endpoint_uri=None, name=None, provisioning_state=None, resource_location=None, server_certificate=None, server_id=None, server_management_error_code=None, server_os_version=None, server_role=None, service_location=None, storage_sync_service_uid=None, type=None): if agent_version and not isinstance(agent_version, str): raise TypeError("Expected argument 'agent_version' to be a str") pulumi.set(__self__, "agent_version", agent_version) if cluster_id and not isinstance(cluster_id, str): raise TypeError("Expected argument 'cluster_id' to be a str") pulumi.set(__self__, "cluster_id", cluster_id) if cluster_name and not isinstance(cluster_name, str): raise TypeError("Expected argument 'cluster_name' to be a str") pulumi.set(__self__, "cluster_name", cluster_name) if discovery_endpoint_uri and not isinstance(discovery_endpoint_uri, str): raise TypeError("Expected argument 'discovery_endpoint_uri' to be a str") pulumi.set(__self__, "discovery_endpoint_uri", discovery_endpoint_uri) if friendly_name and not isinstance(friendly_name, str): raise TypeError("Expected argument 'friendly_name' to be a str") pulumi.set(__self__, "friendly_name", friendly_name) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_heart_beat and not isinstance(last_heart_beat, str): raise TypeError("Expected argument 'last_heart_beat' to be a str") pulumi.set(__self__, "last_heart_beat", last_heart_beat) if last_operation_name and not isinstance(last_operation_name, str): raise TypeError("Expected argument 'last_operation_name' to be a str") pulumi.set(__self__, "last_operation_name", last_operation_name) if last_workflow_id and not isinstance(last_workflow_id, str): raise TypeError("Expected argument 'last_workflow_id' to be a str") pulumi.set(__self__, "last_workflow_id", last_workflow_id) if management_endpoint_uri and not isinstance(management_endpoint_uri, str): raise TypeError("Expected argument 'management_endpoint_uri' to be a str") pulumi.set(__self__, "management_endpoint_uri", management_endpoint_uri) if monitoring_configuration and not isinstance(monitoring_configuration, str): raise TypeError("Expected argument 'monitoring_configuration' to be a str") pulumi.set(__self__, "monitoring_configuration", monitoring_configuration) if monitoring_endpoint_uri and not isinstance(monitoring_endpoint_uri, str): raise TypeError("Expected argument 'monitoring_endpoint_uri' to be a str") pulumi.set(__self__, "monitoring_endpoint_uri", monitoring_endpoint_uri) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if resource_location and not isinstance(resource_location, str): raise TypeError("Expected argument 'resource_location' to be a str") pulumi.set(__self__, "resource_location", resource_location) if server_certificate and not isinstance(server_certificate, str): raise TypeError("Expected argument 'server_certificate' to be a str") pulumi.set(__self__, "server_certificate", server_certificate) if server_id and not isinstance(server_id, str): raise TypeError("Expected argument 'server_id' to be a str") pulumi.set(__self__, "server_id", server_id) if server_management_error_code and not isinstance(server_management_error_code, int): raise TypeError("Expected argument 'server_management_error_code' to be a int") pulumi.set(__self__, "server_management_error_code", server_management_error_code) if server_os_version and not isinstance(server_os_version, str): raise TypeError("Expected argument 'server_os_version' to be a str") pulumi.set(__self__, "server_os_version", server_os_version) if server_role and not isinstance(server_role, str): raise TypeError("Expected argument 'server_role' to be a str") pulumi.set(__self__, "server_role", server_role) if service_location and not isinstance(service_location, str): raise TypeError("Expected argument 'service_location' to be a str") pulumi.set(__self__, "service_location", service_location) if storage_sync_service_uid and not isinstance(storage_sync_service_uid, str): raise TypeError("Expected argument 'storage_sync_service_uid' to be a str") pulumi.set(__self__, "storage_sync_service_uid", storage_sync_service_uid) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="agentVersion") def agent_version(self) -> Optional[str]: """ Registered Server Agent Version """ return pulumi.get(self, "agent_version") @property @pulumi.getter(name="clusterId") def cluster_id(self) -> Optional[str]: """ Registered Server clusterId """ return pulumi.get(self, "cluster_id") @property @pulumi.getter(name="clusterName") def cluster_name(self) -> Optional[str]: """ Registered Server clusterName """ return pulumi.get(self, "cluster_name") @property @pulumi.getter(name="discoveryEndpointUri") def discovery_endpoint_uri(self) -> Optional[str]: """ Resource discoveryEndpointUri """ return pulumi.get(self, "discovery_endpoint_uri") @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> Optional[str]: """ Friendly Name """ return pulumi.get(self, "friendly_name") @property @pulumi.getter def id(self) -> str: """ Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName} """ return pulumi.get(self, "id") @property @pulumi.getter(name="lastHeartBeat") def last_heart_beat(self) -> Optional[str]: """ Registered Server last heart beat """ return pulumi.get(self, "last_heart_beat") @property @pulumi.getter(name="lastOperationName") def last_operation_name(self) -> Optional[str]: """ Resource Last Operation Name """ return pulumi.get(self, "last_operation_name") @property @pulumi.getter(name="lastWorkflowId") def last_workflow_id(self) -> Optional[str]: """ Registered Server lastWorkflowId """ return pulumi.get(self, "last_workflow_id") @property @pulumi.getter(name="managementEndpointUri") def management_endpoint_uri(self) -> Optional[str]: """ Management Endpoint Uri """ return pulumi.get(self, "management_endpoint_uri") @property @pulumi.getter(name="monitoringConfiguration") def monitoring_configuration(self) -> Optional[str]: """ Monitoring Configuration """ return pulumi.get(self, "monitoring_configuration") @property @pulumi.getter(name="monitoringEndpointUri") def monitoring_endpoint_uri(self) -> Optional[str]: """ Telemetry Endpoint Uri """ return pulumi.get(self, "monitoring_endpoint_uri") @property @pulumi.getter def name(self) -> str: """ The name of the resource """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> Optional[str]: """ Registered Server Provisioning State """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[str]: """ Resource Location """ return pulumi.get(self, "resource_location") @property @pulumi.getter(name="serverCertificate") def server_certificate(self) -> Optional[str]: """ Registered Server Certificate """ return pulumi.get(self, "server_certificate") @property @pulumi.getter(name="serverId") def server_id(self) -> Optional[str]: """ Registered Server serverId """ return pulumi.get(self, "server_id") @property @pulumi.getter(name="serverManagementErrorCode") def server_management_error_code(self) -> Optional[int]: """ Registered Server Management Error Code """ return pulumi.get(self, "server_management_error_code") @property @pulumi.getter(name="serverOSVersion") def server_os_version(self) -> Optional[str]: """ Registered Server OS Version """ return pulumi.get(self, "server_os_version") @property @pulumi.getter(name="serverRole") def server_role(self) -> Optional[str]: """ Registered Server serverRole """ return pulumi.get(self, "server_role") @property @pulumi.getter(name="serviceLocation") def service_location(self) -> Optional[str]: """ Service Location """ return pulumi.get(self, "service_location") @property @pulumi.getter(name="storageSyncServiceUid") def storage_sync_service_uid(self) -> Optional[str]: """ Registered Server storageSyncServiceUid """ return pulumi.get(self, "storage_sync_service_uid") @property @pulumi.getter def type(self) -> str: """ The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" """ return pulumi.get(self, "type") class AwaitableGetRegisteredServerResult(GetRegisteredServerResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetRegisteredServerResult( agent_version=self.agent_version, cluster_id=self.cluster_id, cluster_name=self.cluster_name, discovery_endpoint_uri=self.discovery_endpoint_uri, friendly_name=self.friendly_name, id=self.id, last_heart_beat=self.last_heart_beat, last_operation_name=self.last_operation_name, last_workflow_id=self.last_workflow_id, management_endpoint_uri=self.management_endpoint_uri, monitoring_configuration=self.monitoring_configuration, monitoring_endpoint_uri=self.monitoring_endpoint_uri, name=self.name, provisioning_state=self.provisioning_state, resource_location=self.resource_location, server_certificate=self.server_certificate, server_id=self.server_id, server_management_error_code=self.server_management_error_code, server_os_version=self.server_os_version, server_role=self.server_role, service_location=self.service_location, storage_sync_service_uid=self.storage_sync_service_uid, type=self.type) def get_registered_server(resource_group_name: Optional[str] = None, server_id: Optional[str] = None, storage_sync_service_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRegisteredServerResult: """ Registered Server resource. Latest API Version: 2020-03-01. :param str resource_group_name: The name of the resource group. The name is case insensitive. :param str server_id: GUID identifying the on-premises server. :param str storage_sync_service_name: Name of Storage Sync Service resource. """ pulumi.log.warn("get_registered_server is deprecated: The 'latest' version is deprecated. Please migrate to the function in the top-level module: 'azure-nextgen:storagesync:getRegisteredServer'.") __args__ = dict() __args__['resourceGroupName'] = resource_group_name __args__['serverId'] = server_id __args__['storageSyncServiceName'] = storage_sync_service_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:storagesync/latest:getRegisteredServer', __args__, opts=opts, typ=GetRegisteredServerResult).value return AwaitableGetRegisteredServerResult( agent_version=__ret__.agent_version, cluster_id=__ret__.cluster_id, cluster_name=__ret__.cluster_name, discovery_endpoint_uri=__ret__.discovery_endpoint_uri, friendly_name=__ret__.friendly_name, id=__ret__.id, last_heart_beat=__ret__.last_heart_beat, last_operation_name=__ret__.last_operation_name, last_workflow_id=__ret__.last_workflow_id, management_endpoint_uri=__ret__.management_endpoint_uri, monitoring_configuration=__ret__.monitoring_configuration, monitoring_endpoint_uri=__ret__.monitoring_endpoint_uri, name=__ret__.name, provisioning_state=__ret__.provisioning_state, resource_location=__ret__.resource_location, server_certificate=__ret__.server_certificate, server_id=__ret__.server_id, server_management_error_code=__ret__.server_management_error_code, server_os_version=__ret__.server_os_version, server_role=__ret__.server_role, service_location=__ret__.service_location, storage_sync_service_uid=__ret__.storage_sync_service_uid, type=__ret__.type)

42.671309

546

0.687447

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'GetRegisteredServerResult', 'AwaitableGetRegisteredServerResult', 'get_registered_server', ] warnings.warn("""The 'latest' version is deprecated. Please migrate to the function in the top-level module: 'azure-nextgen:storagesync:getRegisteredServer'.""", DeprecationWarning) @pulumi.output_type class GetRegisteredServerResult: def __init__(__self__, agent_version=None, cluster_id=None, cluster_name=None, discovery_endpoint_uri=None, friendly_name=None, id=None, last_heart_beat=None, last_operation_name=None, last_workflow_id=None, management_endpoint_uri=None, monitoring_configuration=None, monitoring_endpoint_uri=None, name=None, provisioning_state=None, resource_location=None, server_certificate=None, server_id=None, server_management_error_code=None, server_os_version=None, server_role=None, service_location=None, storage_sync_service_uid=None, type=None): if agent_version and not isinstance(agent_version, str): raise TypeError("Expected argument 'agent_version' to be a str") pulumi.set(__self__, "agent_version", agent_version) if cluster_id and not isinstance(cluster_id, str): raise TypeError("Expected argument 'cluster_id' to be a str") pulumi.set(__self__, "cluster_id", cluster_id) if cluster_name and not isinstance(cluster_name, str): raise TypeError("Expected argument 'cluster_name' to be a str") pulumi.set(__self__, "cluster_name", cluster_name) if discovery_endpoint_uri and not isinstance(discovery_endpoint_uri, str): raise TypeError("Expected argument 'discovery_endpoint_uri' to be a str") pulumi.set(__self__, "discovery_endpoint_uri", discovery_endpoint_uri) if friendly_name and not isinstance(friendly_name, str): raise TypeError("Expected argument 'friendly_name' to be a str") pulumi.set(__self__, "friendly_name", friendly_name) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_heart_beat and not isinstance(last_heart_beat, str): raise TypeError("Expected argument 'last_heart_beat' to be a str") pulumi.set(__self__, "last_heart_beat", last_heart_beat) if last_operation_name and not isinstance(last_operation_name, str): raise TypeError("Expected argument 'last_operation_name' to be a str") pulumi.set(__self__, "last_operation_name", last_operation_name) if last_workflow_id and not isinstance(last_workflow_id, str): raise TypeError("Expected argument 'last_workflow_id' to be a str") pulumi.set(__self__, "last_workflow_id", last_workflow_id) if management_endpoint_uri and not isinstance(management_endpoint_uri, str): raise TypeError("Expected argument 'management_endpoint_uri' to be a str") pulumi.set(__self__, "management_endpoint_uri", management_endpoint_uri) if monitoring_configuration and not isinstance(monitoring_configuration, str): raise TypeError("Expected argument 'monitoring_configuration' to be a str") pulumi.set(__self__, "monitoring_configuration", monitoring_configuration) if monitoring_endpoint_uri and not isinstance(monitoring_endpoint_uri, str): raise TypeError("Expected argument 'monitoring_endpoint_uri' to be a str") pulumi.set(__self__, "monitoring_endpoint_uri", monitoring_endpoint_uri) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if resource_location and not isinstance(resource_location, str): raise TypeError("Expected argument 'resource_location' to be a str") pulumi.set(__self__, "resource_location", resource_location) if server_certificate and not isinstance(server_certificate, str): raise TypeError("Expected argument 'server_certificate' to be a str") pulumi.set(__self__, "server_certificate", server_certificate) if server_id and not isinstance(server_id, str): raise TypeError("Expected argument 'server_id' to be a str") pulumi.set(__self__, "server_id", server_id) if server_management_error_code and not isinstance(server_management_error_code, int): raise TypeError("Expected argument 'server_management_error_code' to be a int") pulumi.set(__self__, "server_management_error_code", server_management_error_code) if server_os_version and not isinstance(server_os_version, str): raise TypeError("Expected argument 'server_os_version' to be a str") pulumi.set(__self__, "server_os_version", server_os_version) if server_role and not isinstance(server_role, str): raise TypeError("Expected argument 'server_role' to be a str") pulumi.set(__self__, "server_role", server_role) if service_location and not isinstance(service_location, str): raise TypeError("Expected argument 'service_location' to be a str") pulumi.set(__self__, "service_location", service_location) if storage_sync_service_uid and not isinstance(storage_sync_service_uid, str): raise TypeError("Expected argument 'storage_sync_service_uid' to be a str") pulumi.set(__self__, "storage_sync_service_uid", storage_sync_service_uid) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="agentVersion") def agent_version(self) -> Optional[str]: return pulumi.get(self, "agent_version") @property @pulumi.getter(name="clusterId") def cluster_id(self) -> Optional[str]: return pulumi.get(self, "cluster_id") @property @pulumi.getter(name="clusterName") def cluster_name(self) -> Optional[str]: return pulumi.get(self, "cluster_name") @property @pulumi.getter(name="discoveryEndpointUri") def discovery_endpoint_uri(self) -> Optional[str]: return pulumi.get(self, "discovery_endpoint_uri") @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> Optional[str]: return pulumi.get(self, "friendly_name") @property @pulumi.getter def id(self) -> str: return pulumi.get(self, "id") @property @pulumi.getter(name="lastHeartBeat") def last_heart_beat(self) -> Optional[str]: return pulumi.get(self, "last_heart_beat") @property @pulumi.getter(name="lastOperationName") def last_operation_name(self) -> Optional[str]: return pulumi.get(self, "last_operation_name") @property @pulumi.getter(name="lastWorkflowId") def last_workflow_id(self) -> Optional[str]: return pulumi.get(self, "last_workflow_id") @property @pulumi.getter(name="managementEndpointUri") def management_endpoint_uri(self) -> Optional[str]: return pulumi.get(self, "management_endpoint_uri") @property @pulumi.getter(name="monitoringConfiguration") def monitoring_configuration(self) -> Optional[str]: return pulumi.get(self, "monitoring_configuration") @property @pulumi.getter(name="monitoringEndpointUri") def monitoring_endpoint_uri(self) -> Optional[str]: return pulumi.get(self, "monitoring_endpoint_uri") @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> Optional[str]: return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[str]: return pulumi.get(self, "resource_location") @property @pulumi.getter(name="serverCertificate") def server_certificate(self) -> Optional[str]: return pulumi.get(self, "server_certificate") @property @pulumi.getter(name="serverId") def server_id(self) -> Optional[str]: return pulumi.get(self, "server_id") @property @pulumi.getter(name="serverManagementErrorCode") def server_management_error_code(self) -> Optional[int]: return pulumi.get(self, "server_management_error_code") @property @pulumi.getter(name="serverOSVersion") def server_os_version(self) -> Optional[str]: return pulumi.get(self, "server_os_version") @property @pulumi.getter(name="serverRole") def server_role(self) -> Optional[str]: return pulumi.get(self, "server_role") @property @pulumi.getter(name="serviceLocation") def service_location(self) -> Optional[str]: return pulumi.get(self, "service_location") @property @pulumi.getter(name="storageSyncServiceUid") def storage_sync_service_uid(self) -> Optional[str]: return pulumi.get(self, "storage_sync_service_uid") @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") class AwaitableGetRegisteredServerResult(GetRegisteredServerResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetRegisteredServerResult( agent_version=self.agent_version, cluster_id=self.cluster_id, cluster_name=self.cluster_name, discovery_endpoint_uri=self.discovery_endpoint_uri, friendly_name=self.friendly_name, id=self.id, last_heart_beat=self.last_heart_beat, last_operation_name=self.last_operation_name, last_workflow_id=self.last_workflow_id, management_endpoint_uri=self.management_endpoint_uri, monitoring_configuration=self.monitoring_configuration, monitoring_endpoint_uri=self.monitoring_endpoint_uri, name=self.name, provisioning_state=self.provisioning_state, resource_location=self.resource_location, server_certificate=self.server_certificate, server_id=self.server_id, server_management_error_code=self.server_management_error_code, server_os_version=self.server_os_version, server_role=self.server_role, service_location=self.service_location, storage_sync_service_uid=self.storage_sync_service_uid, type=self.type) def get_registered_server(resource_group_name: Optional[str] = None, server_id: Optional[str] = None, storage_sync_service_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRegisteredServerResult: pulumi.log.warn("get_registered_server is deprecated: The 'latest' version is deprecated. Please migrate to the function in the top-level module: 'azure-nextgen:storagesync:getRegisteredServer'.") __args__ = dict() __args__['resourceGroupName'] = resource_group_name __args__['serverId'] = server_id __args__['storageSyncServiceName'] = storage_sync_service_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-nextgen:storagesync/latest:getRegisteredServer', __args__, opts=opts, typ=GetRegisteredServerResult).value return AwaitableGetRegisteredServerResult( agent_version=__ret__.agent_version, cluster_id=__ret__.cluster_id, cluster_name=__ret__.cluster_name, discovery_endpoint_uri=__ret__.discovery_endpoint_uri, friendly_name=__ret__.friendly_name, id=__ret__.id, last_heart_beat=__ret__.last_heart_beat, last_operation_name=__ret__.last_operation_name, last_workflow_id=__ret__.last_workflow_id, management_endpoint_uri=__ret__.management_endpoint_uri, monitoring_configuration=__ret__.monitoring_configuration, monitoring_endpoint_uri=__ret__.monitoring_endpoint_uri, name=__ret__.name, provisioning_state=__ret__.provisioning_state, resource_location=__ret__.resource_location, server_certificate=__ret__.server_certificate, server_id=__ret__.server_id, server_management_error_code=__ret__.server_management_error_code, server_os_version=__ret__.server_os_version, server_role=__ret__.server_role, service_location=__ret__.service_location, storage_sync_service_uid=__ret__.storage_sync_service_uid, type=__ret__.type)

true

f72001cd4e99d72b538406d817ae842ce6ada978

37,141

py

Python

test/functional/rpc_psbt.py

GumFruit/cpuchain

c2fb213eb1e376a7457a8eecf907eca719eb4c99

[ "MIT" ]

null

test/functional/rpc_psbt.py

GumFruit/cpuchain

c2fb213eb1e376a7457a8eecf907eca719eb4c99

[ "MIT" ]

null

test/functional/rpc_psbt.py

GumFruit/cpuchain

c2fb213eb1e376a7457a8eecf907eca719eb4c99

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2018-2020 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the Partially Signed Transaction RPCs. """ from decimal import Decimal from itertools import product from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_approx, assert_equal, assert_greater_than, assert_raises_rpc_error, find_output, ) import json import os MAX_BIP125_RBF_SEQUENCE = 0xfffffffd # Create one-input, one-output, no-fee transaction: class PSBTTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 3 self.extra_args = [ ["-walletrbf=1"], ["-walletrbf=0", "-changetype=legacy"], [] ] self.supports_cli = False def skip_test_if_missing_module(self): self.skip_if_no_wallet() # TODO: Re-enable this test with segwit v1 def test_utxo_conversion(self): mining_node = self.nodes[2] offline_node = self.nodes[0] online_node = self.nodes[1] # Disconnect offline node from others # Topology of test network is linear, so this one call is enough self.disconnect_nodes(0, 1) # Create watchonly on online_node online_node.createwallet(wallet_name='wonline', disable_private_keys=True) wonline = online_node.get_wallet_rpc('wonline') w2 = online_node.get_wallet_rpc('') # Mine a transaction that credits the offline address offline_addr = offline_node.getnewaddress(address_type="p2sh-segwit") online_addr = w2.getnewaddress(address_type="p2sh-segwit") wonline.importaddress(offline_addr, "", False) mining_node.sendtoaddress(address=offline_addr, amount=1.0) mining_node.generate(nblocks=1) self.sync_blocks([mining_node, online_node]) # Construct an unsigned PSBT on the online node (who doesn't know the output is Segwit, so will include a non-witness UTXO) utxos = wonline.listunspent(addresses=[offline_addr]) raw = wonline.createrawtransaction([{"txid":utxos[0]["txid"], "vout":utxos[0]["vout"]}],[{online_addr:0.9999}]) psbt = wonline.walletprocesspsbt(online_node.converttopsbt(raw))["psbt"] assert "non_witness_utxo" in mining_node.decodepsbt(psbt)["inputs"][0] # Have the offline node sign the PSBT (which will update the UTXO to segwit) signed_psbt = offline_node.walletprocesspsbt(psbt)["psbt"] assert "witness_utxo" in mining_node.decodepsbt(signed_psbt)["inputs"][0] # Make sure we can mine the resulting transaction txid = mining_node.sendrawtransaction(mining_node.finalizepsbt(signed_psbt)["hex"]) mining_node.generate(1) self.sync_blocks([mining_node, online_node]) assert_equal(online_node.gettxout(txid,0)["confirmations"], 1) wonline.unloadwallet() # Reconnect self.connect_nodes(0, 1) self.connect_nodes(0, 2) def assert_change_type(self, psbtx, expected_type): """Assert that the given PSBT has a change output with the given type.""" # The decodepsbt RPC is stateless and independent of any settings, we can always just call it on the first node decoded_psbt = self.nodes[0].decodepsbt(psbtx["psbt"]) changepos = psbtx["changepos"] assert_equal(decoded_psbt["tx"]["vout"][changepos]["scriptPubKey"]["type"], expected_type) def run_test(self): # Create and fund a raw tx for sending 10 CPU psbtx1 = self.nodes[0].walletcreatefundedpsbt([], {self.nodes[2].getnewaddress():10})['psbt'] # If inputs are specified, do not automatically add more: utxo1 = self.nodes[0].listunspent()[0] assert_raises_rpc_error(-4, "Insufficient funds", self.nodes[0].walletcreatefundedpsbt, [{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():90}) psbtx1 = self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():90}, 0, {"add_inputs": True})['psbt'] assert_equal(len(self.nodes[0].decodepsbt(psbtx1)['tx']['vin']), 2) # Inputs argument can be null self.nodes[0].walletcreatefundedpsbt(None, {self.nodes[2].getnewaddress():10}) # Node 1 should not be able to add anything to it but still return the psbtx same as before psbtx = self.nodes[1].walletprocesspsbt(psbtx1)['psbt'] assert_equal(psbtx1, psbtx) # Sign the transaction and send signed_tx = self.nodes[0].walletprocesspsbt(psbtx)['psbt'] final_tx = self.nodes[0].finalizepsbt(signed_tx)['hex'] self.nodes[0].sendrawtransaction(final_tx) # Manually selected inputs can be locked: assert_equal(len(self.nodes[0].listlockunspent()), 0) utxo1 = self.nodes[0].listunspent()[0] psbtx1 = self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():1}, 0,{"lockUnspents": True})["psbt"] assert_equal(len(self.nodes[0].listlockunspent()), 1) # Locks are ignored for manually selected inputs self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():1}, 0) # Create p2sh, p2wpkh, and p2wsh addresses pubkey0 = self.nodes[0].getaddressinfo(self.nodes[0].getnewaddress())['pubkey'] pubkey1 = self.nodes[1].getaddressinfo(self.nodes[1].getnewaddress())['pubkey'] pubkey2 = self.nodes[2].getaddressinfo(self.nodes[2].getnewaddress())['pubkey'] # Setup watchonly wallets self.nodes[2].createwallet(wallet_name='wmulti', disable_private_keys=True) wmulti = self.nodes[2].get_wallet_rpc('wmulti') # Create all the addresses p2sh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "legacy")['address'] p2wsh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "bech32")['address'] p2sh_p2wsh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "p2sh-segwit")['address'] if not self.options.descriptors: wmulti.importaddress(p2sh) wmulti.importaddress(p2wsh) wmulti.importaddress(p2sh_p2wsh) p2wpkh = self.nodes[1].getnewaddress("", "bech32") p2pkh = self.nodes[1].getnewaddress("", "legacy") p2sh_p2wpkh = self.nodes[1].getnewaddress("", "p2sh-segwit") # fund those addresses rawtx = self.nodes[0].createrawtransaction([], {p2sh:10, p2wsh:10, p2wpkh:10, p2sh_p2wsh:10, p2sh_p2wpkh:10, p2pkh:10}) rawtx = self.nodes[0].fundrawtransaction(rawtx, {"changePosition":3}) signed_tx = self.nodes[0].signrawtransactionwithwallet(rawtx['hex'])['hex'] txid = self.nodes[0].sendrawtransaction(signed_tx) self.nodes[0].generate(6) self.sync_all() # Find the output pos p2sh_pos = -1 p2wsh_pos = -1 p2wpkh_pos = -1 p2pkh_pos = -1 p2sh_p2wsh_pos = -1 p2sh_p2wpkh_pos = -1 decoded = self.nodes[0].decoderawtransaction(signed_tx) for out in decoded['vout']: if out['scriptPubKey']['address'] == p2sh: p2sh_pos = out['n'] elif out['scriptPubKey']['address'] == p2wsh: p2wsh_pos = out['n'] elif out['scriptPubKey']['address'] == p2wpkh: p2wpkh_pos = out['n'] elif out['scriptPubKey']['address'] == p2sh_p2wsh: p2sh_p2wsh_pos = out['n'] elif out['scriptPubKey']['address'] == p2sh_p2wpkh: p2sh_p2wpkh_pos = out['n'] elif out['scriptPubKey']['address'] == p2pkh: p2pkh_pos = out['n'] inputs = [{"txid": txid, "vout": p2wpkh_pos}, {"txid": txid, "vout": p2sh_p2wpkh_pos}, {"txid": txid, "vout": p2pkh_pos}] outputs = [{self.nodes[1].getnewaddress(): 29.99}] # spend single key from node 1 created_psbt = self.nodes[1].walletcreatefundedpsbt(inputs, outputs) walletprocesspsbt_out = self.nodes[1].walletprocesspsbt(created_psbt['psbt']) # Make sure it has both types of UTXOs decoded = self.nodes[1].decodepsbt(walletprocesspsbt_out['psbt']) assert 'non_witness_utxo' in decoded['inputs'][0] assert 'witness_utxo' in decoded['inputs'][0] # Check decodepsbt fee calculation (input values shall only be counted once per UTXO) assert_equal(decoded['fee'], created_psbt['fee']) assert_equal(walletprocesspsbt_out['complete'], True) self.nodes[1].sendrawtransaction(self.nodes[1].finalizepsbt(walletprocesspsbt_out['psbt'])['hex']) self.log.info("Test walletcreatefundedpsbt fee rate of 10000 sat/vB and 0.1 CPU/kvB produces a total fee at or slightly below -maxtxfee (~0.05290000)") res1 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"fee_rate": 10000, "add_inputs": True}) assert_approx(res1["fee"], 0.055, 0.005) res2 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"feeRate": "0.1", "add_inputs": True}) assert_approx(res2["fee"], 0.055, 0.005) self.log.info("Test min fee rate checks with walletcreatefundedpsbt are bypassed, e.g. a fee_rate under 1 sat/vB is allowed") res3 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"fee_rate": "0.999", "add_inputs": True}) assert_approx(res3["fee"], 0.00000381, 0.0000001) res4 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"feeRate": 0.00000999, "add_inputs": True}) assert_approx(res4["fee"], 0.00000381, 0.0000001) self.log.info("Test min fee rate checks with walletcreatefundedpsbt are bypassed and that funding non-standard 'zero-fee' transactions is valid") for param, zero_value in product(["fee_rate", "feeRate"], [0, 0.000, 0.00000000, "0", "0.000", "0.00000000"]): assert_equal(0, self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {param: zero_value, "add_inputs": True})["fee"]) self.log.info("Test invalid fee rate settings") for param, value in {("fee_rate", 100000), ("feeRate", 1)}: assert_raises_rpc_error(-4, "Fee exceeds maximum configured by user (e.g. -maxtxfee, maxfeerate)", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: value, "add_inputs": True}) assert_raises_rpc_error(-3, "Amount out of range", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: -1, "add_inputs": True}) assert_raises_rpc_error(-3, "Amount is not a number or string", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: {"foo": "bar"}, "add_inputs": True}) # Test fee rate values that don't pass fixed-point parsing checks. for invalid_value in ["", 0.000000001, 1e-09, 1.111111111, 1111111111111111, "31.999999999999999999999"]: assert_raises_rpc_error(-3, "Invalid amount", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: invalid_value, "add_inputs": True}) # Test fee_rate values that cannot be represented in sat/vB. for invalid_value in [0.0001, 0.00000001, 0.00099999, 31.99999999, "0.0001", "0.00000001", "0.00099999", "31.99999999"]: assert_raises_rpc_error(-3, "Invalid amount", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"fee_rate": invalid_value, "add_inputs": True}) self.log.info("- raises RPC error if both feeRate and fee_rate are passed") assert_raises_rpc_error(-8, "Cannot specify both fee_rate (sat/vB) and feeRate (CPU/kvB)", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"fee_rate": 0.1, "feeRate": 0.1, "add_inputs": True}) self.log.info("- raises RPC error if both feeRate and estimate_mode passed") assert_raises_rpc_error(-8, "Cannot specify both estimate_mode and feeRate", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": "economical", "feeRate": 0.1, "add_inputs": True}) for param in ["feeRate", "fee_rate"]: self.log.info("- raises RPC error if both {} and conf_target are passed".format(param)) assert_raises_rpc_error(-8, "Cannot specify both conf_target and {}. Please provide either a confirmation " "target in blocks for automatic fee estimation, or an explicit fee rate.".format(param), self.nodes[1].walletcreatefundedpsbt ,inputs, outputs, 0, {param: 1, "conf_target": 1, "add_inputs": True}) self.log.info("- raises RPC error if both fee_rate and estimate_mode are passed") assert_raises_rpc_error(-8, "Cannot specify both estimate_mode and fee_rate", self.nodes[1].walletcreatefundedpsbt ,inputs, outputs, 0, {"fee_rate": 1, "estimate_mode": "economical", "add_inputs": True}) self.log.info("- raises RPC error with invalid estimate_mode settings") for k, v in {"number": 42, "object": {"foo": "bar"}}.items(): assert_raises_rpc_error(-3, "Expected type string for estimate_mode, got {}".format(k), self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": v, "conf_target": 0.1, "add_inputs": True}) for mode in ["", "foo", Decimal("3.141592")]: assert_raises_rpc_error(-8, 'Invalid estimate_mode parameter, must be one of: "unset", "economical", "conservative"', self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": 0.1, "add_inputs": True}) self.log.info("- raises RPC error with invalid conf_target settings") for mode in ["unset", "economical", "conservative"]: self.log.debug("{}".format(mode)) for k, v in {"string": "", "object": {"foo": "bar"}}.items(): assert_raises_rpc_error(-3, "Expected type number for conf_target, got {}".format(k), self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": v, "add_inputs": True}) for n in [-1, 0, 1009]: assert_raises_rpc_error(-8, "Invalid conf_target, must be between 1 and 1008", # max value of 1008 per src/policy/fees.h self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": n, "add_inputs": True}) self.log.info("Test walletcreatefundedpsbt with too-high fee rate produces total fee well above -maxtxfee and raises RPC error") # previously this was silently capped at -maxtxfee for bool_add, outputs_array in {True: outputs, False: [{self.nodes[1].getnewaddress(): 1}]}.items(): msg = "Fee exceeds maximum configured by user (e.g. -maxtxfee, maxfeerate)" assert_raises_rpc_error(-4, msg, self.nodes[1].walletcreatefundedpsbt, inputs, outputs_array, 0, {"fee_rate": 1000000, "add_inputs": bool_add}) assert_raises_rpc_error(-4, msg, self.nodes[1].walletcreatefundedpsbt, inputs, outputs_array, 0, {"feeRate": 1, "add_inputs": bool_add}) self.log.info("Test various PSBT operations") # partially sign multisig things with node 1 psbtx = wmulti.walletcreatefundedpsbt(inputs=[{"txid":txid,"vout":p2wsh_pos},{"txid":txid,"vout":p2sh_pos},{"txid":txid,"vout":p2sh_p2wsh_pos}], outputs={self.nodes[1].getnewaddress():29.99}, options={'changeAddress': self.nodes[1].getrawchangeaddress()})['psbt'] walletprocesspsbt_out = self.nodes[1].walletprocesspsbt(psbtx) psbtx = walletprocesspsbt_out['psbt'] assert_equal(walletprocesspsbt_out['complete'], False) # Unload wmulti, we don't need it anymore wmulti.unloadwallet() # partially sign with node 2. This should be complete and sendable walletprocesspsbt_out = self.nodes[2].walletprocesspsbt(psbtx) assert_equal(walletprocesspsbt_out['complete'], True) self.nodes[2].sendrawtransaction(self.nodes[2].finalizepsbt(walletprocesspsbt_out['psbt'])['hex']) # check that walletprocesspsbt fails to decode a non-psbt rawtx = self.nodes[1].createrawtransaction([{"txid":txid,"vout":p2wpkh_pos}], {self.nodes[1].getnewaddress():9.99}) assert_raises_rpc_error(-22, "TX decode failed", self.nodes[1].walletprocesspsbt, rawtx) # Convert a non-psbt to psbt and make sure we can decode it rawtx = self.nodes[0].createrawtransaction([], {self.nodes[1].getnewaddress():10}) rawtx = self.nodes[0].fundrawtransaction(rawtx) new_psbt = self.nodes[0].converttopsbt(rawtx['hex']) self.nodes[0].decodepsbt(new_psbt) # Make sure that a non-psbt with signatures cannot be converted # Error could be either "TX decode failed" (segwit inputs causes parsing to fail) or "Inputs must not have scriptSigs and scriptWitnesses" # We must set iswitness=True because the serialized transaction has inputs and is therefore a witness transaction signedtx = self.nodes[0].signrawtransactionwithwallet(rawtx['hex']) assert_raises_rpc_error(-22, "", self.nodes[0].converttopsbt, hexstring=signedtx['hex'], iswitness=True) assert_raises_rpc_error(-22, "", self.nodes[0].converttopsbt, hexstring=signedtx['hex'], permitsigdata=False, iswitness=True) # Unless we allow it to convert and strip signatures self.nodes[0].converttopsbt(signedtx['hex'], True) # Explicitly allow converting non-empty txs new_psbt = self.nodes[0].converttopsbt(rawtx['hex']) self.nodes[0].decodepsbt(new_psbt) # Create outputs to nodes 1 and 2 node1_addr = self.nodes[1].getnewaddress() node2_addr = self.nodes[2].getnewaddress() txid1 = self.nodes[0].sendtoaddress(node1_addr, 13) txid2 = self.nodes[0].sendtoaddress(node2_addr, 13) blockhash = self.nodes[0].generate(6)[0] self.sync_all() vout1 = find_output(self.nodes[1], txid1, 13, blockhash=blockhash) vout2 = find_output(self.nodes[2], txid2, 13, blockhash=blockhash) # Create a psbt spending outputs from nodes 1 and 2 psbt_orig = self.nodes[0].createpsbt([{"txid":txid1, "vout":vout1}, {"txid":txid2, "vout":vout2}], {self.nodes[0].getnewaddress():25.999}) # Update psbts, should only have data for one input and not the other psbt1 = self.nodes[1].walletprocesspsbt(psbt_orig, False, "ALL")['psbt'] psbt1_decoded = self.nodes[0].decodepsbt(psbt1) assert psbt1_decoded['inputs'][0] and not psbt1_decoded['inputs'][1] # Check that BIP32 path was added assert "bip32_derivs" in psbt1_decoded['inputs'][0] psbt2 = self.nodes[2].walletprocesspsbt(psbt_orig, False, "ALL", False)['psbt'] psbt2_decoded = self.nodes[0].decodepsbt(psbt2) assert not psbt2_decoded['inputs'][0] and psbt2_decoded['inputs'][1] # Check that BIP32 paths were not added assert "bip32_derivs" not in psbt2_decoded['inputs'][1] # Sign PSBTs (workaround issue #18039) psbt1 = self.nodes[1].walletprocesspsbt(psbt_orig)['psbt'] psbt2 = self.nodes[2].walletprocesspsbt(psbt_orig)['psbt'] # Combine, finalize, and send the psbts combined = self.nodes[0].combinepsbt([psbt1, psbt2]) finalized = self.nodes[0].finalizepsbt(combined)['hex'] self.nodes[0].sendrawtransaction(finalized) self.nodes[0].generate(6) self.sync_all() # Test additional args in walletcreatepsbt # Make sure both pre-included and funded inputs # have the correct sequence numbers based on # replaceable arg block_height = self.nodes[0].getblockcount() unspent = self.nodes[0].listunspent()[0] psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height+2, {"replaceable": False, "add_inputs": True}, False) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_greater_than(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" not in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], block_height+2) # Same construction with only locktime set and RBF explicitly enabled psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height, {"replaceable": True, "add_inputs": True}, True) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_equal(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], block_height) # Same construction without optional arguments psbtx_info = self.nodes[0].walletcreatefundedpsbt([], [{self.nodes[2].getnewaddress():unspent["amount"]+1}]) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_equal(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], 0) # Same construction without optional arguments, for a node with -walletrbf=0 unspent1 = self.nodes[1].listunspent()[0] psbtx_info = self.nodes[1].walletcreatefundedpsbt([{"txid":unspent1["txid"], "vout":unspent1["vout"]}], [{self.nodes[2].getnewaddress():unspent1["amount"]+1}], block_height, {"add_inputs": True}) decoded_psbt = self.nodes[1].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_greater_than(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in # Make sure change address wallet does not have P2SH innerscript access to results in success # when attempting BnB coin selection self.nodes[0].walletcreatefundedpsbt([], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height+2, {"changeAddress":self.nodes[1].getnewaddress()}, False) # Make sure the wallet's change type is respected by default small_output = {self.nodes[0].getnewaddress():0.1} psbtx_native = self.nodes[0].walletcreatefundedpsbt([], [small_output]) self.assert_change_type(psbtx_native, "witness_v0_keyhash") psbtx_legacy = self.nodes[1].walletcreatefundedpsbt([], [small_output]) self.assert_change_type(psbtx_legacy, "pubkeyhash") # Make sure the change type of the wallet can also be overwritten psbtx_np2wkh = self.nodes[1].walletcreatefundedpsbt([], [small_output], 0, {"change_type":"p2sh-segwit"}) self.assert_change_type(psbtx_np2wkh, "scripthash") # Make sure the change type cannot be specified if a change address is given invalid_options = {"change_type":"legacy","changeAddress":self.nodes[0].getnewaddress()} assert_raises_rpc_error(-8, "both change address and address type options", self.nodes[0].walletcreatefundedpsbt, [], [small_output], 0, invalid_options) # Regression test for 14473 (mishandling of already-signed witness transaction): psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], 0, {"add_inputs": True}) complete_psbt = self.nodes[0].walletprocesspsbt(psbtx_info["psbt"]) double_processed_psbt = self.nodes[0].walletprocesspsbt(complete_psbt["psbt"]) assert_equal(complete_psbt, double_processed_psbt) # We don't care about the decode result, but decoding must succeed. self.nodes[0].decodepsbt(double_processed_psbt["psbt"]) # Make sure unsafe inputs are included if specified self.nodes[2].createwallet(wallet_name="unsafe") wunsafe = self.nodes[2].get_wallet_rpc("unsafe") self.nodes[0].sendtoaddress(wunsafe.getnewaddress(), 2) self.sync_mempools() assert_raises_rpc_error(-4, "Insufficient funds", wunsafe.walletcreatefundedpsbt, [], [{self.nodes[0].getnewaddress(): 1}]) wunsafe.walletcreatefundedpsbt([], [{self.nodes[0].getnewaddress(): 1}], 0, {"include_unsafe": True}) # BIP 174 Test Vectors # Check that unknown values are just passed through unknown_psbt = "cHNidP8BAD8CAAAAAf//////////////////////////////////////////AAAAAAD/////AQAAAAAAAAAAA2oBAAAAAAAACg8BAgMEBQYHCAkPAQIDBAUGBwgJCgsMDQ4PAAA=" unknown_out = self.nodes[0].walletprocesspsbt(unknown_psbt)['psbt'] assert_equal(unknown_psbt, unknown_out) # Open the data file with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/rpc_psbt.json'), encoding='utf-8') as f: d = json.load(f) invalids = d['invalid'] valids = d['valid'] creators = d['creator'] signers = d['signer'] combiners = d['combiner'] finalizers = d['finalizer'] extractors = d['extractor'] # Invalid PSBTs for invalid in invalids: assert_raises_rpc_error(-22, "TX decode failed", self.nodes[0].decodepsbt, invalid) # Valid PSBTs for valid in valids: self.nodes[0].decodepsbt(valid) # Creator Tests for creator in creators: created_tx = self.nodes[0].createpsbt(creator['inputs'], creator['outputs']) assert_equal(created_tx, creator['result']) # Signer tests for i, signer in enumerate(signers): self.nodes[2].createwallet(wallet_name="wallet{}".format(i)) wrpc = self.nodes[2].get_wallet_rpc("wallet{}".format(i)) for key in signer['privkeys']: wrpc.importprivkey(key) signed_tx = wrpc.walletprocesspsbt(signer['psbt'])['psbt'] assert_equal(signed_tx, signer['result']) # Combiner test for combiner in combiners: combined = self.nodes[2].combinepsbt(combiner['combine']) assert_equal(combined, combiner['result']) # Empty combiner test assert_raises_rpc_error(-8, "Parameter 'txs' cannot be empty", self.nodes[0].combinepsbt, []) # Finalizer test for finalizer in finalizers: finalized = self.nodes[2].finalizepsbt(finalizer['finalize'], False)['psbt'] assert_equal(finalized, finalizer['result']) # Extractor test for extractor in extractors: extracted = self.nodes[2].finalizepsbt(extractor['extract'], True)['hex'] assert_equal(extracted, extractor['result']) # Unload extra wallets for i, signer in enumerate(signers): self.nodes[2].unloadwallet("wallet{}".format(i)) # TODO: Re-enable this for segwit v1 # self.test_utxo_conversion() # Test that psbts with p2pkh outputs are created properly p2pkh = self.nodes[0].getnewaddress(address_type='legacy') psbt = self.nodes[1].walletcreatefundedpsbt([], [{p2pkh : 1}], 0, {"includeWatching" : True}, True) self.nodes[0].decodepsbt(psbt['psbt']) # Test decoding error: invalid base64 assert_raises_rpc_error(-22, "TX decode failed invalid base64", self.nodes[0].decodepsbt, ";definitely not base64;") # Send to all types of addresses addr1 = self.nodes[1].getnewaddress("", "bech32") txid1 = self.nodes[0].sendtoaddress(addr1, 11) vout1 = find_output(self.nodes[0], txid1, 11) addr2 = self.nodes[1].getnewaddress("", "legacy") txid2 = self.nodes[0].sendtoaddress(addr2, 11) vout2 = find_output(self.nodes[0], txid2, 11) addr3 = self.nodes[1].getnewaddress("", "p2sh-segwit") txid3 = self.nodes[0].sendtoaddress(addr3, 11) vout3 = find_output(self.nodes[0], txid3, 11) self.sync_all() def test_psbt_input_keys(psbt_input, keys): """Check that the psbt input has only the expected keys.""" assert_equal(set(keys), set(psbt_input.keys())) # Create a PSBT. None of the inputs are filled initially psbt = self.nodes[1].createpsbt([{"txid":txid1, "vout":vout1},{"txid":txid2, "vout":vout2},{"txid":txid3, "vout":vout3}], {self.nodes[0].getnewaddress():32.999}) decoded = self.nodes[1].decodepsbt(psbt) test_psbt_input_keys(decoded['inputs'][0], []) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], []) # Update a PSBT with UTXOs from the node # Bech32 inputs should be filled with witness UTXO. Other inputs should not be filled because they are non-witness updated = self.nodes[1].utxoupdatepsbt(psbt) decoded = self.nodes[1].decodepsbt(updated) test_psbt_input_keys(decoded['inputs'][0], ['witness_utxo']) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], []) # Try again, now while providing descriptors, making P2SH-segwit work, and causing bip32_derivs and redeem_script to be filled in descs = [self.nodes[1].getaddressinfo(addr)['desc'] for addr in [addr1,addr2,addr3]] updated = self.nodes[1].utxoupdatepsbt(psbt=psbt, descriptors=descs) decoded = self.nodes[1].decodepsbt(updated) test_psbt_input_keys(decoded['inputs'][0], ['witness_utxo', 'bip32_derivs']) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], ['witness_utxo', 'bip32_derivs', 'redeem_script']) # Two PSBTs with a common input should not be joinable psbt1 = self.nodes[1].createpsbt([{"txid":txid1, "vout":vout1}], {self.nodes[0].getnewaddress():Decimal('10.999')}) assert_raises_rpc_error(-8, "exists in multiple PSBTs", self.nodes[1].joinpsbts, [psbt1, updated]) # Join two distinct PSBTs addr4 = self.nodes[1].getnewaddress("", "p2sh-segwit") txid4 = self.nodes[0].sendtoaddress(addr4, 5) vout4 = find_output(self.nodes[0], txid4, 5) self.nodes[0].generate(6) self.sync_all() psbt2 = self.nodes[1].createpsbt([{"txid":txid4, "vout":vout4}], {self.nodes[0].getnewaddress():Decimal('4.999')}) psbt2 = self.nodes[1].walletprocesspsbt(psbt2)['psbt'] psbt2_decoded = self.nodes[0].decodepsbt(psbt2) assert "final_scriptwitness" in psbt2_decoded['inputs'][0] and "final_scriptSig" in psbt2_decoded['inputs'][0] joined = self.nodes[0].joinpsbts([psbt, psbt2]) joined_decoded = self.nodes[0].decodepsbt(joined) assert len(joined_decoded['inputs']) == 4 and len(joined_decoded['outputs']) == 2 and "final_scriptwitness" not in joined_decoded['inputs'][3] and "final_scriptSig" not in joined_decoded['inputs'][3] # Check that joining shuffles the inputs and outputs # 10 attempts should be enough to get a shuffled join shuffled = False for _ in range(10): shuffled_joined = self.nodes[0].joinpsbts([psbt, psbt2]) shuffled |= joined != shuffled_joined if shuffled: break assert shuffled # Newly created PSBT needs UTXOs and updating addr = self.nodes[1].getnewaddress("", "p2sh-segwit") txid = self.nodes[0].sendtoaddress(addr, 7) addrinfo = self.nodes[1].getaddressinfo(addr) blockhash = self.nodes[0].generate(6)[0] self.sync_all() vout = find_output(self.nodes[0], txid, 7, blockhash=blockhash) psbt = self.nodes[1].createpsbt([{"txid":txid, "vout":vout}], {self.nodes[0].getnewaddress("", "p2sh-segwit"):Decimal('6.999')}) analyzed = self.nodes[0].analyzepsbt(psbt) assert not analyzed['inputs'][0]['has_utxo'] and not analyzed['inputs'][0]['is_final'] and analyzed['inputs'][0]['next'] == 'updater' and analyzed['next'] == 'updater' # After update with wallet, only needs signing updated = self.nodes[1].walletprocesspsbt(psbt, False, 'ALL', True)['psbt'] analyzed = self.nodes[0].analyzepsbt(updated) assert analyzed['inputs'][0]['has_utxo'] and not analyzed['inputs'][0]['is_final'] and analyzed['inputs'][0]['next'] == 'signer' and analyzed['next'] == 'signer' and analyzed['inputs'][0]['missing']['signatures'][0] == addrinfo['embedded']['witness_program'] # Check fee and size things assert analyzed['fee'] == Decimal('0.001') and analyzed['estimated_vsize'] == 134 and analyzed['estimated_feerate'] == Decimal('0.00746268') # After signing and finalizing, needs extracting signed = self.nodes[1].walletprocesspsbt(updated)['psbt'] analyzed = self.nodes[0].analyzepsbt(signed) assert analyzed['inputs'][0]['has_utxo'] and analyzed['inputs'][0]['is_final'] and analyzed['next'] == 'extractor' self.log.info("PSBT spending unspendable outputs should have error message and Creator as next") analysis = self.nodes[0].analyzepsbt('cHNidP8BAJoCAAAAAljoeiG1ba8MI76OcHBFbDNvfLqlyHV5JPVFiHuyq911AAAAAAD/////g40EJ9DsZQpoqka7CwmK6kQiwHGyyng1Kgd5WdB86h0BAAAAAP////8CcKrwCAAAAAAWAEHYXCtx0AYLCcmIauuBXlCZHdoSTQDh9QUAAAAAFv8/wADXYP/7//////8JxOh0LR2HAI8AAAAAAAEBIADC6wsAAAAAF2oUt/X69ELjeX2nTof+fZ10l+OyAokDAQcJAwEHEAABAACAAAEBIADC6wsAAAAAF2oUt/X69ELjeX2nTof+fZ10l+OyAokDAQcJAwEHENkMak8AAAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 spends unspendable output') self.log.info("PSBT with invalid values should have error message and Creator as next") analysis = self.nodes[0].analyzepsbt('cHNidP8BAHECAAAAAfA00BFgAm6tp86RowwH6BMImQNL5zXUcTT97XoLGz0BAAAAAAD/////AgD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XL87QKVAAAAABYAFPck4gF7iL4NL4wtfRAKgQbghiTUAAAAAAABAR8AgIFq49AHABYAFJUDtxf2PHo641HEOBOAIvFMNTr2AAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 has invalid value') self.log.info("PSBT with signed, but not finalized, inputs should have Finalizer as next") analysis = self.nodes[0].analyzepsbt('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') assert_equal(analysis['next'], 'finalizer') analysis = self.nodes[0].analyzepsbt('cHNidP8BAHECAAAAAfA00BFgAm6tp86RowwH6BMImQNL5zXUcTT97XoLGz0BAAAAAAD/////AgCAgWrj0AcAFgAUKNw0x8HRctAgmvoevm4u1SbN7XL87QKVAAAAABYAFPck4gF7iL4NL4wtfRAKgQbghiTUAAAAAAABAR8A8gUqAQAAABYAFJUDtxf2PHo641HEOBOAIvFMNTr2AAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Output amount invalid') analysis = self.nodes[0].analyzepsbt('cHNidP8BAJoCAAAAAkvEW8NnDtdNtDpsmze+Ht2LH35IJcKv00jKAlUs21RrAwAAAAD/////S8Rbw2cO1020OmybN74e3Ysffkglwq/TSMoCVSzbVGsBAAAAAP7///8CwLYClQAAAAAWABSNJKzjaUb3uOxixsvh1GGE3fW7zQD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XIAAAAAAAEAnQIAAAACczMa321tVHuN4GKWKRncycI22aX3uXgwSFUKM2orjRsBAAAAAP7///9zMxrfbW1Ue43gYpYpGdzJwjbZpfe5eDBIVQozaiuNGwAAAAAA/v///wIA+QKVAAAAABl2qRT9zXUVA8Ls5iVqynLHe5/vSe1XyYisQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAAAAAQEfQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAA==') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 specifies invalid prevout') assert_raises_rpc_error(-25, 'Inputs missing or spent', self.nodes[0].walletprocesspsbt, 'cHNidP8BAJoCAAAAAkvEW8NnDtdNtDpsmze+Ht2LH35IJcKv00jKAlUs21RrAwAAAAD/////S8Rbw2cO1020OmybN74e3Ysffkglwq/TSMoCVSzbVGsBAAAAAP7///8CwLYClQAAAAAWABSNJKzjaUb3uOxixsvh1GGE3fW7zQD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XIAAAAAAAEAnQIAAAACczMa321tVHuN4GKWKRncycI22aX3uXgwSFUKM2orjRsBAAAAAP7///9zMxrfbW1Ue43gYpYpGdzJwjbZpfe5eDBIVQozaiuNGwAAAAAA/v///wIA+QKVAAAAABl2qRT9zXUVA8Ls5iVqynLHe5/vSe1XyYisQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAAAAAQEfQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAA==') if __name__ == '__main__': PSBTTest().main()

61.593698

584

0.675991

from decimal import Decimal from itertools import product from test_framework.test_framework import BitcoinTestFramework from test_framework.util import ( assert_approx, assert_equal, assert_greater_than, assert_raises_rpc_error, find_output, ) import json import os MAX_BIP125_RBF_SEQUENCE = 0xfffffffd class PSBTTest(BitcoinTestFramework): def set_test_params(self): self.num_nodes = 3 self.extra_args = [ ["-walletrbf=1"], ["-walletrbf=0", "-changetype=legacy"], [] ] self.supports_cli = False def skip_test_if_missing_module(self): self.skip_if_no_wallet() def test_utxo_conversion(self): mining_node = self.nodes[2] offline_node = self.nodes[0] online_node = self.nodes[1] self.disconnect_nodes(0, 1) online_node.createwallet(wallet_name='wonline', disable_private_keys=True) wonline = online_node.get_wallet_rpc('wonline') w2 = online_node.get_wallet_rpc('') offline_addr = offline_node.getnewaddress(address_type="p2sh-segwit") online_addr = w2.getnewaddress(address_type="p2sh-segwit") wonline.importaddress(offline_addr, "", False) mining_node.sendtoaddress(address=offline_addr, amount=1.0) mining_node.generate(nblocks=1) self.sync_blocks([mining_node, online_node]) utxos = wonline.listunspent(addresses=[offline_addr]) raw = wonline.createrawtransaction([{"txid":utxos[0]["txid"], "vout":utxos[0]["vout"]}],[{online_addr:0.9999}]) psbt = wonline.walletprocesspsbt(online_node.converttopsbt(raw))["psbt"] assert "non_witness_utxo" in mining_node.decodepsbt(psbt)["inputs"][0] # Have the offline node sign the PSBT (which will update the UTXO to segwit) signed_psbt = offline_node.walletprocesspsbt(psbt)["psbt"] assert "witness_utxo" in mining_node.decodepsbt(signed_psbt)["inputs"][0] # Make sure we can mine the resulting transaction txid = mining_node.sendrawtransaction(mining_node.finalizepsbt(signed_psbt)["hex"]) mining_node.generate(1) self.sync_blocks([mining_node, online_node]) assert_equal(online_node.gettxout(txid,0)["confirmations"], 1) wonline.unloadwallet() # Reconnect self.connect_nodes(0, 1) self.connect_nodes(0, 2) def assert_change_type(self, psbtx, expected_type): # The decodepsbt RPC is stateless and independent of any settings, we can always just call it on the first node decoded_psbt = self.nodes[0].decodepsbt(psbtx["psbt"]) changepos = psbtx["changepos"] assert_equal(decoded_psbt["tx"]["vout"][changepos]["scriptPubKey"]["type"], expected_type) def run_test(self): # Create and fund a raw tx for sending 10 CPU psbtx1 = self.nodes[0].walletcreatefundedpsbt([], {self.nodes[2].getnewaddress():10})['psbt'] # If inputs are specified, do not automatically add more: utxo1 = self.nodes[0].listunspent()[0] assert_raises_rpc_error(-4, "Insufficient funds", self.nodes[0].walletcreatefundedpsbt, [{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():90}) psbtx1 = self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():90}, 0, {"add_inputs": True})['psbt'] assert_equal(len(self.nodes[0].decodepsbt(psbtx1)['tx']['vin']), 2) # Inputs argument can be null self.nodes[0].walletcreatefundedpsbt(None, {self.nodes[2].getnewaddress():10}) # Node 1 should not be able to add anything to it but still return the psbtx same as before psbtx = self.nodes[1].walletprocesspsbt(psbtx1)['psbt'] assert_equal(psbtx1, psbtx) # Sign the transaction and send signed_tx = self.nodes[0].walletprocesspsbt(psbtx)['psbt'] final_tx = self.nodes[0].finalizepsbt(signed_tx)['hex'] self.nodes[0].sendrawtransaction(final_tx) # Manually selected inputs can be locked: assert_equal(len(self.nodes[0].listlockunspent()), 0) utxo1 = self.nodes[0].listunspent()[0] psbtx1 = self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():1}, 0,{"lockUnspents": True})["psbt"] assert_equal(len(self.nodes[0].listlockunspent()), 1) # Locks are ignored for manually selected inputs self.nodes[0].walletcreatefundedpsbt([{"txid": utxo1['txid'], "vout": utxo1['vout']}], {self.nodes[2].getnewaddress():1}, 0) # Create p2sh, p2wpkh, and p2wsh addresses pubkey0 = self.nodes[0].getaddressinfo(self.nodes[0].getnewaddress())['pubkey'] pubkey1 = self.nodes[1].getaddressinfo(self.nodes[1].getnewaddress())['pubkey'] pubkey2 = self.nodes[2].getaddressinfo(self.nodes[2].getnewaddress())['pubkey'] # Setup watchonly wallets self.nodes[2].createwallet(wallet_name='wmulti', disable_private_keys=True) wmulti = self.nodes[2].get_wallet_rpc('wmulti') # Create all the addresses p2sh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "legacy")['address'] p2wsh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "bech32")['address'] p2sh_p2wsh = wmulti.addmultisigaddress(2, [pubkey0, pubkey1, pubkey2], "", "p2sh-segwit")['address'] if not self.options.descriptors: wmulti.importaddress(p2sh) wmulti.importaddress(p2wsh) wmulti.importaddress(p2sh_p2wsh) p2wpkh = self.nodes[1].getnewaddress("", "bech32") p2pkh = self.nodes[1].getnewaddress("", "legacy") p2sh_p2wpkh = self.nodes[1].getnewaddress("", "p2sh-segwit") # fund those addresses rawtx = self.nodes[0].createrawtransaction([], {p2sh:10, p2wsh:10, p2wpkh:10, p2sh_p2wsh:10, p2sh_p2wpkh:10, p2pkh:10}) rawtx = self.nodes[0].fundrawtransaction(rawtx, {"changePosition":3}) signed_tx = self.nodes[0].signrawtransactionwithwallet(rawtx['hex'])['hex'] txid = self.nodes[0].sendrawtransaction(signed_tx) self.nodes[0].generate(6) self.sync_all() # Find the output pos p2sh_pos = -1 p2wsh_pos = -1 p2wpkh_pos = -1 p2pkh_pos = -1 p2sh_p2wsh_pos = -1 p2sh_p2wpkh_pos = -1 decoded = self.nodes[0].decoderawtransaction(signed_tx) for out in decoded['vout']: if out['scriptPubKey']['address'] == p2sh: p2sh_pos = out['n'] elif out['scriptPubKey']['address'] == p2wsh: p2wsh_pos = out['n'] elif out['scriptPubKey']['address'] == p2wpkh: p2wpkh_pos = out['n'] elif out['scriptPubKey']['address'] == p2sh_p2wsh: p2sh_p2wsh_pos = out['n'] elif out['scriptPubKey']['address'] == p2sh_p2wpkh: p2sh_p2wpkh_pos = out['n'] elif out['scriptPubKey']['address'] == p2pkh: p2pkh_pos = out['n'] inputs = [{"txid": txid, "vout": p2wpkh_pos}, {"txid": txid, "vout": p2sh_p2wpkh_pos}, {"txid": txid, "vout": p2pkh_pos}] outputs = [{self.nodes[1].getnewaddress(): 29.99}] # spend single key from node 1 created_psbt = self.nodes[1].walletcreatefundedpsbt(inputs, outputs) walletprocesspsbt_out = self.nodes[1].walletprocesspsbt(created_psbt['psbt']) # Make sure it has both types of UTXOs decoded = self.nodes[1].decodepsbt(walletprocesspsbt_out['psbt']) assert 'non_witness_utxo' in decoded['inputs'][0] assert 'witness_utxo' in decoded['inputs'][0] # Check decodepsbt fee calculation (input values shall only be counted once per UTXO) assert_equal(decoded['fee'], created_psbt['fee']) assert_equal(walletprocesspsbt_out['complete'], True) self.nodes[1].sendrawtransaction(self.nodes[1].finalizepsbt(walletprocesspsbt_out['psbt'])['hex']) self.log.info("Test walletcreatefundedpsbt fee rate of 10000 sat/vB and 0.1 CPU/kvB produces a total fee at or slightly below -maxtxfee (~0.05290000)") res1 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"fee_rate": 10000, "add_inputs": True}) assert_approx(res1["fee"], 0.055, 0.005) res2 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"feeRate": "0.1", "add_inputs": True}) assert_approx(res2["fee"], 0.055, 0.005) self.log.info("Test min fee rate checks with walletcreatefundedpsbt are bypassed, e.g. a fee_rate under 1 sat/vB is allowed") res3 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"fee_rate": "0.999", "add_inputs": True}) assert_approx(res3["fee"], 0.00000381, 0.0000001) res4 = self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {"feeRate": 0.00000999, "add_inputs": True}) assert_approx(res4["fee"], 0.00000381, 0.0000001) self.log.info("Test min fee rate checks with walletcreatefundedpsbt are bypassed and that funding non-standard 'zero-fee' transactions is valid") for param, zero_value in product(["fee_rate", "feeRate"], [0, 0.000, 0.00000000, "0", "0.000", "0.00000000"]): assert_equal(0, self.nodes[1].walletcreatefundedpsbt(inputs, outputs, 0, {param: zero_value, "add_inputs": True})["fee"]) self.log.info("Test invalid fee rate settings") for param, value in {("fee_rate", 100000), ("feeRate", 1)}: assert_raises_rpc_error(-4, "Fee exceeds maximum configured by user (e.g. -maxtxfee, maxfeerate)", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: value, "add_inputs": True}) assert_raises_rpc_error(-3, "Amount out of range", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: -1, "add_inputs": True}) assert_raises_rpc_error(-3, "Amount is not a number or string", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: {"foo": "bar"}, "add_inputs": True}) # Test fee rate values that don't pass fixed-point parsing checks. for invalid_value in ["", 0.000000001, 1e-09, 1.111111111, 1111111111111111, "31.999999999999999999999"]: assert_raises_rpc_error(-3, "Invalid amount", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {param: invalid_value, "add_inputs": True}) for invalid_value in [0.0001, 0.00000001, 0.00099999, 31.99999999, "0.0001", "0.00000001", "0.00099999", "31.99999999"]: assert_raises_rpc_error(-3, "Invalid amount", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"fee_rate": invalid_value, "add_inputs": True}) self.log.info("- raises RPC error if both feeRate and fee_rate are passed") assert_raises_rpc_error(-8, "Cannot specify both fee_rate (sat/vB) and feeRate (CPU/kvB)", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"fee_rate": 0.1, "feeRate": 0.1, "add_inputs": True}) self.log.info("- raises RPC error if both feeRate and estimate_mode passed") assert_raises_rpc_error(-8, "Cannot specify both estimate_mode and feeRate", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": "economical", "feeRate": 0.1, "add_inputs": True}) for param in ["feeRate", "fee_rate"]: self.log.info("- raises RPC error if both {} and conf_target are passed".format(param)) assert_raises_rpc_error(-8, "Cannot specify both conf_target and {}. Please provide either a confirmation " "target in blocks for automatic fee estimation, or an explicit fee rate.".format(param), self.nodes[1].walletcreatefundedpsbt ,inputs, outputs, 0, {param: 1, "conf_target": 1, "add_inputs": True}) self.log.info("- raises RPC error if both fee_rate and estimate_mode are passed") assert_raises_rpc_error(-8, "Cannot specify both estimate_mode and fee_rate", self.nodes[1].walletcreatefundedpsbt ,inputs, outputs, 0, {"fee_rate": 1, "estimate_mode": "economical", "add_inputs": True}) self.log.info("- raises RPC error with invalid estimate_mode settings") for k, v in {"number": 42, "object": {"foo": "bar"}}.items(): assert_raises_rpc_error(-3, "Expected type string for estimate_mode, got {}".format(k), self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": v, "conf_target": 0.1, "add_inputs": True}) for mode in ["", "foo", Decimal("3.141592")]: assert_raises_rpc_error(-8, 'Invalid estimate_mode parameter, must be one of: "unset", "economical", "conservative"', self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": 0.1, "add_inputs": True}) self.log.info("- raises RPC error with invalid conf_target settings") for mode in ["unset", "economical", "conservative"]: self.log.debug("{}".format(mode)) for k, v in {"string": "", "object": {"foo": "bar"}}.items(): assert_raises_rpc_error(-3, "Expected type number for conf_target, got {}".format(k), self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": v, "add_inputs": True}) for n in [-1, 0, 1009]: assert_raises_rpc_error(-8, "Invalid conf_target, must be between 1 and 1008", self.nodes[1].walletcreatefundedpsbt, inputs, outputs, 0, {"estimate_mode": mode, "conf_target": n, "add_inputs": True}) self.log.info("Test walletcreatefundedpsbt with too-high fee rate produces total fee well above -maxtxfee and raises RPC error") for bool_add, outputs_array in {True: outputs, False: [{self.nodes[1].getnewaddress(): 1}]}.items(): msg = "Fee exceeds maximum configured by user (e.g. -maxtxfee, maxfeerate)" assert_raises_rpc_error(-4, msg, self.nodes[1].walletcreatefundedpsbt, inputs, outputs_array, 0, {"fee_rate": 1000000, "add_inputs": bool_add}) assert_raises_rpc_error(-4, msg, self.nodes[1].walletcreatefundedpsbt, inputs, outputs_array, 0, {"feeRate": 1, "add_inputs": bool_add}) self.log.info("Test various PSBT operations") psbtx = wmulti.walletcreatefundedpsbt(inputs=[{"txid":txid,"vout":p2wsh_pos},{"txid":txid,"vout":p2sh_pos},{"txid":txid,"vout":p2sh_p2wsh_pos}], outputs={self.nodes[1].getnewaddress():29.99}, options={'changeAddress': self.nodes[1].getrawchangeaddress()})['psbt'] walletprocesspsbt_out = self.nodes[1].walletprocesspsbt(psbtx) psbtx = walletprocesspsbt_out['psbt'] assert_equal(walletprocesspsbt_out['complete'], False) wmulti.unloadwallet() # partially sign with node 2. This should be complete and sendable walletprocesspsbt_out = self.nodes[2].walletprocesspsbt(psbtx) assert_equal(walletprocesspsbt_out['complete'], True) self.nodes[2].sendrawtransaction(self.nodes[2].finalizepsbt(walletprocesspsbt_out['psbt'])['hex']) # check that walletprocesspsbt fails to decode a non-psbt rawtx = self.nodes[1].createrawtransaction([{"txid":txid,"vout":p2wpkh_pos}], {self.nodes[1].getnewaddress():9.99}) assert_raises_rpc_error(-22, "TX decode failed", self.nodes[1].walletprocesspsbt, rawtx) # Convert a non-psbt to psbt and make sure we can decode it rawtx = self.nodes[0].createrawtransaction([], {self.nodes[1].getnewaddress():10}) rawtx = self.nodes[0].fundrawtransaction(rawtx) new_psbt = self.nodes[0].converttopsbt(rawtx['hex']) self.nodes[0].decodepsbt(new_psbt) # Make sure that a non-psbt with signatures cannot be converted # Error could be either "TX decode failed" (segwit inputs causes parsing to fail) or "Inputs must not have scriptSigs and scriptWitnesses" # We must set iswitness=True because the serialized transaction has inputs and is therefore a witness transaction signedtx = self.nodes[0].signrawtransactionwithwallet(rawtx['hex']) assert_raises_rpc_error(-22, "", self.nodes[0].converttopsbt, hexstring=signedtx['hex'], iswitness=True) assert_raises_rpc_error(-22, "", self.nodes[0].converttopsbt, hexstring=signedtx['hex'], permitsigdata=False, iswitness=True) # Unless we allow it to convert and strip signatures self.nodes[0].converttopsbt(signedtx['hex'], True) # Explicitly allow converting non-empty txs new_psbt = self.nodes[0].converttopsbt(rawtx['hex']) self.nodes[0].decodepsbt(new_psbt) # Create outputs to nodes 1 and 2 node1_addr = self.nodes[1].getnewaddress() node2_addr = self.nodes[2].getnewaddress() txid1 = self.nodes[0].sendtoaddress(node1_addr, 13) txid2 = self.nodes[0].sendtoaddress(node2_addr, 13) blockhash = self.nodes[0].generate(6)[0] self.sync_all() vout1 = find_output(self.nodes[1], txid1, 13, blockhash=blockhash) vout2 = find_output(self.nodes[2], txid2, 13, blockhash=blockhash) # Create a psbt spending outputs from nodes 1 and 2 psbt_orig = self.nodes[0].createpsbt([{"txid":txid1, "vout":vout1}, {"txid":txid2, "vout":vout2}], {self.nodes[0].getnewaddress():25.999}) # Update psbts, should only have data for one input and not the other psbt1 = self.nodes[1].walletprocesspsbt(psbt_orig, False, "ALL")['psbt'] psbt1_decoded = self.nodes[0].decodepsbt(psbt1) assert psbt1_decoded['inputs'][0] and not psbt1_decoded['inputs'][1] # Check that BIP32 path was added assert "bip32_derivs" in psbt1_decoded['inputs'][0] psbt2 = self.nodes[2].walletprocesspsbt(psbt_orig, False, "ALL", False)['psbt'] psbt2_decoded = self.nodes[0].decodepsbt(psbt2) assert not psbt2_decoded['inputs'][0] and psbt2_decoded['inputs'][1] # Check that BIP32 paths were not added assert "bip32_derivs" not in psbt2_decoded['inputs'][1] # Sign PSBTs (workaround issue #18039) psbt1 = self.nodes[1].walletprocesspsbt(psbt_orig)['psbt'] psbt2 = self.nodes[2].walletprocesspsbt(psbt_orig)['psbt'] # Combine, finalize, and send the psbts combined = self.nodes[0].combinepsbt([psbt1, psbt2]) finalized = self.nodes[0].finalizepsbt(combined)['hex'] self.nodes[0].sendrawtransaction(finalized) self.nodes[0].generate(6) self.sync_all() # Test additional args in walletcreatepsbt # Make sure both pre-included and funded inputs # have the correct sequence numbers based on # replaceable arg block_height = self.nodes[0].getblockcount() unspent = self.nodes[0].listunspent()[0] psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height+2, {"replaceable": False, "add_inputs": True}, False) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_greater_than(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" not in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], block_height+2) # Same construction with only locktime set and RBF explicitly enabled psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height, {"replaceable": True, "add_inputs": True}, True) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_equal(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], block_height) # Same construction without optional arguments psbtx_info = self.nodes[0].walletcreatefundedpsbt([], [{self.nodes[2].getnewaddress():unspent["amount"]+1}]) decoded_psbt = self.nodes[0].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_equal(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in assert_equal(decoded_psbt["tx"]["locktime"], 0) # Same construction without optional arguments, for a node with -walletrbf=0 unspent1 = self.nodes[1].listunspent()[0] psbtx_info = self.nodes[1].walletcreatefundedpsbt([{"txid":unspent1["txid"], "vout":unspent1["vout"]}], [{self.nodes[2].getnewaddress():unspent1["amount"]+1}], block_height, {"add_inputs": True}) decoded_psbt = self.nodes[1].decodepsbt(psbtx_info["psbt"]) for tx_in, psbt_in in zip(decoded_psbt["tx"]["vin"], decoded_psbt["inputs"]): assert_greater_than(tx_in["sequence"], MAX_BIP125_RBF_SEQUENCE) assert "bip32_derivs" in psbt_in # Make sure change address wallet does not have P2SH innerscript access to results in success # when attempting BnB coin selection self.nodes[0].walletcreatefundedpsbt([], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], block_height+2, {"changeAddress":self.nodes[1].getnewaddress()}, False) # Make sure the wallet's change type is respected by default small_output = {self.nodes[0].getnewaddress():0.1} psbtx_native = self.nodes[0].walletcreatefundedpsbt([], [small_output]) self.assert_change_type(psbtx_native, "witness_v0_keyhash") psbtx_legacy = self.nodes[1].walletcreatefundedpsbt([], [small_output]) self.assert_change_type(psbtx_legacy, "pubkeyhash") psbtx_np2wkh = self.nodes[1].walletcreatefundedpsbt([], [small_output], 0, {"change_type":"p2sh-segwit"}) self.assert_change_type(psbtx_np2wkh, "scripthash") invalid_options = {"change_type":"legacy","changeAddress":self.nodes[0].getnewaddress()} assert_raises_rpc_error(-8, "both change address and address type options", self.nodes[0].walletcreatefundedpsbt, [], [small_output], 0, invalid_options) psbtx_info = self.nodes[0].walletcreatefundedpsbt([{"txid":unspent["txid"], "vout":unspent["vout"]}], [{self.nodes[2].getnewaddress():unspent["amount"]+1}], 0, {"add_inputs": True}) complete_psbt = self.nodes[0].walletprocesspsbt(psbtx_info["psbt"]) double_processed_psbt = self.nodes[0].walletprocesspsbt(complete_psbt["psbt"]) assert_equal(complete_psbt, double_processed_psbt) self.nodes[0].decodepsbt(double_processed_psbt["psbt"]) # Make sure unsafe inputs are included if specified self.nodes[2].createwallet(wallet_name="unsafe") wunsafe = self.nodes[2].get_wallet_rpc("unsafe") self.nodes[0].sendtoaddress(wunsafe.getnewaddress(), 2) self.sync_mempools() assert_raises_rpc_error(-4, "Insufficient funds", wunsafe.walletcreatefundedpsbt, [], [{self.nodes[0].getnewaddress(): 1}]) wunsafe.walletcreatefundedpsbt([], [{self.nodes[0].getnewaddress(): 1}], 0, {"include_unsafe": True}) # BIP 174 Test Vectors # Check that unknown values are just passed through unknown_psbt = "cHNidP8BAD8CAAAAAf//////////////////////////////////////////AAAAAAD/////AQAAAAAAAAAAA2oBAAAAAAAACg8BAgMEBQYHCAkPAQIDBAUGBwgJCgsMDQ4PAAA=" unknown_out = self.nodes[0].walletprocesspsbt(unknown_psbt)['psbt'] assert_equal(unknown_psbt, unknown_out) # Open the data file with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data/rpc_psbt.json'), encoding='utf-8') as f: d = json.load(f) invalids = d['invalid'] valids = d['valid'] creators = d['creator'] signers = d['signer'] combiners = d['combiner'] finalizers = d['finalizer'] extractors = d['extractor'] # Invalid PSBTs for invalid in invalids: assert_raises_rpc_error(-22, "TX decode failed", self.nodes[0].decodepsbt, invalid) # Valid PSBTs for valid in valids: self.nodes[0].decodepsbt(valid) # Creator Tests for creator in creators: created_tx = self.nodes[0].createpsbt(creator['inputs'], creator['outputs']) assert_equal(created_tx, creator['result']) # Signer tests for i, signer in enumerate(signers): self.nodes[2].createwallet(wallet_name="wallet{}".format(i)) wrpc = self.nodes[2].get_wallet_rpc("wallet{}".format(i)) for key in signer['privkeys']: wrpc.importprivkey(key) signed_tx = wrpc.walletprocesspsbt(signer['psbt'])['psbt'] assert_equal(signed_tx, signer['result']) # Combiner test for combiner in combiners: combined = self.nodes[2].combinepsbt(combiner['combine']) assert_equal(combined, combiner['result']) # Empty combiner test assert_raises_rpc_error(-8, "Parameter 'txs' cannot be empty", self.nodes[0].combinepsbt, []) # Finalizer test for finalizer in finalizers: finalized = self.nodes[2].finalizepsbt(finalizer['finalize'], False)['psbt'] assert_equal(finalized, finalizer['result']) # Extractor test for extractor in extractors: extracted = self.nodes[2].finalizepsbt(extractor['extract'], True)['hex'] assert_equal(extracted, extractor['result']) # Unload extra wallets for i, signer in enumerate(signers): self.nodes[2].unloadwallet("wallet{}".format(i)) # TODO: Re-enable this for segwit v1 # self.test_utxo_conversion() # Test that psbts with p2pkh outputs are created properly p2pkh = self.nodes[0].getnewaddress(address_type='legacy') psbt = self.nodes[1].walletcreatefundedpsbt([], [{p2pkh : 1}], 0, {"includeWatching" : True}, True) self.nodes[0].decodepsbt(psbt['psbt']) # Test decoding error: invalid base64 assert_raises_rpc_error(-22, "TX decode failed invalid base64", self.nodes[0].decodepsbt, ";definitely not base64;") # Send to all types of addresses addr1 = self.nodes[1].getnewaddress("", "bech32") txid1 = self.nodes[0].sendtoaddress(addr1, 11) vout1 = find_output(self.nodes[0], txid1, 11) addr2 = self.nodes[1].getnewaddress("", "legacy") txid2 = self.nodes[0].sendtoaddress(addr2, 11) vout2 = find_output(self.nodes[0], txid2, 11) addr3 = self.nodes[1].getnewaddress("", "p2sh-segwit") txid3 = self.nodes[0].sendtoaddress(addr3, 11) vout3 = find_output(self.nodes[0], txid3, 11) self.sync_all() def test_psbt_input_keys(psbt_input, keys): assert_equal(set(keys), set(psbt_input.keys())) # Create a PSBT. None of the inputs are filled initially psbt = self.nodes[1].createpsbt([{"txid":txid1, "vout":vout1},{"txid":txid2, "vout":vout2},{"txid":txid3, "vout":vout3}], {self.nodes[0].getnewaddress():32.999}) decoded = self.nodes[1].decodepsbt(psbt) test_psbt_input_keys(decoded['inputs'][0], []) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], []) # Update a PSBT with UTXOs from the node # Bech32 inputs should be filled with witness UTXO. Other inputs should not be filled because they are non-witness updated = self.nodes[1].utxoupdatepsbt(psbt) decoded = self.nodes[1].decodepsbt(updated) test_psbt_input_keys(decoded['inputs'][0], ['witness_utxo']) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], []) # Try again, now while providing descriptors, making P2SH-segwit work, and causing bip32_derivs and redeem_script to be filled in descs = [self.nodes[1].getaddressinfo(addr)['desc'] for addr in [addr1,addr2,addr3]] updated = self.nodes[1].utxoupdatepsbt(psbt=psbt, descriptors=descs) decoded = self.nodes[1].decodepsbt(updated) test_psbt_input_keys(decoded['inputs'][0], ['witness_utxo', 'bip32_derivs']) test_psbt_input_keys(decoded['inputs'][1], []) test_psbt_input_keys(decoded['inputs'][2], ['witness_utxo', 'bip32_derivs', 'redeem_script']) # Two PSBTs with a common input should not be joinable psbt1 = self.nodes[1].createpsbt([{"txid":txid1, "vout":vout1}], {self.nodes[0].getnewaddress():Decimal('10.999')}) assert_raises_rpc_error(-8, "exists in multiple PSBTs", self.nodes[1].joinpsbts, [psbt1, updated]) # Join two distinct PSBTs addr4 = self.nodes[1].getnewaddress("", "p2sh-segwit") txid4 = self.nodes[0].sendtoaddress(addr4, 5) vout4 = find_output(self.nodes[0], txid4, 5) self.nodes[0].generate(6) self.sync_all() psbt2 = self.nodes[1].createpsbt([{"txid":txid4, "vout":vout4}], {self.nodes[0].getnewaddress():Decimal('4.999')}) psbt2 = self.nodes[1].walletprocesspsbt(psbt2)['psbt'] psbt2_decoded = self.nodes[0].decodepsbt(psbt2) assert "final_scriptwitness" in psbt2_decoded['inputs'][0] and "final_scriptSig" in psbt2_decoded['inputs'][0] joined = self.nodes[0].joinpsbts([psbt, psbt2]) joined_decoded = self.nodes[0].decodepsbt(joined) assert len(joined_decoded['inputs']) == 4 and len(joined_decoded['outputs']) == 2 and "final_scriptwitness" not in joined_decoded['inputs'][3] and "final_scriptSig" not in joined_decoded['inputs'][3] # Check that joining shuffles the inputs and outputs # 10 attempts should be enough to get a shuffled join shuffled = False for _ in range(10): shuffled_joined = self.nodes[0].joinpsbts([psbt, psbt2]) shuffled |= joined != shuffled_joined if shuffled: break assert shuffled # Newly created PSBT needs UTXOs and updating addr = self.nodes[1].getnewaddress("", "p2sh-segwit") txid = self.nodes[0].sendtoaddress(addr, 7) addrinfo = self.nodes[1].getaddressinfo(addr) blockhash = self.nodes[0].generate(6)[0] self.sync_all() vout = find_output(self.nodes[0], txid, 7, blockhash=blockhash) psbt = self.nodes[1].createpsbt([{"txid":txid, "vout":vout}], {self.nodes[0].getnewaddress("", "p2sh-segwit"):Decimal('6.999')}) analyzed = self.nodes[0].analyzepsbt(psbt) assert not analyzed['inputs'][0]['has_utxo'] and not analyzed['inputs'][0]['is_final'] and analyzed['inputs'][0]['next'] == 'updater' and analyzed['next'] == 'updater' # After update with wallet, only needs signing updated = self.nodes[1].walletprocesspsbt(psbt, False, 'ALL', True)['psbt'] analyzed = self.nodes[0].analyzepsbt(updated) assert analyzed['inputs'][0]['has_utxo'] and not analyzed['inputs'][0]['is_final'] and analyzed['inputs'][0]['next'] == 'signer' and analyzed['next'] == 'signer' and analyzed['inputs'][0]['missing']['signatures'][0] == addrinfo['embedded']['witness_program'] # Check fee and size things assert analyzed['fee'] == Decimal('0.001') and analyzed['estimated_vsize'] == 134 and analyzed['estimated_feerate'] == Decimal('0.00746268') # After signing and finalizing, needs extracting signed = self.nodes[1].walletprocesspsbt(updated)['psbt'] analyzed = self.nodes[0].analyzepsbt(signed) assert analyzed['inputs'][0]['has_utxo'] and analyzed['inputs'][0]['is_final'] and analyzed['next'] == 'extractor' self.log.info("PSBT spending unspendable outputs should have error message and Creator as next") analysis = self.nodes[0].analyzepsbt('cHNidP8BAJoCAAAAAljoeiG1ba8MI76OcHBFbDNvfLqlyHV5JPVFiHuyq911AAAAAAD/////g40EJ9DsZQpoqka7CwmK6kQiwHGyyng1Kgd5WdB86h0BAAAAAP////8CcKrwCAAAAAAWAEHYXCtx0AYLCcmIauuBXlCZHdoSTQDh9QUAAAAAFv8/wADXYP/7//////8JxOh0LR2HAI8AAAAAAAEBIADC6wsAAAAAF2oUt/X69ELjeX2nTof+fZ10l+OyAokDAQcJAwEHEAABAACAAAEBIADC6wsAAAAAF2oUt/X69ELjeX2nTof+fZ10l+OyAokDAQcJAwEHENkMak8AAAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 spends unspendable output') self.log.info("PSBT with invalid values should have error message and Creator as next") analysis = self.nodes[0].analyzepsbt('cHNidP8BAHECAAAAAfA00BFgAm6tp86RowwH6BMImQNL5zXUcTT97XoLGz0BAAAAAAD/////AgD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XL87QKVAAAAABYAFPck4gF7iL4NL4wtfRAKgQbghiTUAAAAAAABAR8AgIFq49AHABYAFJUDtxf2PHo641HEOBOAIvFMNTr2AAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 has invalid value') self.log.info("PSBT with signed, but not finalized, inputs should have Finalizer as next") analysis = self.nodes[0].analyzepsbt('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') assert_equal(analysis['next'], 'finalizer') analysis = self.nodes[0].analyzepsbt('cHNidP8BAHECAAAAAfA00BFgAm6tp86RowwH6BMImQNL5zXUcTT97XoLGz0BAAAAAAD/////AgCAgWrj0AcAFgAUKNw0x8HRctAgmvoevm4u1SbN7XL87QKVAAAAABYAFPck4gF7iL4NL4wtfRAKgQbghiTUAAAAAAABAR8A8gUqAQAAABYAFJUDtxf2PHo641HEOBOAIvFMNTr2AAAA') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Output amount invalid') analysis = self.nodes[0].analyzepsbt('cHNidP8BAJoCAAAAAkvEW8NnDtdNtDpsmze+Ht2LH35IJcKv00jKAlUs21RrAwAAAAD/////S8Rbw2cO1020OmybN74e3Ysffkglwq/TSMoCVSzbVGsBAAAAAP7///8CwLYClQAAAAAWABSNJKzjaUb3uOxixsvh1GGE3fW7zQD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XIAAAAAAAEAnQIAAAACczMa321tVHuN4GKWKRncycI22aX3uXgwSFUKM2orjRsBAAAAAP7///9zMxrfbW1Ue43gYpYpGdzJwjbZpfe5eDBIVQozaiuNGwAAAAAA/v///wIA+QKVAAAAABl2qRT9zXUVA8Ls5iVqynLHe5/vSe1XyYisQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAAAAAQEfQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAA==') assert_equal(analysis['next'], 'creator') assert_equal(analysis['error'], 'PSBT is not valid. Input 0 specifies invalid prevout') assert_raises_rpc_error(-25, 'Inputs missing or spent', self.nodes[0].walletprocesspsbt, 'cHNidP8BAJoCAAAAAkvEW8NnDtdNtDpsmze+Ht2LH35IJcKv00jKAlUs21RrAwAAAAD/////S8Rbw2cO1020OmybN74e3Ysffkglwq/TSMoCVSzbVGsBAAAAAP7///8CwLYClQAAAAAWABSNJKzjaUb3uOxixsvh1GGE3fW7zQD5ApUAAAAAFgAUKNw0x8HRctAgmvoevm4u1SbN7XIAAAAAAAEAnQIAAAACczMa321tVHuN4GKWKRncycI22aX3uXgwSFUKM2orjRsBAAAAAP7///9zMxrfbW1Ue43gYpYpGdzJwjbZpfe5eDBIVQozaiuNGwAAAAAA/v///wIA+QKVAAAAABl2qRT9zXUVA8Ls5iVqynLHe5/vSe1XyYisQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAAAAAQEfQM0ClQAAAAAWABRmWQUcjSjghQ8/uH4Bn/zkakwLtAAAAA==') if __name__ == '__main__': PSBTTest().main()

true

f72001fbbd35b8a678f898e1db551767c3665048

30,425

py

Python

src/waldur_azure/migrations/0003_redesign.py

opennode/nodeconductor-assembly-waldur

cad9966389dc9b52b13d2301940c99cf4b243900

[ "MIT" ]

2

2017-01-20T15:26:25.000Z

2017-08-03T04:38:08.000Z

src/waldur_azure/migrations/0003_redesign.py

opennode/nodeconductor-assembly-waldur

cad9966389dc9b52b13d2301940c99cf4b243900

[ "MIT" ]

null

src/waldur_azure/migrations/0003_redesign.py

opennode/nodeconductor-assembly-waldur

cad9966389dc9b52b13d2301940c99cf4b243900

[ "MIT" ]

null

# Generated by Django 1.11.18 on 2019-01-28 14:05 import re import django.core.validators import django.db.models.deletion import django.utils.timezone import django_fsm import model_utils.fields from django.db import migrations, models import waldur_azure.validators import waldur_core.core.fields import waldur_core.core.models import waldur_core.core.shims import waldur_core.core.validators import waldur_core.logging.loggers class Migration(migrations.Migration): dependencies = [ ('structure', '0005_customer_domain'), ('core', '0003_enlarge_username'), ('waldur_azure', '0002_immutable_default_json'), ] operations = [ migrations.CreateModel( name='Location', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('latitude', models.FloatField(blank=True, null=True)), ('longitude', models.FloatField(blank=True, null=True)), ('backend_id', models.CharField(db_index=True, max_length=255)), ( 'settings', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), ), ], options={ 'abstract': False, }, bases=(waldur_core.core.models.BackendModelMixin, models.Model), ), migrations.CreateModel( name='Network', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=64, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('cidr', models.CharField(max_length=32)), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='NetworkInterface', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=80, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('config_name', models.CharField(max_length=255)), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='ResourceGroup', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=90, validators=[ django.core.validators.RegexValidator( message='The name can include alphanumeric, underscore, parentheses, hyphen, period (except at end), and Unicode characters that match the allowed characters.', regex=re.compile('^[-\\w._()]+$'), ) ], ), ), ( 'location', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Location', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='Size', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('backend_id', models.CharField(db_index=True, max_length=255)), ('max_data_disk_count', models.PositiveIntegerField()), ('memory_in_mb', models.PositiveIntegerField()), ('number_of_cores', models.PositiveIntegerField()), ('os_disk_size_in_mb', models.PositiveIntegerField()), ('resource_disk_size_in_mb', models.PositiveIntegerField()), ( 'settings', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), ), ], options={ 'abstract': False, }, bases=(waldur_core.core.models.BackendModelMixin, models.Model), ), migrations.CreateModel( name='SQLDatabase', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ('charset', models.CharField(blank=True, max_length=255)), ('collation', models.CharField(blank=True, max_length=255)), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='SQLServer', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ( 'name', models.CharField( max_length=150, validators=[ django.core.validators.RegexValidator( message='The name can only be made up of lowercase letters "a"-"z", the numbers 0-9 and the hyphen. The hyphen may not lead or trail in the name.', regex=re.compile('[a-z0-9][a-z0-9-]+[a-z0-9]$'), ) ], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ('username', models.CharField(max_length=50)), ('password', models.CharField(max_length=50)), ('storage_mb', models.PositiveIntegerField(null=True)), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='SubNet', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=80, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('cidr', models.CharField(max_length=32)), ( 'network', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Network', ), ), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.RemoveField( model_name='instanceendpoint', name='instance', ), migrations.RemoveField( model_name='virtualmachine', name='private_ips', ), migrations.RemoveField( model_name='virtualmachine', name='public_ips', ), migrations.RemoveField( model_name='virtualmachine', name='user_password', ), migrations.RemoveField( model_name='virtualmachine', name='user_username', ), migrations.AddField( model_name='image', name='offer', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='publisher', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='settings', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), preserve_default=False, ), migrations.AddField( model_name='image', name='sku', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='version', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='image', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Image', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='password', field=models.CharField( default=None, max_length=72, validators=[ django.core.validators.MinLengthValidator(6), django.core.validators.MaxLengthValidator(72), waldur_azure.validators.validate_password, ], ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='ssh_key', field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.SshPublicKey', ), ), migrations.AddField( model_name='virtualmachine', name='username', field=models.CharField( default=None, max_length=32, validators=[waldur_azure.validators.VirtualMachineUsernameValidator], ), preserve_default=False, ), migrations.AlterField( model_name='image', name='backend_id', field=models.CharField(db_index=True, max_length=255), ), migrations.AlterField( model_name='virtualmachine', name='name', field=models.CharField( max_length=15, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, and hyphens. The name must be shorter than 15 characters and start with a letter and must end with a letter or a number.', regex=re.compile('[a-zA-Z][a-zA-Z0-9-]{0,13}[a-zA-Z0-9]$'), ) ], ), ), migrations.AlterUniqueTogether( name='image', unique_together=set([('settings', 'backend_id')]), ), migrations.DeleteModel( name='InstanceEndpoint', ), migrations.AddField( model_name='sqldatabase', name='server', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.SQLServer' ), ), migrations.AddField( model_name='networkinterface', name='resource_group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), migrations.AddField( model_name='networkinterface', name='subnet', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.SubNet' ), ), migrations.AddField( model_name='network', name='resource_group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), migrations.AddField( model_name='virtualmachine', name='network_interface', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.NetworkInterface', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='resource_group', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='size', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Size', ), preserve_default=False, ), migrations.AlterUniqueTogether( name='size', unique_together=set([('settings', 'backend_id')]), ), ]

36.745169

199

0.396647

import re import django.core.validators import django.db.models.deletion import django.utils.timezone import django_fsm import model_utils.fields from django.db import migrations, models import waldur_azure.validators import waldur_core.core.fields import waldur_core.core.models import waldur_core.core.shims import waldur_core.core.validators import waldur_core.logging.loggers class Migration(migrations.Migration): dependencies = [ ('structure', '0005_customer_domain'), ('core', '0003_enlarge_username'), ('waldur_azure', '0002_immutable_default_json'), ] operations = [ migrations.CreateModel( name='Location', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('latitude', models.FloatField(blank=True, null=True)), ('longitude', models.FloatField(blank=True, null=True)), ('backend_id', models.CharField(db_index=True, max_length=255)), ( 'settings', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), ), ], options={ 'abstract': False, }, bases=(waldur_core.core.models.BackendModelMixin, models.Model), ), migrations.CreateModel( name='Network', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=64, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('cidr', models.CharField(max_length=32)), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='NetworkInterface', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=80, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('config_name', models.CharField(max_length=255)), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='ResourceGroup', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=90, validators=[ django.core.validators.RegexValidator( message='The name can include alphanumeric, underscore, parentheses, hyphen, period (except at end), and Unicode characters that match the allowed characters.', regex=re.compile('^[-\\w._()]+$'), ) ], ), ), ( 'location', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Location', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='Size', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('backend_id', models.CharField(db_index=True, max_length=255)), ('max_data_disk_count', models.PositiveIntegerField()), ('memory_in_mb', models.PositiveIntegerField()), ('number_of_cores', models.PositiveIntegerField()), ('os_disk_size_in_mb', models.PositiveIntegerField()), ('resource_disk_size_in_mb', models.PositiveIntegerField()), ( 'settings', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), ), ], options={ 'abstract': False, }, bases=(waldur_core.core.models.BackendModelMixin, models.Model), ), migrations.CreateModel( name='SQLDatabase', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ( 'name', models.CharField( max_length=150, validators=[waldur_core.core.validators.validate_name], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ('charset', models.CharField(blank=True, max_length=255)), ('collation', models.CharField(blank=True, max_length=255)), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='SQLServer', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ( 'name', models.CharField( max_length=150, validators=[ django.core.validators.RegexValidator( message='The name can only be made up of lowercase letters "a"-"z", the numbers 0-9 and the hyphen. The hyphen may not lead or trail in the name.', regex=re.compile('[a-z0-9][a-z0-9-]+[a-z0-9]$'), ) ], verbose_name='name', ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ('username', models.CharField(max_length=50)), ('password', models.CharField(max_length=50)), ('storage_mb', models.PositiveIntegerField(null=True)), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.CreateModel( name='SubNet', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ( 'created', model_utils.fields.AutoCreatedField( default=django.utils.timezone.now, editable=False, verbose_name='created', ), ), ( 'modified', model_utils.fields.AutoLastModifiedField( default=django.utils.timezone.now, editable=False, verbose_name='modified', ), ), ( 'description', models.CharField( blank=True, max_length=500, verbose_name='description' ), ), ('uuid', waldur_core.core.fields.UUIDField()), ('error_message', models.TextField(blank=True)), ( 'runtime_state', models.CharField( blank=True, max_length=150, verbose_name='runtime state' ), ), ( 'state', django_fsm.FSMIntegerField( choices=[ (5, 'Creation Scheduled'), (6, 'Creating'), (1, 'Update Scheduled'), (2, 'Updating'), (7, 'Deletion Scheduled'), (8, 'Deleting'), (3, 'OK'), (4, 'Erred'), ], default=5, ), ), ('backend_id', models.CharField(blank=True, max_length=255)), ( 'name', models.CharField( max_length=80, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, underscore, period and hyphens.', regex=re.compile('[a-zA-Z][a-zA-Z0-9._-]+$'), ) ], ), ), ('cidr', models.CharField(max_length=32)), ( 'network', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Network', ), ), ( 'resource_group', models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), ], options={ 'abstract': False, }, bases=( waldur_core.core.models.DescendantMixin, waldur_core.core.models.BackendModelMixin, waldur_core.logging.loggers.LoggableMixin, models.Model, ), ), migrations.RemoveField( model_name='instanceendpoint', name='instance', ), migrations.RemoveField( model_name='virtualmachine', name='private_ips', ), migrations.RemoveField( model_name='virtualmachine', name='public_ips', ), migrations.RemoveField( model_name='virtualmachine', name='user_password', ), migrations.RemoveField( model_name='virtualmachine', name='user_username', ), migrations.AddField( model_name='image', name='offer', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='publisher', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='settings', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, related_name='+', to='structure.ServiceSettings', ), preserve_default=False, ), migrations.AddField( model_name='image', name='sku', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='image', name='version', field=models.CharField(default=None, max_length=255), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='image', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Image', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='password', field=models.CharField( default=None, max_length=72, validators=[ django.core.validators.MinLengthValidator(6), django.core.validators.MaxLengthValidator(72), waldur_azure.validators.validate_password, ], ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='ssh_key', field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='core.SshPublicKey', ), ), migrations.AddField( model_name='virtualmachine', name='username', field=models.CharField( default=None, max_length=32, validators=[waldur_azure.validators.VirtualMachineUsernameValidator], ), preserve_default=False, ), migrations.AlterField( model_name='image', name='backend_id', field=models.CharField(db_index=True, max_length=255), ), migrations.AlterField( model_name='virtualmachine', name='name', field=models.CharField( max_length=15, validators=[ django.core.validators.RegexValidator( message='The name can contain only letters, numbers, and hyphens. The name must be shorter than 15 characters and start with a letter and must end with a letter or a number.', regex=re.compile('[a-zA-Z][a-zA-Z0-9-]{0,13}[a-zA-Z0-9]$'), ) ], ), ), migrations.AlterUniqueTogether( name='image', unique_together=set([('settings', 'backend_id')]), ), migrations.DeleteModel( name='InstanceEndpoint', ), migrations.AddField( model_name='sqldatabase', name='server', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.SQLServer' ), ), migrations.AddField( model_name='networkinterface', name='resource_group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), migrations.AddField( model_name='networkinterface', name='subnet', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.SubNet' ), ), migrations.AddField( model_name='network', name='resource_group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), ), migrations.AddField( model_name='virtualmachine', name='network_interface', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.NetworkInterface', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='resource_group', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.ResourceGroup', ), preserve_default=False, ), migrations.AddField( model_name='virtualmachine', name='size', field=models.ForeignKey( default=None, on_delete=django.db.models.deletion.CASCADE, to='waldur_azure.Size', ), preserve_default=False, ), migrations.AlterUniqueTogether( name='size', unique_together=set([('settings', 'backend_id')]), ), ]

true

f7200243f3b4d289ac50951e7f5c03cf8e464b4c

7,970

py

Python

src/pyrad_proc/pyrad/EGG-INFO/scripts/rewrite_monitoring.py

jfigui/pyrad

7811d593bb09a7f8a621c0e8ae3f32c2b85a0254

[ "BSD-3-Clause" ]

41

2016-12-01T08:46:06.000Z

2021-06-24T21:14:33.000Z

src/pyrad_proc/pyrad/EGG-INFO/scripts/rewrite_monitoring.py

jfigui/pyrad

7811d593bb09a7f8a621c0e8ae3f32c2b85a0254

[ "BSD-3-Clause" ]

42

2017-02-23T14:52:49.000Z

2021-02-01T10:43:52.000Z

src/pyrad_proc/pyrad/EGG-INFO/scripts/rewrite_monitoring.py

jfigui/pyrad

7811d593bb09a7f8a621c0e8ae3f32c2b85a0254

[ "BSD-3-Clause" ]

21

2016-08-25T15:02:12.000Z

2021-05-27T04:09:40.000Z

#!/home/daniel/anaconda3/bin/python # -*- coding: utf-8 -*- """ ================================================ rewrite_monitoring ================================================ This program rewrites a monitoring time series files into the correct time order """ # Author: fvj # License: BSD 3 clause import datetime import atexit import numpy as np import os from pyrad.io import read_monitoring_ts, write_monitoring_ts from pyrad.graph import plot_monitoring_ts from pyrad.io import generate_field_name_str, get_fieldname_pyart print(__doc__) def main(): """ """ input_base = ( '/store/msrad/radar/pyrad_products/') output_base = ( '/store/msrad/radar/pyrad_products/') rad_vec = ['D'] var_vec = ['PhiDP0', 'RhoHV_rain', 'ZDR_prec', 'ZDR_snow', 'dBZ_bias'] year_vec = [datetime.datetime(2018, 1, 1)] plot_data = True print("====== Monitoring rewriting started: %s" % datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) atexit.register(_print_end_msg, "====== Monitoring rewriting finished: ") for i, rad in enumerate(rad_vec): print('Processing Radar '+rad) for j, var in enumerate(var_vec): if var == 'dBZ': basedir = 'rad4alp_gc_PH'+rad dsdir = 'monitoring_clt_Zh' mon_type = 'GC_MONITORING' quantiles = [50., 95., 99.] elif var == 'dBZv': basedir = 'rad4alp_gc_PH'+rad dsdir = 'monitoring_clt_Zv' mon_type = 'GC_MONITORING' quantiles = [50., 95., 99.] elif var == 'RhoHV_rain': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_RhoHV' mon_type = 'MONITORING' quantiles = [65., 80., 95.] elif var == 'PhiDP0': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_PhiDP0' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'ZDR_prec': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_ZDR' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'ZDR_snow': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_ZDR_snow' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'dBZ_bias': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_Zh_bias' mon_type = 'MONITORING' quantiles = [25., 50., 75.] input_path = input_base+basedir+'/'+dsdir+'/VOL_TS/' output_path = output_base+basedir+'/'+dsdir+'/VOL_TS/' if not os.path.isdir(output_path): os.makedirs(output_path) print('- Processing Variable '+var) for k, year in enumerate(year_vec): print('-- Processing Year '+year.strftime('%Y')) fname_input = ( input_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.csv') fname_output = ( output_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.csv') figfname = [ output_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.png'] date, np_t_vec, cquant_vec, lquant_vec, hquant_vec = ( read_monitoring_ts(fname_input, sort_by_date=True)) if date is None: continue val_vec = np.ma.asarray( [lquant_vec, cquant_vec, hquant_vec]).T fname = write_monitoring_ts( date, np_t_vec, val_vec, quantiles, var, fname_output, rewrite=True) print('written file '+fname) if not plot_data: continue titldate = (date[0].strftime('%Y%m%d')+'-' + date[-1].strftime('%Y%m%d')) titl = rad+' Monitoring '+titldate labely = generate_field_name_str(var) if var == 'dBZ': if rad == 'A': ref_value = 49.5 vmin = 44.5 vmax = 54.5 np_min = 100000 elif rad == 'D': ref_value = 48.5 vmin = 43.5 vmax = 53.5 np_min = 20000 elif rad == 'L': ref_value = 67. vmin = 62. vmax = 72. np_min = 100000 elif rad == 'P': ref_value = 69. vmin = 64. vmax = 74. np_min = 100000 elif rad == 'W': ref_value = 27.5 vmin = 22.5 vmax = 32.5 np_min = 100000 elif var == 'dBZv': if rad == 'A': ref_value = 51.5 vmin = 46.5 vmax = 56.5 np_min = 100000 elif rad == 'D': ref_value = 50.5 vmin = 45.5 vmax = 55.5 np_min = 20000 elif rad == 'L': ref_value = 69.5 vmin = 64.5 vmax = 74.5 np_min = 100000 elif rad == 'P': ref_value = 68.5 vmin = 63.5 vmax = 73.5 np_min = 100000 elif rad == 'W': ref_value = 26.5 vmin = 21.5 vmax = 31.5 np_min = 100000 elif var == 'RhoHV_rain': ref_value = 0.99 vmin = 0.95 vmax = 1.01 np_min = 5000 elif var == 'PhiDP0': ref_value = 0. vmin = -20. vmax = 20. np_min = 500000 elif var == 'ZDR_prec': ref_value = 0.2 vmin = -2. vmax = 2. np_min = 5000 elif var == 'ZDR_snow': ref_value = 0.2 vmin = -2. vmax = 2. np_min = 5000 elif var == 'dBZ_bias': ref_value = 0. vmin = -30. vmax = 30. np_min = 100 fname = plot_monitoring_ts( date, np_t_vec, cquant_vec, lquant_vec, hquant_vec, get_fieldname_pyart(var), figfname, ref_value=ref_value, vmin=vmin, vmax=vmax, np_min=np_min, labelx='Time UTC', labely=labely, titl=titl) print('plotted file '+' '.join(fname)) def _print_end_msg(text): """ prints end message Parameters ---------- text : str the text to be printed Returns ------- Nothing """ print(text + datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) # --------------------------------------------------------- # Start main: # --------------------------------------------------------- if __name__ == "__main__": main()

33.628692

77

0.408281

import datetime import atexit import numpy as np import os from pyrad.io import read_monitoring_ts, write_monitoring_ts from pyrad.graph import plot_monitoring_ts from pyrad.io import generate_field_name_str, get_fieldname_pyart print(__doc__) def main(): input_base = ( '/store/msrad/radar/pyrad_products/') output_base = ( '/store/msrad/radar/pyrad_products/') rad_vec = ['D'] var_vec = ['PhiDP0', 'RhoHV_rain', 'ZDR_prec', 'ZDR_snow', 'dBZ_bias'] year_vec = [datetime.datetime(2018, 1, 1)] plot_data = True print("====== Monitoring rewriting started: %s" % datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) atexit.register(_print_end_msg, "====== Monitoring rewriting finished: ") for i, rad in enumerate(rad_vec): print('Processing Radar '+rad) for j, var in enumerate(var_vec): if var == 'dBZ': basedir = 'rad4alp_gc_PH'+rad dsdir = 'monitoring_clt_Zh' mon_type = 'GC_MONITORING' quantiles = [50., 95., 99.] elif var == 'dBZv': basedir = 'rad4alp_gc_PH'+rad dsdir = 'monitoring_clt_Zv' mon_type = 'GC_MONITORING' quantiles = [50., 95., 99.] elif var == 'RhoHV_rain': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_RhoHV' mon_type = 'MONITORING' quantiles = [65., 80., 95.] elif var == 'PhiDP0': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_PhiDP0' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'ZDR_prec': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_ZDR' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'ZDR_snow': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_ZDR_snow' mon_type = 'MONITORING' quantiles = [25., 50., 75.] elif var == 'dBZ_bias': basedir = 'rad4alp_dataquality_PL'+rad dsdir = 'monitoring_Zh_bias' mon_type = 'MONITORING' quantiles = [25., 50., 75.] input_path = input_base+basedir+'/'+dsdir+'/VOL_TS/' output_path = output_base+basedir+'/'+dsdir+'/VOL_TS/' if not os.path.isdir(output_path): os.makedirs(output_path) print('- Processing Variable '+var) for k, year in enumerate(year_vec): print('-- Processing Year '+year.strftime('%Y')) fname_input = ( input_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.csv') fname_output = ( output_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.csv') figfname = [ output_path+year.strftime('%Y')+'_'+rad + '_ts_'+mon_type+'_'+var+'.png'] date, np_t_vec, cquant_vec, lquant_vec, hquant_vec = ( read_monitoring_ts(fname_input, sort_by_date=True)) if date is None: continue val_vec = np.ma.asarray( [lquant_vec, cquant_vec, hquant_vec]).T fname = write_monitoring_ts( date, np_t_vec, val_vec, quantiles, var, fname_output, rewrite=True) print('written file '+fname) if not plot_data: continue titldate = (date[0].strftime('%Y%m%d')+'-' + date[-1].strftime('%Y%m%d')) titl = rad+' Monitoring '+titldate labely = generate_field_name_str(var) if var == 'dBZ': if rad == 'A': ref_value = 49.5 vmin = 44.5 vmax = 54.5 np_min = 100000 elif rad == 'D': ref_value = 48.5 vmin = 43.5 vmax = 53.5 np_min = 20000 elif rad == 'L': ref_value = 67. vmin = 62. vmax = 72. np_min = 100000 elif rad == 'P': ref_value = 69. vmin = 64. vmax = 74. np_min = 100000 elif rad == 'W': ref_value = 27.5 vmin = 22.5 vmax = 32.5 np_min = 100000 elif var == 'dBZv': if rad == 'A': ref_value = 51.5 vmin = 46.5 vmax = 56.5 np_min = 100000 elif rad == 'D': ref_value = 50.5 vmin = 45.5 vmax = 55.5 np_min = 20000 elif rad == 'L': ref_value = 69.5 vmin = 64.5 vmax = 74.5 np_min = 100000 elif rad == 'P': ref_value = 68.5 vmin = 63.5 vmax = 73.5 np_min = 100000 elif rad == 'W': ref_value = 26.5 vmin = 21.5 vmax = 31.5 np_min = 100000 elif var == 'RhoHV_rain': ref_value = 0.99 vmin = 0.95 vmax = 1.01 np_min = 5000 elif var == 'PhiDP0': ref_value = 0. vmin = -20. vmax = 20. np_min = 500000 elif var == 'ZDR_prec': ref_value = 0.2 vmin = -2. vmax = 2. np_min = 5000 elif var == 'ZDR_snow': ref_value = 0.2 vmin = -2. vmax = 2. np_min = 5000 elif var == 'dBZ_bias': ref_value = 0. vmin = -30. vmax = 30. np_min = 100 fname = plot_monitoring_ts( date, np_t_vec, cquant_vec, lquant_vec, hquant_vec, get_fieldname_pyart(var), figfname, ref_value=ref_value, vmin=vmin, vmax=vmax, np_min=np_min, labelx='Time UTC', labely=labely, titl=titl) print('plotted file '+' '.join(fname)) def _print_end_msg(text): print(text + datetime.datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S")) if __name__ == "__main__": main()

true

f72002f8e1ad1752270b6c4051b237ce04dec27e

13,299

py

Python

Scripts/simulation/interactions/jog_interaction.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

null

Scripts/simulation/interactions/jog_interaction.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

null

Scripts/simulation/interactions/jog_interaction.py

velocist/TS4CheatsInfo

b59ea7e5f4bd01d3b3bd7603843d525a9c179867

[ "Apache-2.0" ]

null

# uncompyle6 version 3.7.4 # Python bytecode 3.7 (3394) # Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)] # Embedded file name: T:\InGame\Gameplay\Scripts\Server\interactions\jog_interaction.py # Compiled at: 2020-07-22 05:56:20 # Size of source mod 2**32: 16676 bytes from _math import Vector3 import itertools, random from balloon.tunable_balloon import TunableBalloon from element_utils import do_all from event_testing.results import TestResult from interactions import TargetType from interactions.base.super_interaction import SuperInteraction from interactions.constraints import Circle, ANYWHERE from interactions.utils.routing import FollowPath, PlanRoute, get_route_element_for_path from routing.walkstyle.walkstyle_request import WalkStyleRequest from routing.waypoints.waypoint_generator_variant import TunableWaypointGeneratorVariant from routing.waypoints.waypoint_stitching import WaypointStitchingVariant from sims4 import random from sims4.tuning.tunable import TunableRange, Tunable, OptionalTunable from sims4.tuning.tunable_base import GroupNames from sims4.utils import flexmethod import element_utils, routing, sims4.log logger = sims4.log.Logger('WaypointInteraction') class _WaypointGeneratorRallyable: def __init__(self, waypoint_info): self._original_generator = waypoint_info def get_start_constraint(self): return self._original_generator.get_start_constraint() def get_waypoint_constraints_gen(self, routing_agent, waypoint_count): yield from self._original_generator.get_waypoint_constraints_gen(routing_agent, waypoint_count) if False: yield None class WaypointInteraction(SuperInteraction): INSTANCE_TUNABLES = {'waypoint_constraint':TunableWaypointGeneratorVariant(tuning_group=GroupNames.ROUTING), 'waypoint_count':TunableRange(description='\n The number of waypoints to select, from spawn points in the zone, to\n visit for a Jog prior to returning to the original location.\n ', tunable_type=int, default=2, minimum=2, tuning_group=GroupNames.ROUTING), 'waypoint_walk_style':WalkStyleRequest.TunableFactory(description='\n The walkstyle to use when routing between waypoints.\n ', tuning_group=GroupNames.ROUTING), 'waypoint_stitching':WaypointStitchingVariant(tuning_group=GroupNames.ROUTING), 'waypoint_randomize_orientation':Tunable(description='\n Make Waypoint orientation random. Default is velocity aligned.\n ', tunable_type=bool, default=False, tuning_group=GroupNames.ROUTING), 'waypoint_clear_locomotion_mask':Tunable(description='\n If enabled, override the locomotion queue mask. This mask controls\n which Animation Requests and XEvents get blocked during locomotion.\n By default, the mask blocks everything. If cleared, it blocks\n nothing. It also lowers the animation track used by locomotion to \n 9,999 from the default of 10,000. Use with care, ask your GPE.\n ', tunable_type=bool, default=False, tuning_group=GroupNames.ROUTING), 'waypoint_override_agent_radius':OptionalTunable(description='\n If enabled, use the specified value as the agent radius when\n generating goals for the waypoints. The agent radius is restored\n for the actual route.\n ', tunable=TunableRange(description='\n The value to use as the agent radius when generating goals. \n ', tunable_type=float, minimum=0, maximum=1.0, default=0.123), tuning_group=GroupNames.ROUTING), 'waypoint_route_fail_balloon':OptionalTunable(description='\n Tuning for balloon to show when failing to plan a aroute for this waypoint interaction. \n ', tunable=TunableBalloon(locked_args={'balloon_delay':0, 'balloon_delay_random_offset':0, 'balloon_chance':100}), tuning_group=GroupNames.ROUTING)} def __init__(self, aop, *args, waypoint_generator=None, **kwargs): (super().__init__)(aop, *args, **kwargs) waypoint_info = kwargs.get('waypoint_info') if waypoint_info is not None: self._waypoint_generator = _WaypointGeneratorRallyable(waypoint_info) else: if aop.target is None: if self.target_type is TargetType.ACTOR: target = self.sim else: target = aop.target elif waypoint_generator is None: self._waypoint_generator = self.waypoint_constraint(self.context, target) else: self._waypoint_generator = waypoint_generator self._routing_infos = None self._goal_size = 0.0 self.register_on_finishing_callback(self._clean_up_waypoint_generator) @classmethod def _test(cls, target, context, **interaction_parameters): sim = context.sim routing_master = sim.routing_master if routing_master is not None: if sim.parent is not routing_master: return TestResult(False, '{} cannot run Waypoint interactions because they are following {}', sim, routing_master) return (super()._test)(target, context, **interaction_parameters) def _get_starting_constraint(self, *args, **kwargs): constraint = ANYWHERE target = self.target if self._waypoint_generator.is_for_vehicle and target is not None and target.vehicle_component is not None: constraint = target.is_in_inventory() or Circle((target.position), (target.vehicle_component.minimum_route_distance), routing_surface=(target.routing_surface)) constraint = constraint.intersect(self._waypoint_generator.get_water_constraint()) else: constraint = self._waypoint_generator.get_start_constraint() posture_constraint = self._waypoint_generator.get_posture_constraint() if posture_constraint is not None: constraint = constraint.intersect(posture_constraint) return constraint @flexmethod def _constraint_gen(cls, inst, *args, **kwargs): inst_or_cls = inst if inst is not None else cls if inst is not None: constraint = (inst._get_starting_constraint)(*args, **kwargs) yield constraint yield from (super(__class__, inst_or_cls)._constraint_gen)(*args, **kwargs) def cancel(self, *args, **kwargs): for sim_primitive in list(self.sim.primitives): if isinstance(sim_primitive, FollowPath): sim_primitive.detach() return (super().cancel)(*args, **kwargs) def _clean_up_waypoint_generator(self, _): self._waypoint_generator.clean_up() def _get_goals_for_constraint(self, constraint, routing_agent): goals = [] handles = constraint.get_connectivity_handles(routing_agent) for handle in handles: goals.extend(handle.get_goals(always_reject_invalid_goals=True)) return goals def _show_route_fail_balloon(self): balloon_tuning = self.waypoint_route_fail_balloon if balloon_tuning is None: return else: return self.is_user_directed or None balloon_requests = balloon_tuning(self) if balloon_requests: chosen_balloon = random.random.choice(balloon_requests) if chosen_balloon is not None: chosen_balloon.distribute() def _run_interaction_gen(self, timeline): all_sims = self.required_sims() if not all_sims: return self._routing_infos = [] routing_agent = self.sim for sim in all_sims: routing_context = sim.routing_context routing_agent = sim vehicle = None if not sim.posture.is_vehicle else sim.parent if vehicle is not None: if vehicle.vehicle_component is not None: routing_agent = vehicle routing_context = vehicle.routing_component.pathplan_context self._routing_infos.append((routing_agent, routing_context)) waypoints = [] default_agent_radius = None if self.waypoint_override_agent_radius is not None: if routing_agent.routing_component is not None: default_agent_radius = routing_agent.routing_component._pathplan_context.agent_radius routing_agent.routing_component._pathplan_context.agent_radius = self.waypoint_override_agent_radius else: try: for constraint in self._waypoint_generator.get_waypoint_constraints_gen(routing_agent, self.waypoint_count): goals = self._get_goals_for_constraint(constraint, routing_agent) if not goals: continue if self.waypoint_randomize_orientation: for goal in goals: goal.orientation = sims4.math.angle_to_yaw_quaternion(random.uniform(0.0, sims4.math.TWO_PI)) waypoints.append(goals) finally: if default_agent_radius is not None: routing_agent.routing_component._pathplan_context.agent_radius = default_agent_radius return waypoints or False self._goal_size = max((info[0].routing_component.get_routing_context().agent_goal_radius for info in self._routing_infos)) self._goal_size *= self._goal_size if self.staging: for route_waypoints in itertools.cycle(self.waypoint_stitching(waypoints, self._waypoint_generator.loops)): result = yield from self._do_route_to_constraint_gen(route_waypoints, timeline) if not result: return result else: for route_waypoints in self.waypoint_stitching(waypoints, self._waypoint_generator.loops): result = yield from self._do_route_to_constraint_gen(route_waypoints, timeline) return result return True if False: yield None def _do_route_to_constraint_gen(self, waypoints, timeline): if self.is_finishing: return False plan_primitives = [] for i, routing_info in enumerate(self._routing_infos): routing_agent = routing_info[0] routing_context = routing_info[1] route = routing.Route((routing_agent.routing_location), (waypoints[(-1)]), waypoints=(waypoints[:-1]), routing_context=routing_context) plan_primitive = PlanRoute(route, routing_agent, interaction=self) result = yield from element_utils.run_child(timeline, plan_primitive) if not result: self._show_route_fail_balloon() return False plan_primitive.path.nodes and plan_primitive.path.nodes.plan_success or self._show_route_fail_balloon() return False plan_primitive.path.blended_orientation = self.waypoint_randomize_orientation plan_primitives.append(plan_primitive) if i == len(self._routing_infos) - 1: continue for node in plan_primitive.path.nodes: position = Vector3(*node.position) for goal in itertools.chain.from_iterable(waypoints): if goal.routing_surface_id != node.routing_surface_id: continue dist_sq = (Vector3(*goal.position) - position).magnitude_2d_squared() if dist_sq < self._goal_size: goal.cost = routing.get_default_obstacle_cost() route_primitives = [] track_override = None mask_override = None if self.waypoint_clear_locomotion_mask: mask_override = 0 track_override = 9999 for plan_primitive in plan_primitives: sequence = get_route_element_for_path((plan_primitive.sim), (plan_primitive.path), interaction=self, force_follow_path=True, track_override=track_override, mask_override=mask_override) walkstyle_request = self.waypoint_walk_style(plan_primitive.sim) sequence = walkstyle_request(sequence=sequence) route_primitives.append(sequence) result = yield from element_utils.run_child(timeline, do_all(*route_primitives)) return result if False: yield None @classmethod def get_rallyable_aops_gen(cls, target, context, **kwargs): key = 'waypoint_info' if key not in kwargs: waypoint_generator = cls.waypoint_constraint(context, target) kwargs[key] = waypoint_generator yield from (super().get_rallyable_aops_gen)(target, context, rally_constraint=waypoint_generator.get_start_constraint(), **kwargs) if False: yield None

50.759542

471

0.666892

from _math import Vector3 import itertools, random from balloon.tunable_balloon import TunableBalloon from element_utils import do_all from event_testing.results import TestResult from interactions import TargetType from interactions.base.super_interaction import SuperInteraction from interactions.constraints import Circle, ANYWHERE from interactions.utils.routing import FollowPath, PlanRoute, get_route_element_for_path from routing.walkstyle.walkstyle_request import WalkStyleRequest from routing.waypoints.waypoint_generator_variant import TunableWaypointGeneratorVariant from routing.waypoints.waypoint_stitching import WaypointStitchingVariant from sims4 import random from sims4.tuning.tunable import TunableRange, Tunable, OptionalTunable from sims4.tuning.tunable_base import GroupNames from sims4.utils import flexmethod import element_utils, routing, sims4.log logger = sims4.log.Logger('WaypointInteraction') class _WaypointGeneratorRallyable: def __init__(self, waypoint_info): self._original_generator = waypoint_info def get_start_constraint(self): return self._original_generator.get_start_constraint() def get_waypoint_constraints_gen(self, routing_agent, waypoint_count): yield from self._original_generator.get_waypoint_constraints_gen(routing_agent, waypoint_count) if False: yield None class WaypointInteraction(SuperInteraction): INSTANCE_TUNABLES = {'waypoint_constraint':TunableWaypointGeneratorVariant(tuning_group=GroupNames.ROUTING), 'waypoint_count':TunableRange(description='\n The number of waypoints to select, from spawn points in the zone, to\n visit for a Jog prior to returning to the original location.\n ', tunable_type=int, default=2, minimum=2, tuning_group=GroupNames.ROUTING), 'waypoint_walk_style':WalkStyleRequest.TunableFactory(description='\n The walkstyle to use when routing between waypoints.\n ', tuning_group=GroupNames.ROUTING), 'waypoint_stitching':WaypointStitchingVariant(tuning_group=GroupNames.ROUTING), 'waypoint_randomize_orientation':Tunable(description='\n Make Waypoint orientation random. Default is velocity aligned.\n ', tunable_type=bool, default=False, tuning_group=GroupNames.ROUTING), 'waypoint_clear_locomotion_mask':Tunable(description='\n If enabled, override the locomotion queue mask. This mask controls\n which Animation Requests and XEvents get blocked during locomotion.\n By default, the mask blocks everything. If cleared, it blocks\n nothing. It also lowers the animation track used by locomotion to \n 9,999 from the default of 10,000. Use with care, ask your GPE.\n ', tunable_type=bool, default=False, tuning_group=GroupNames.ROUTING), 'waypoint_override_agent_radius':OptionalTunable(description='\n If enabled, use the specified value as the agent radius when\n generating goals for the waypoints. The agent radius is restored\n for the actual route.\n ', tunable=TunableRange(description='\n The value to use as the agent radius when generating goals. \n ', tunable_type=float, minimum=0, maximum=1.0, default=0.123), tuning_group=GroupNames.ROUTING), 'waypoint_route_fail_balloon':OptionalTunable(description='\n Tuning for balloon to show when failing to plan a aroute for this waypoint interaction. \n ', tunable=TunableBalloon(locked_args={'balloon_delay':0, 'balloon_delay_random_offset':0, 'balloon_chance':100}), tuning_group=GroupNames.ROUTING)} def __init__(self, aop, *args, waypoint_generator=None, **kwargs): (super().__init__)(aop, *args, **kwargs) waypoint_info = kwargs.get('waypoint_info') if waypoint_info is not None: self._waypoint_generator = _WaypointGeneratorRallyable(waypoint_info) else: if aop.target is None: if self.target_type is TargetType.ACTOR: target = self.sim else: target = aop.target elif waypoint_generator is None: self._waypoint_generator = self.waypoint_constraint(self.context, target) else: self._waypoint_generator = waypoint_generator self._routing_infos = None self._goal_size = 0.0 self.register_on_finishing_callback(self._clean_up_waypoint_generator) @classmethod def _test(cls, target, context, **interaction_parameters): sim = context.sim routing_master = sim.routing_master if routing_master is not None: if sim.parent is not routing_master: return TestResult(False, '{} cannot run Waypoint interactions because they are following {}', sim, routing_master) return (super()._test)(target, context, **interaction_parameters) def _get_starting_constraint(self, *args, **kwargs): constraint = ANYWHERE target = self.target if self._waypoint_generator.is_for_vehicle and target is not None and target.vehicle_component is not None: constraint = target.is_in_inventory() or Circle((target.position), (target.vehicle_component.minimum_route_distance), routing_surface=(target.routing_surface)) constraint = constraint.intersect(self._waypoint_generator.get_water_constraint()) else: constraint = self._waypoint_generator.get_start_constraint() posture_constraint = self._waypoint_generator.get_posture_constraint() if posture_constraint is not None: constraint = constraint.intersect(posture_constraint) return constraint @flexmethod def _constraint_gen(cls, inst, *args, **kwargs): inst_or_cls = inst if inst is not None else cls if inst is not None: constraint = (inst._get_starting_constraint)(*args, **kwargs) yield constraint yield from (super(__class__, inst_or_cls)._constraint_gen)(*args, **kwargs) def cancel(self, *args, **kwargs): for sim_primitive in list(self.sim.primitives): if isinstance(sim_primitive, FollowPath): sim_primitive.detach() return (super().cancel)(*args, **kwargs) def _clean_up_waypoint_generator(self, _): self._waypoint_generator.clean_up() def _get_goals_for_constraint(self, constraint, routing_agent): goals = [] handles = constraint.get_connectivity_handles(routing_agent) for handle in handles: goals.extend(handle.get_goals(always_reject_invalid_goals=True)) return goals def _show_route_fail_balloon(self): balloon_tuning = self.waypoint_route_fail_balloon if balloon_tuning is None: return else: return self.is_user_directed or None balloon_requests = balloon_tuning(self) if balloon_requests: chosen_balloon = random.random.choice(balloon_requests) if chosen_balloon is not None: chosen_balloon.distribute() def _run_interaction_gen(self, timeline): all_sims = self.required_sims() if not all_sims: return self._routing_infos = [] routing_agent = self.sim for sim in all_sims: routing_context = sim.routing_context routing_agent = sim vehicle = None if not sim.posture.is_vehicle else sim.parent if vehicle is not None: if vehicle.vehicle_component is not None: routing_agent = vehicle routing_context = vehicle.routing_component.pathplan_context self._routing_infos.append((routing_agent, routing_context)) waypoints = [] default_agent_radius = None if self.waypoint_override_agent_radius is not None: if routing_agent.routing_component is not None: default_agent_radius = routing_agent.routing_component._pathplan_context.agent_radius routing_agent.routing_component._pathplan_context.agent_radius = self.waypoint_override_agent_radius else: try: for constraint in self._waypoint_generator.get_waypoint_constraints_gen(routing_agent, self.waypoint_count): goals = self._get_goals_for_constraint(constraint, routing_agent) if not goals: continue if self.waypoint_randomize_orientation: for goal in goals: goal.orientation = sims4.math.angle_to_yaw_quaternion(random.uniform(0.0, sims4.math.TWO_PI)) waypoints.append(goals) finally: if default_agent_radius is not None: routing_agent.routing_component._pathplan_context.agent_radius = default_agent_radius return waypoints or False self._goal_size = max((info[0].routing_component.get_routing_context().agent_goal_radius for info in self._routing_infos)) self._goal_size *= self._goal_size if self.staging: for route_waypoints in itertools.cycle(self.waypoint_stitching(waypoints, self._waypoint_generator.loops)): result = yield from self._do_route_to_constraint_gen(route_waypoints, timeline) if not result: return result else: for route_waypoints in self.waypoint_stitching(waypoints, self._waypoint_generator.loops): result = yield from self._do_route_to_constraint_gen(route_waypoints, timeline) return result return True if False: yield None def _do_route_to_constraint_gen(self, waypoints, timeline): if self.is_finishing: return False plan_primitives = [] for i, routing_info in enumerate(self._routing_infos): routing_agent = routing_info[0] routing_context = routing_info[1] route = routing.Route((routing_agent.routing_location), (waypoints[(-1)]), waypoints=(waypoints[:-1]), routing_context=routing_context) plan_primitive = PlanRoute(route, routing_agent, interaction=self) result = yield from element_utils.run_child(timeline, plan_primitive) if not result: self._show_route_fail_balloon() return False plan_primitive.path.nodes and plan_primitive.path.nodes.plan_success or self._show_route_fail_balloon() return False plan_primitive.path.blended_orientation = self.waypoint_randomize_orientation plan_primitives.append(plan_primitive) if i == len(self._routing_infos) - 1: continue for node in plan_primitive.path.nodes: position = Vector3(*node.position) for goal in itertools.chain.from_iterable(waypoints): if goal.routing_surface_id != node.routing_surface_id: continue dist_sq = (Vector3(*goal.position) - position).magnitude_2d_squared() if dist_sq < self._goal_size: goal.cost = routing.get_default_obstacle_cost() route_primitives = [] track_override = None mask_override = None if self.waypoint_clear_locomotion_mask: mask_override = 0 track_override = 9999 for plan_primitive in plan_primitives: sequence = get_route_element_for_path((plan_primitive.sim), (plan_primitive.path), interaction=self, force_follow_path=True, track_override=track_override, mask_override=mask_override) walkstyle_request = self.waypoint_walk_style(plan_primitive.sim) sequence = walkstyle_request(sequence=sequence) route_primitives.append(sequence) result = yield from element_utils.run_child(timeline, do_all(*route_primitives)) return result if False: yield None @classmethod def get_rallyable_aops_gen(cls, target, context, **kwargs): key = 'waypoint_info' if key not in kwargs: waypoint_generator = cls.waypoint_constraint(context, target) kwargs[key] = waypoint_generator yield from (super().get_rallyable_aops_gen)(target, context, rally_constraint=waypoint_generator.get_start_constraint(), **kwargs) if False: yield None

true

f72003c0391c7dabf487c7375b1a310ce99ae57b

2,718

py

Python

test/test_mct.py

pistoia/qiskit-aqua

c7900ffdabc1499145739bfab29a392709bee1a0

[ "Apache-2.0" ]

null

test/test_mct.py

pistoia/qiskit-aqua

c7900ffdabc1499145739bfab29a392709bee1a0

[ "Apache-2.0" ]

null

test/test_mct.py

pistoia/qiskit-aqua

c7900ffdabc1499145739bfab29a392709bee1a0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2018 IBM. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import unittest import itertools import numpy as np from parameterized import parameterized from qiskit import QuantumCircuit, QuantumRegister from qiskit import execute as q_execute from qiskit.quantum_info import state_fidelity from qiskit.aqua import get_aer_backend from test.common import QiskitAquaTestCase num_controls = [i + 1 for i in range(7)] modes = ['basic', 'advanced', 'noancilla'] class TestMCT(QiskitAquaTestCase): @parameterized.expand( itertools.product(num_controls, modes) ) def test_mct(self, num_controls, mode): c = QuantumRegister(num_controls, name='c') o = QuantumRegister(1, name='o') subsets = [tuple(range(i)) for i in range(num_controls + 1)] for subset in subsets: qc = QuantumCircuit(o, c) if mode == 'basic': if num_controls <= 2: num_ancillae = 0 else: num_ancillae = num_controls - 2 elif mode == 'noancilla': num_ancillae = 0 else: if num_controls <= 4: num_ancillae = 0 else: num_ancillae = 1 if num_ancillae > 0: a = QuantumRegister(num_ancillae, name='a') qc.add_register(a) for idx in subset: qc.x(c[idx]) qc.mct( [c[i] for i in range(num_controls)], o[0], [a[i] for i in range(num_ancillae)], mode=mode ) for idx in subset: qc.x(c[idx]) vec = np.asarray(q_execute(qc, get_aer_backend( 'statevector_simulator')).result().get_statevector(qc, decimals=16)) vec_o = [0, 1] if len(subset) == num_controls else [1, 0] f = state_fidelity(vec, np.array(vec_o + [0] * (2 ** (num_controls + num_ancillae + 1) - 2))) self.assertAlmostEqual(f, 1) if __name__ == '__main__': unittest.main()

34.405063

105

0.577999

import unittest import itertools import numpy as np from parameterized import parameterized from qiskit import QuantumCircuit, QuantumRegister from qiskit import execute as q_execute from qiskit.quantum_info import state_fidelity from qiskit.aqua import get_aer_backend from test.common import QiskitAquaTestCase num_controls = [i + 1 for i in range(7)] modes = ['basic', 'advanced', 'noancilla'] class TestMCT(QiskitAquaTestCase): @parameterized.expand( itertools.product(num_controls, modes) ) def test_mct(self, num_controls, mode): c = QuantumRegister(num_controls, name='c') o = QuantumRegister(1, name='o') subsets = [tuple(range(i)) for i in range(num_controls + 1)] for subset in subsets: qc = QuantumCircuit(o, c) if mode == 'basic': if num_controls <= 2: num_ancillae = 0 else: num_ancillae = num_controls - 2 elif mode == 'noancilla': num_ancillae = 0 else: if num_controls <= 4: num_ancillae = 0 else: num_ancillae = 1 if num_ancillae > 0: a = QuantumRegister(num_ancillae, name='a') qc.add_register(a) for idx in subset: qc.x(c[idx]) qc.mct( [c[i] for i in range(num_controls)], o[0], [a[i] for i in range(num_ancillae)], mode=mode ) for idx in subset: qc.x(c[idx]) vec = np.asarray(q_execute(qc, get_aer_backend( 'statevector_simulator')).result().get_statevector(qc, decimals=16)) vec_o = [0, 1] if len(subset) == num_controls else [1, 0] f = state_fidelity(vec, np.array(vec_o + [0] * (2 ** (num_controls + num_ancillae + 1) - 2))) self.assertAlmostEqual(f, 1) if __name__ == '__main__': unittest.main()

true

f72005233f11455f1e95662ff8e8514dc68a23af

3,738

py

Python

letsencrypt/configuration.py

meehow/letsencrypt

64073b234a6b87a574d873599a8d4dbf11729d5c

[ "Apache-2.0" ]

1

2021-09-05T14:18:00.000Z

letsencrypt/configuration.py

meehow/letsencrypt

64073b234a6b87a574d873599a8d4dbf11729d5c

[ "Apache-2.0" ]

null

letsencrypt/configuration.py

meehow/letsencrypt

64073b234a6b87a574d873599a8d4dbf11729d5c

[ "Apache-2.0" ]

null

"""Let's Encrypt user-supplied configuration.""" import os import urlparse import zope.interface from acme import challenges from letsencrypt import constants from letsencrypt import errors from letsencrypt import interfaces class NamespaceConfig(object): """Configuration wrapper around :class:`argparse.Namespace`. For more documentation, including available attributes, please see :class:`letsencrypt.interfaces.IConfig`. However, note that the following attributes are dynamically resolved using :attr:`~letsencrypt.interfaces.IConfig.work_dir` and relative paths defined in :py:mod:`letsencrypt.constants`: - `accounts_dir` - `csr_dir` - `in_progress_dir` - `key_dir` - `renewer_config_file` - `temp_checkpoint_dir` :ivar namespace: Namespace typically produced by :meth:`argparse.ArgumentParser.parse_args`. :type namespace: :class:`argparse.Namespace` """ zope.interface.implements(interfaces.IConfig) def __init__(self, namespace): self.namespace = namespace if self.simple_http_port == self.dvsni_port: raise errors.Error( "Trying to run SimpleHTTP and DVSNI " "on the same port ({0})".format(self.dvsni_port)) def __getattr__(self, name): return getattr(self.namespace, name) @property def server_path(self): """File path based on ``server``.""" parsed = urlparse.urlparse(self.namespace.server) return (parsed.netloc + parsed.path).replace('/', os.path.sep) @property def accounts_dir(self): # pylint: disable=missing-docstring return os.path.join( self.namespace.config_dir, constants.ACCOUNTS_DIR, self.server_path) @property def backup_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.work_dir, constants.BACKUP_DIR) @property def csr_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.config_dir, constants.CSR_DIR) @property def in_progress_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.work_dir, constants.IN_PROGRESS_DIR) @property def key_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.config_dir, constants.KEY_DIR) @property def temp_checkpoint_dir(self): # pylint: disable=missing-docstring return os.path.join( self.namespace.work_dir, constants.TEMP_CHECKPOINT_DIR) @property def simple_http_port(self): # pylint: disable=missing-docstring if self.namespace.simple_http_port is not None: return self.namespace.simple_http_port else: return challenges.SimpleHTTPResponse.PORT class RenewerConfiguration(object): """Configuration wrapper for renewer.""" def __init__(self, namespace): self.namespace = namespace def __getattr__(self, name): return getattr(self.namespace, name) @property def archive_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.config_dir, constants.ARCHIVE_DIR) @property def live_dir(self): # pylint: disable=missing-docstring return os.path.join(self.namespace.config_dir, constants.LIVE_DIR) @property def renewal_configs_dir(self): # pylint: disable=missing-docstring return os.path.join( self.namespace.config_dir, constants.RENEWAL_CONFIGS_DIR) @property def renewer_config_file(self): # pylint: disable=missing-docstring return os.path.join( self.namespace.config_dir, constants.RENEWER_CONFIG_FILENAME)

32.789474

80

0.697164

import os import urlparse import zope.interface from acme import challenges from letsencrypt import constants from letsencrypt import errors from letsencrypt import interfaces class NamespaceConfig(object): zope.interface.implements(interfaces.IConfig) def __init__(self, namespace): self.namespace = namespace if self.simple_http_port == self.dvsni_port: raise errors.Error( "Trying to run SimpleHTTP and DVSNI " "on the same port ({0})".format(self.dvsni_port)) def __getattr__(self, name): return getattr(self.namespace, name) @property def server_path(self): parsed = urlparse.urlparse(self.namespace.server) return (parsed.netloc + parsed.path).replace('/', os.path.sep) @property def accounts_dir(self): return os.path.join( self.namespace.config_dir, constants.ACCOUNTS_DIR, self.server_path) @property def backup_dir(self): return os.path.join(self.namespace.work_dir, constants.BACKUP_DIR) @property def csr_dir(self): return os.path.join(self.namespace.config_dir, constants.CSR_DIR) @property def in_progress_dir(self): return os.path.join(self.namespace.work_dir, constants.IN_PROGRESS_DIR) @property def key_dir(self): return os.path.join(self.namespace.config_dir, constants.KEY_DIR) @property def temp_checkpoint_dir(self): return os.path.join( self.namespace.work_dir, constants.TEMP_CHECKPOINT_DIR) @property def simple_http_port(self): if self.namespace.simple_http_port is not None: return self.namespace.simple_http_port else: return challenges.SimpleHTTPResponse.PORT class RenewerConfiguration(object): def __init__(self, namespace): self.namespace = namespace def __getattr__(self, name): return getattr(self.namespace, name) @property def archive_dir(self): return os.path.join(self.namespace.config_dir, constants.ARCHIVE_DIR) @property def live_dir(self): return os.path.join(self.namespace.config_dir, constants.LIVE_DIR) @property def renewal_configs_dir(self): return os.path.join( self.namespace.config_dir, constants.RENEWAL_CONFIGS_DIR) @property def renewer_config_file(self): return os.path.join( self.namespace.config_dir, constants.RENEWER_CONFIG_FILENAME)

true

f72005518b34101337fb593f9f38ae1ba0642602

8,708

py

Python

src/modules/display_tickets.py

dat-adi/eisen-tickets

bedd6786da5c49d0021ca97e6e4f33b7a07f5be4

[ "MIT" ]

null

src/modules/display_tickets.py

dat-adi/eisen-tickets

bedd6786da5c49d0021ca97e6e4f33b7a07f5be4

[ "MIT" ]

11

2020-07-31T05:48:51.000Z

2022-01-16T08:03:28.000Z

src/modules/display_tickets.py

dat-adi/eisen-tickets

bedd6786da5c49d0021ca97e6e4f33b7a07f5be4

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # GUI import import tkinter as tk # Styling the GUI from tkinter import ttk # Database connection from modules.create_db_components import create_connection # Deletes the ticket from the database from modules.removing_tickets import delete_ticket """This module is used to display all the tickets present in the Database.""" # Owned __author__ = "Datta Adithya" __credits__ = ["Datta Adithya"] __license__ = "MIT" __maintainer__ = "Datta Adithya" __email__ = "dat.adithya@gmail.com" # fonts for the project text_font = ("Helvetica", 12) # functions to retrieve all of the records from the database def do_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DO"') conn.commit() rows = cur.fetchall() return rows def dec_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DEC"') conn.commit() rows = cur.fetchall() return rows def dlg_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DLG"') conn.commit() rows = cur.fetchall() return rows def del_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DEL"') conn.commit() rows = cur.fetchall() return rows # GUI for the project class windows(tk.Tk): def __init__(self, conn, *args, **kwargs): tk.Tk.__init__(self, *args, **kwargs) self.wm_title("Eisen's Tickets") self.iconbitmap(self, default="../../assets/logo.ico") self.conn = conn container = tk.Frame(self, height=400, width=600) container.pack(side="top", fill="both", expand=True) container.grid_rowconfigure(0, weight=1) container.grid_columnconfigure(0, weight=1) self.frames = {} for F in (MainPage, EisenDisplay, DoPage, DecPage, DlgPage, DelPage): frame = F(container, self, self.conn) self.frames[F] = frame frame.grid(row=0, column=0, sticky="nsew") self.show_frame(MainPage) def show_frame(self, cont): frame = self.frames[cont] frame.tkraise() def ticket_display(self, ticket): new_window = tk.Toplevel(self) ticket_id = ticket[0] timestamp = ticket[1] category = ticket[2] task = ticket[3] more_info = ticket[4] fields = ["Ticket ID", "Timestamp", "Category", "Task", "More Info"] details = [ticket_id, timestamp, category, task, more_info] r = 0 for field in fields: tk.Label(new_window, text=field, relief=tk.RIDGE, width=15).grid( row=r, column=0 ) tk.Label(new_window, text=details[r], relief=tk.SUNKEN, width=100).grid( row=r, column=1 ) r += 1 tk.Button( new_window, relief=tk.RIDGE, text="Delete Ticket", background="#FF3333", command=lambda: delete_ticket(self.conn, ticket_id), ).grid(row=r, column=0, columnspan=2, sticky="ew") # Pages made for navigation through the different categories class MainPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) self.conn = conn label = tk.Label(self, text="Start Page", font=text_font) label.pack(padx=10, pady=10) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) class EisenDisplay(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) self.conn = conn label = tk.Label(self, text="Eisen Display", font=text_font) label.pack(padx=10, pady=10) main_button = ttk.Button( self, text="Return to main page", command=lambda: controller.show_frame(MainPage), ) main_button.pack(side="bottom", fill=tk.X) del_button = ttk.Button( self, text="Eisen Delete", command=lambda: controller.show_frame(DelPage) ) del_button.pack(side="bottom", fill=tk.X) dlg_button = ttk.Button( self, text="Eisen Delegate", command=lambda: controller.show_frame(DlgPage) ) dlg_button.pack(side="bottom", fill=tk.X) dec_button = ttk.Button( self, text="Eisen Decide", command=lambda: controller.show_frame(DecPage) ) dec_button.pack(side="bottom", fill=tk.X) do_button = ttk.Button( self, text="Eisen Do", command=lambda: controller.show_frame(DoPage) ) do_button.pack(side="bottom", fill=tk.X) class DoPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Do Page", font=text_font) label.pack(padx=10, pady=10) do_rows = do_cat(conn) for element in do_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) dec_button = ttk.Button( self, text="Eisen Decide", command=lambda: controller.show_frame(DecPage) ) dec_button.pack(side="bottom", fill=tk.X) class DecPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Decide Page", font=text_font) label.pack(padx=10, pady=10) dec_rows = dec_cat(conn) for element in dec_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) dlg_button = ttk.Button( self, text="Eisen Delegate", command=lambda: controller.show_frame(DlgPage) ) dlg_button.pack(side="bottom", fill=tk.X) class DlgPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Delegate Page", font=text_font) label.pack(padx=10, pady=10) dlg_rows = dlg_cat(conn) for element in dlg_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) del_button = ttk.Button( self, text="Eisen Delete", command=lambda: controller.show_frame(DelPage) ) del_button.pack(side="bottom", fill=tk.X) class DelPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Delete Page", font=text_font) label.pack(padx=10, pady=10) del_rows = del_cat(conn) for element in del_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) do_button = ttk.Button( self, text="Eisen Do", command=lambda: controller.show_frame(DoPage) ) do_button.pack(side="bottom", fill=tk.X) if __name__ == "__main__": connection = create_connection(r"D:\eisen-tickets\assets\tickets.db") four_windows = windows(connection) four_windows.mainloop()

30.989324

87

0.598071

import tkinter as tk from tkinter import ttk from modules.create_db_components import create_connection from modules.removing_tickets import delete_ticket __author__ = "Datta Adithya" __credits__ = ["Datta Adithya"] __license__ = "MIT" __maintainer__ = "Datta Adithya" __email__ = "dat.adithya@gmail.com" text_font = ("Helvetica", 12) def do_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DO"') conn.commit() rows = cur.fetchall() return rows def dec_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DEC"') conn.commit() rows = cur.fetchall() return rows def dlg_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DLG"') conn.commit() rows = cur.fetchall() return rows def del_cat(conn): cur = conn.cursor() cur.execute('SELECT * FROM tickets WHERE category = "DEL"') conn.commit() rows = cur.fetchall() return rows class windows(tk.Tk): def __init__(self, conn, *args, **kwargs): tk.Tk.__init__(self, *args, **kwargs) self.wm_title("Eisen's Tickets") self.iconbitmap(self, default="../../assets/logo.ico") self.conn = conn container = tk.Frame(self, height=400, width=600) container.pack(side="top", fill="both", expand=True) container.grid_rowconfigure(0, weight=1) container.grid_columnconfigure(0, weight=1) self.frames = {} for F in (MainPage, EisenDisplay, DoPage, DecPage, DlgPage, DelPage): frame = F(container, self, self.conn) self.frames[F] = frame frame.grid(row=0, column=0, sticky="nsew") self.show_frame(MainPage) def show_frame(self, cont): frame = self.frames[cont] frame.tkraise() def ticket_display(self, ticket): new_window = tk.Toplevel(self) ticket_id = ticket[0] timestamp = ticket[1] category = ticket[2] task = ticket[3] more_info = ticket[4] fields = ["Ticket ID", "Timestamp", "Category", "Task", "More Info"] details = [ticket_id, timestamp, category, task, more_info] r = 0 for field in fields: tk.Label(new_window, text=field, relief=tk.RIDGE, width=15).grid( row=r, column=0 ) tk.Label(new_window, text=details[r], relief=tk.SUNKEN, width=100).grid( row=r, column=1 ) r += 1 tk.Button( new_window, relief=tk.RIDGE, text="Delete Ticket", background="#FF3333", command=lambda: delete_ticket(self.conn, ticket_id), ).grid(row=r, column=0, columnspan=2, sticky="ew") # Pages made for navigation through the different categories class MainPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) self.conn = conn label = tk.Label(self, text="Start Page", font=text_font) label.pack(padx=10, pady=10) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) class EisenDisplay(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) self.conn = conn label = tk.Label(self, text="Eisen Display", font=text_font) label.pack(padx=10, pady=10) main_button = ttk.Button( self, text="Return to main page", command=lambda: controller.show_frame(MainPage), ) main_button.pack(side="bottom", fill=tk.X) del_button = ttk.Button( self, text="Eisen Delete", command=lambda: controller.show_frame(DelPage) ) del_button.pack(side="bottom", fill=tk.X) dlg_button = ttk.Button( self, text="Eisen Delegate", command=lambda: controller.show_frame(DlgPage) ) dlg_button.pack(side="bottom", fill=tk.X) dec_button = ttk.Button( self, text="Eisen Decide", command=lambda: controller.show_frame(DecPage) ) dec_button.pack(side="bottom", fill=tk.X) do_button = ttk.Button( self, text="Eisen Do", command=lambda: controller.show_frame(DoPage) ) do_button.pack(side="bottom", fill=tk.X) class DoPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Do Page", font=text_font) label.pack(padx=10, pady=10) do_rows = do_cat(conn) for element in do_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) dec_button = ttk.Button( self, text="Eisen Decide", command=lambda: controller.show_frame(DecPage) ) dec_button.pack(side="bottom", fill=tk.X) class DecPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Decide Page", font=text_font) label.pack(padx=10, pady=10) dec_rows = dec_cat(conn) for element in dec_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) dlg_button = ttk.Button( self, text="Eisen Delegate", command=lambda: controller.show_frame(DlgPage) ) dlg_button.pack(side="bottom", fill=tk.X) class DlgPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Delegate Page", font=text_font) label.pack(padx=10, pady=10) dlg_rows = dlg_cat(conn) for element in dlg_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) del_button = ttk.Button( self, text="Eisen Delete", command=lambda: controller.show_frame(DelPage) ) del_button.pack(side="bottom", fill=tk.X) class DelPage(tk.Frame): def __init__(self, parent, controller, conn): tk.Frame.__init__(self, parent) label = tk.Label(self, text="Eisen's Delete Page", font=text_font) label.pack(padx=10, pady=10) del_rows = del_cat(conn) for element in del_rows: tk.Button( self, text=element[3], fg="black", command=lambda ele=element: controller.ticket_display(ele), ).pack(fill=tk.X) eisen_display_button = ttk.Button( self, text="Display Selection", command=lambda: controller.show_frame(EisenDisplay), ) eisen_display_button.pack(side="bottom", fill=tk.X) do_button = ttk.Button( self, text="Eisen Do", command=lambda: controller.show_frame(DoPage) ) do_button.pack(side="bottom", fill=tk.X) if __name__ == "__main__": connection = create_connection(r"D:\eisen-tickets\assets\tickets.db") four_windows = windows(connection) four_windows.mainloop()

true

f720055934c6413aad908cb155527edb52b40062

1,116

py

Python

root/scripts/includes/python_logger.py

DragonCrafted87/docker-alpine-base

033199c1d7d6d57271f16841b132469c78658dcf

[ "MIT" ]

null

root/scripts/includes/python_logger.py

DragonCrafted87/docker-alpine-base

033199c1d7d6d57271f16841b132469c78658dcf

[ "MIT" ]

null

root/scripts/includes/python_logger.py

DragonCrafted87/docker-alpine-base

033199c1d7d6d57271f16841b132469c78658dcf

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding: utf-8 -*- from logging import DEBUG from logging import INFO from logging import Formatter from logging import StreamHandler from logging import getLogger from sys import stderr from sys import stdout class LogLevelFilter: def __init__(self, level): self.__level = level def filter(self, log_record): return log_record.levelno == self.__level def create_logger(name=None): # create logger log = getLogger(name) log.setLevel(DEBUG) # create formatter and add it to the handlers log_format = Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") # create console handler with a higher log level info_handler = StreamHandler(stdout) info_handler.setLevel(INFO) info_handler.setFormatter(log_format) log.addHandler(info_handler) # create console handler with a higher log level debug_handler = StreamHandler(stderr) debug_handler.setLevel(DEBUG) debug_handler.setFormatter(log_format) debug_handler.addFilter(LogLevelFilter(DEBUG)) log.addHandler(debug_handler) return log

25.953488

82

0.728495

from logging import DEBUG from logging import INFO from logging import Formatter from logging import StreamHandler from logging import getLogger from sys import stderr from sys import stdout class LogLevelFilter: def __init__(self, level): self.__level = level def filter(self, log_record): return log_record.levelno == self.__level def create_logger(name=None): log = getLogger(name) log.setLevel(DEBUG) log_format = Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") info_handler = StreamHandler(stdout) info_handler.setLevel(INFO) info_handler.setFormatter(log_format) log.addHandler(info_handler) debug_handler = StreamHandler(stderr) debug_handler.setLevel(DEBUG) debug_handler.setFormatter(log_format) debug_handler.addFilter(LogLevelFilter(DEBUG)) log.addHandler(debug_handler) return log

true

f720066904260a4d87d72e6f5790ddf77d44d217

101

py

Python

codes_auto/1603.running-sum-of-1d-array.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

[ "MIT" ]

null

codes_auto/1603.running-sum-of-1d-array.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

[ "MIT" ]

null

codes_auto/1603.running-sum-of-1d-array.py

smartmark-pro/leetcode_record

6504b733d892a705571eb4eac836fb10e94e56db

[ "MIT" ]

null

# # @lc app=leetcode.cn id=1603 lang=python3 # # [1603] running-sum-of-1d-array # None # @lc code=end

14.428571

42

0.673267

None

true

f72006ab8586955d57cb0232db8f0b952693aca0

1,522

py

Python

setup.py

Rishk/alpha_vantage

1cb28a98cb0c8526b85d163be96a37fd2d16ff95

[ "MIT" ]

1

2019-12-27T17:50:59.000Z

setup.py

Rishk/alpha_vantage

1cb28a98cb0c8526b85d163be96a37fd2d16ff95

[ "MIT" ]

null

setup.py

Rishk/alpha_vantage

1cb28a98cb0c8526b85d163be96a37fd2d16ff95

[ "MIT" ]

null

from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) try: with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() except IOError: long_description = 'Python module to get stock data from the Alpha Vantage Api' setup( name='alpha_vantage', version='2.0.0', author='Romel J. Torres', author_email='romel.torres@gmail.com', license='MIT', description='Python module to get stock data from the Alpha Vantage Api', long_description=long_description, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Office/Business :: Financial :: Investment', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], url='https://github.com/RomelTorres/alpha_vantage', install_requires=[ 'requests', 'simplejson' ], test_requires=[ 'nose', 'requests_mock' ], extras_requires={ 'pandas': ['pandas'], }, keywords=['stocks', 'market', 'finance', 'alpha_vantage', 'quotes', 'shares'], packages=find_packages( exclude=['helpers', 'test_alpha_vantage', 'images']), package_data={ 'alpha_vantage': [], } )

30.44

83

0.61498

from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) try: with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() except IOError: long_description = 'Python module to get stock data from the Alpha Vantage Api' setup( name='alpha_vantage', version='2.0.0', author='Romel J. Torres', author_email='romel.torres@gmail.com', license='MIT', description='Python module to get stock data from the Alpha Vantage Api', long_description=long_description, classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'Topic :: Office/Business :: Financial :: Investment', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], url='https://github.com/RomelTorres/alpha_vantage', install_requires=[ 'requests', 'simplejson' ], test_requires=[ 'nose', 'requests_mock' ], extras_requires={ 'pandas': ['pandas'], }, keywords=['stocks', 'market', 'finance', 'alpha_vantage', 'quotes', 'shares'], packages=find_packages( exclude=['helpers', 'test_alpha_vantage', 'images']), package_data={ 'alpha_vantage': [], } )

true

f720075710121fe0930a9588a836db8847406e4b

18,959

py

Python

tools/rnahmm/algbioi/core/analysis16s.py

fplaza/CAMISIM

4f2ab5e94773a355210568be946e732df7437cb6

[ "Apache-2.0" ]

88

2017-11-20T01:30:40.000Z

2022-03-31T07:29:25.000Z

tools/rnahmm/algbioi/core/analysis16s.py

fplaza/CAMISIM

4f2ab5e94773a355210568be946e732df7437cb6

[ "Apache-2.0" ]

121

2017-11-15T09:03:38.000Z

2022-03-24T09:50:59.000Z

tools/rnahmm/algbioi/core/analysis16s.py

fplaza/CAMISIM

4f2ab5e94773a355210568be946e732df7437cb6

[ "Apache-2.0" ]

35

2017-12-14T08:19:40.000Z

2022-03-03T11:26:46.000Z

#!/usr/bin/env python """ Copyright (C) 2014 Ivan Gregor This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Note that we could have written some parts of this code in a nicer way, but didn't have time. Be careful when reusing the source code. """ import os import sys import re import subprocess from Bio import SeqIO from algbioi.com import common, csv from scripts.configparserwrapper import ConfigParserWrapper class RRNA16S(): """ A class that handels the rRNA 16S analysis. """ def __init__(self, config, s16Database, workingDir): assert isinstance(config, ConfigParserWrapper) self._config = config self._workingDir = workingDir # self._refDir = os.path.normpath(s16Database) # self._refDict = csv.getMappingTuple(os.path.join(self._refDir, 'content.csv'), (0,1), (2,), '\t') def runHMM(self, inputFastaFile, outLog=None, hmmer=3, moltypes="ssu", kingdoms="arc,bac"): """ Run the hidden markov model to get regions in the input sequences where the 16S and 23S genes are located. """ if hmmer == 2: self.runHMM_2(inputFastaFile, outLog, moltypes=moltypes, kingdoms=kingdoms) elif hmmer == 3: self.runHMM_3(inputFastaFile, outLog, moltypes=moltypes, kingdoms=kingdoms) def runHMM_2(self, inputFastaFile, outLog=None, moltypes="ssu", kingdoms="arc,bac"): hmmInstallDir = self._config.get_value("MarkerGeneExtraction", 'rnaHmmInstallDir', is_path=True) rnammer_executable = self._config.get_value("MarkerGeneExtraction", 'rnammer', is_path=True) assert isinstance(hmmInstallDir, basestring) assert isinstance(rnammer_executable, basestring) out_file_name_prefix = os.path.join(self._workingDir, os.path.basename(inputFastaFile)) hmmer_args = [ "-i '{}'".format(inputFastaFile), "-o '{}'".format(out_file_name_prefix), "-r '{}'".format(rnammer_executable), "-m '{}'".format(moltypes), "-k '{}'".format(kingdoms), "-T '{}'".format(self._workingDir) ] cmd = "{script} {args}".format( script=os.path.join(hmmInstallDir, 'rna_hmm2.py'), args=" ".join(hmmer_args), ) if os.name != 'posix': print 'Cannot run HMM since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd return if outLog is not None: stdoutLog = open(outLog, 'a') else: stdoutLog = subprocess.PIPE # stdout=subprocess.STDOUT hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=hmmInstallDir, stdout=stdoutLog) print 'run cmd:', cmd hmmProc.wait() if outLog is not None: stdoutLog.close() print 'HMM return code:', hmmProc.returncode if hmmProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd)) def runHMM_3(self, inputFastaFile, outLog=None, moltypes="ssu", kingdoms="arc,bac"): hmmInstallDir = self._config.get_value("MarkerGeneExtraction", 'rnaHmmInstallDir', is_path=True) hmmerBinDir = self._config.get_value("MarkerGeneExtraction", 'hmmerBinDir', is_path=True) assert isinstance(hmmInstallDir, basestring) assert isinstance(hmmerBinDir, basestring) regionsFile = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.gff')) hmmer_args = [ "-i '{}'".format(inputFastaFile), "-o '{}'".format(regionsFile), "-r '{}'".format(os.path.join(hmmerBinDir, "hmmsearch")), "-m '{}'".format(moltypes), "-k '{}'".format(kingdoms) ] cmd = "{wrapper} {args}".format( hmmerBinDir=hmmerBinDir, wrapper=os.path.join(hmmInstallDir, 'rna_hmm3.py'), args=" ".join(hmmer_args) ) if os.name != 'posix': print 'Cannot run HMM since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd return if outLog is not None: stdoutLog = open(outLog, 'a') else: stdoutLog = subprocess.PIPE # stdout=subprocess.STDOUT hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=hmmInstallDir, stdout=stdoutLog) print 'run cmd:', cmd hmmProc.wait() if outLog is not None: stdoutLog.close() print 'HMM return code:', hmmProc.returncode if hmmProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd)) handle = open(inputFastaFile, "rU") record_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta")) handle.close() # trunkoutputfilename = inputFastaFile.split( "/" )[-1] trunkoutputfilename = os.path.join(self._workingDir, os.path.basename(inputFastaFile)) # parse results file line by line for line in open(regionsFile, "rU"): if line.startswith("#"): continue line = line.split() ident = line[0] start = int(line[3]) stop = int(line[4]) strand = line[6] gene = line[8] seq = record_dict[ident].seq if strand == "+": subseq = seq[start-1:stop] elif strand == "-": subseq = seq[start-1:stop].reverse_complement() else: sys.stderr.write(" analysis16s: invalid strand symbol") exit(1) outfile = open(trunkoutputfilename + "." + gene + ".fna", "a") print >> outfile, ">%s_%i_%i_%s" % (ident, start, stop, strand) print >> outfile, subseq outfile.close() def classify16S(self, inputFastaFile, outLog=None): """ Run mothur to classify the sequences. """ try: self._classify(16, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 16S sequences.') def classify23S(self, inputFastaFile, outLog=None): try: self._classify(23, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 23S sequences.') def classify5S(self, inputFastaFile, outLog=None): try: self._classify(5, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 5S sequences.') def _classify(self, mode, inputFastaFile, outLog=None): mothur = os.path.join(os.path.normpath(self._config.get('mothurInstallDir')), 'mothur') if mode == 16: extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('16S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('16S_rRNA','templateDNA')][0])) #mothurPredFileName = str(extractedRegionsFasta[0:extractedRegionsFasta.rindex('.')] + '.taxonomy') mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16P')) #extractedRegionsFasta = str(inputFastaFile + '.16S_rRNA.fna') #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam16STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam16STaxonomy')) #mothurPredFileName = str(inputFastaFile + '.16S_rRNA.bacteria+archaea.taxonomy') #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.bacteria+archaea.taxonomy')) #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.fasta.taxonomy')) #predFileName = str(inputFastaFile + '.16P') elif mode == 23: extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('23S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('23S_rRNA','templateDNA')][0])) #mothurPredFileName = str(extractedRegionsFasta[0:extractedRegionsFasta.rindex('.')] + '.taxonomy') mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23P')) #extractedRegionsFasta = str(inputFastaFile + '.23S_rRNA.fna') #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam23STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam23STaxonomy')) #mothurPredFileName = str(inputFastaFile + '.23S_rRNA.bacteria+archaea.taxonomy') #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.bacteria+archaea.taxonomy')) #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.fasta.taxonomy')) #predFileName = str(inputFastaFile + '.23P') elif mode == 5: #extractedRegionsFasta = str(inputFastaFile + '.5S_rRNA.fna') extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('5S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('5S_rRNA','templateDNA')][0])) mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5P')) #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam5STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam5STaxonomy')) #mothurPredFileName = os.path.join(self._workingDir, # str(os.path.basename(inputFastaFile) + '.5S_rRNA.' + os.path.basename(taxonomyFile) + 'onomy'))#.taxonomy #predFileName = str(inputFastaFile + '.5P') else: raise Exception('Wrong branch') if not os.path.isfile(mothurPredFileName): mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile, suffix='.bayesian.taxonomy') param = self._config.get('mothurClassifyParamOther') cmd = str('time ' + mothur + ' "#classify.seqs(fasta=' + extractedRegionsFasta + ', template=' + templateFile + ', taxonomy=' + taxonomyFile + ', ' + param + ')"') if os.name == 'posix': if outLog is not None: stdoutLog = open(outLog, 'w') else: stdoutLog = subprocess.STDOUT mothurProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self._workingDir, stdout=stdoutLog) print 'run cmd:', cmd mothurProc.wait() if outLog is not None: stdoutLog.close() print 'mothur return code:', mothurProc.returncode if mothurProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (mothurProc.returncode, cmd)) else: print 'Cannot run mothur since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd #transform mothur prediction files to the tab separated files self.mothurPredToTabSepPred(mothurPredFileName, predFileName) def mothurPredToTabSepPred(self, mothurPredFileName, outPredFileName): """ Transforms the mothur output prediction file (*.taxonomy) to the tab separated prediction file seqName tab ncbid tab weight. """ try: fr = open(os.path.normpath(mothurPredFileName),'r') except Exception: sys.stderr.write("Cannot open file:" + mothurPredFileName + '\n') raise else: try: fw = open(os.path.normpath(outPredFileName), 'w') lineCount = 0 for line in fr: line = common.noNewLine(line) try: if re.match(r'^[0-9]+_[0-9]+_[0-9]+_[0-9]+.*', line): name = re.sub(r'([0-9]+_[0-9]+)_[0-9]+_[0-9]+_[\+\-\t ]+.*', r'\1' , line) tag = re.sub(r'[0-9]+_[0-9]+_([0-9]+_[0-9]+_[\+\-]+)[\t ]+.*', r'\1' , line) placementList = re.sub(r'[0-9]+_[0-9]+_[0-9]+_[0-9]+_[\+\-\t ]+(.*)', r'\1' , line.replace('unclassified;', '')).rsplit(';') if len(placementList) < 2: continue placement = placementList[-2] try: clade = int(re.sub('([0-9]+)$.*', r'\1' , placement)) except ValueError: continue weight = float(re.sub('[0-9]+\(([0-9\.]+)$', r'\1' , placement)) lineCount += 1 if lineCount == 1: fw.write(name + '\t' + str(clade) + '\t' + str(weight) + '\t' + str(tag)) else: fw.write('\n' + name + '\t' + str(clade) + '\t' + str(weight) + '\t' + str(tag)) except Exception: sys.stderr.write('Cannot parse line: ' + str(lineCount) + 'in file: ' + mothurPredFileName + '\n') raise except Exception: sys.stderr.write("Cannot write to file:" + outPredFileName + '\n') raise finally: fw.close() fr.close() # def setCandidatePlacementFrom16S23S5S(self, sequences, taxonomy, inputFastaFile): #set assigned by the 16S countList[0] #set assigned by the 23S countList[1] #intersection = both countList[3] #predFileName16S = str(inputFastaFile + '.16P') predFileName16S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16P')) #predFileName23S = str(inputFastaFile + '.23P') predFileName23S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23P')) #predFileName5S = str(inputFastaFile + '.5P') predFileName5S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5P')) try: seqIdSet16S = self._setCandidatePlacement(sequences, taxonomy, predFileName16S, '16S_rRNA') except Exception: seqIdSet16S = set() print("Can't set candidate placement from 16S classification.") try: seqIdSet23S = self._setCandidatePlacement(sequences, taxonomy, predFileName23S, '23S_rRNA') except Exception: seqIdSet23S = set() print("Can't set candidate placement from 23S sequences.") try: seqIdSet5S = self._setCandidatePlacement(sequences, taxonomy, predFileName5S, '5S_rRNA') except Exception: seqIdSet5S = set() print("Can't set candidate placement from 5S sequences.") intersectSet = seqIdSet16S | seqIdSet23S | seqIdSet5S return [len(seqIdSet16S),len(seqIdSet23S),len(seqIdSet5S),len(intersectSet)] # def _setCandidatePlacement(self, sequences, taxonomy, predFileName, source): assignedIdList = [] try: f = open(os.path.normpath(predFileName),'r') except Exception: print "Cannot open file:", predFileName raise else: for line in f: line = common.noNewLine(line) if re.match(r'^[0-9]+_[0-9]+\t[0-9]+\t[0-9\.]+\t[^\t]+$', line): scaffoldId = int(re.sub(r'^([0-9]+)_[0-9]+\t[0-9]+\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) contigId = int(re.sub(r'^[0-9]+_([0-9]+)\t[0-9]+\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) ncbid = int(re.sub(r'^[0-9]+_[0-9]+\t([0-9]+)\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) weight = float(re.sub(r'^[0-9]+_[0-9]+\t[0-9]+\t([0-9\.]+)\t[^\t]+$',r'\1' ,line)) tag = str(re.sub(r'^[0-9]+_[0-9]+\t[0-9]+\t[0-9\.]+\t([^\t]+)$',r'\1' ,line)) if ncbid != 1: taxPathDict = taxonomy.getPathToRoot(ncbid) if taxPathDict is not None and taxPathDict.keys() >= 1: sequences.setCandidateTaxonomyPath(contigId, scaffoldId, taxPathDict, weight, source, tag) assignedIdList.append(contigId) else: sys.stderr.write(str('No taxonomic path found for ncbid: ' + str(ncbid))) finally: f.close() return set(assignedIdList) if __name__ == "__main__": #print '16S analysis' #line = '125_528_- 2(100);unclassified;unclassified;unclassified;unclassified;unclassified;unclassified;unclassified;' line = '125_528_- 2157(100);28890(95.3333);183925(95.3333);2158(95.3333);2159(95.3333);2172(95.3333);unclassified;unclassified;' try: name = re.sub('([0-9]+_[0-9]+)_[\+\-\t ]+.*', r'\1' , line) placement = re.sub('[0-9]+_[0-9]+_[\+\-\t ]+(.*)', r'\1' , line.replace('unclassified;', '')).rsplit(';')[-2] clade = int(re.sub('([0-9]+)$.*', r'\1' , placement)) weight = float(re.sub('[0-9]+\(([0-9\.]+)$', r'\1' , placement)) except Exception: print sys.stderr.write('Cannot parse line in file x' + '\n') print name print placement print clade print weight

50.156085

152

0.588586

""" Copyright (C) 2014 Ivan Gregor This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Note that we could have written some parts of this code in a nicer way, but didn't have time. Be careful when reusing the source code. """ import os import sys import re import subprocess from Bio import SeqIO from algbioi.com import common, csv from scripts.configparserwrapper import ConfigParserWrapper class RRNA16S(): """ A class that handels the rRNA 16S analysis. """ def __init__(self, config, s16Database, workingDir): assert isinstance(config, ConfigParserWrapper) self._config = config self._workingDir = workingDir # self._refDir = os.path.normpath(s16Database) # self._refDict = csv.getMappingTuple(os.path.join(self._refDir, 'content.csv'), (0,1), (2,), '\t') def runHMM(self, inputFastaFile, outLog=None, hmmer=3, moltypes="ssu", kingdoms="arc,bac"): """ Run the hidden markov model to get regions in the input sequences where the 16S and 23S genes are located. """ if hmmer == 2: self.runHMM_2(inputFastaFile, outLog, moltypes=moltypes, kingdoms=kingdoms) elif hmmer == 3: self.runHMM_3(inputFastaFile, outLog, moltypes=moltypes, kingdoms=kingdoms) def runHMM_2(self, inputFastaFile, outLog=None, moltypes="ssu", kingdoms="arc,bac"): hmmInstallDir = self._config.get_value("MarkerGeneExtraction", 'rnaHmmInstallDir', is_path=True) rnammer_executable = self._config.get_value("MarkerGeneExtraction", 'rnammer', is_path=True) assert isinstance(hmmInstallDir, basestring) assert isinstance(rnammer_executable, basestring) out_file_name_prefix = os.path.join(self._workingDir, os.path.basename(inputFastaFile)) hmmer_args = [ "-i '{}'".format(inputFastaFile), "-o '{}'".format(out_file_name_prefix), "-r '{}'".format(rnammer_executable), "-m '{}'".format(moltypes), "-k '{}'".format(kingdoms), "-T '{}'".format(self._workingDir) ] cmd = "{script} {args}".format( script=os.path.join(hmmInstallDir, 'rna_hmm2.py'), args=" ".join(hmmer_args), ) if os.name != 'posix': print 'Cannot run HMM since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd return if outLog is not None: stdoutLog = open(outLog, 'a') else: stdoutLog = subprocess.PIPE # stdout=subprocess.STDOUT hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=hmmInstallDir, stdout=stdoutLog) print 'run cmd:', cmd hmmProc.wait() if outLog is not None: stdoutLog.close() print 'HMM return code:', hmmProc.returncode if hmmProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd)) def runHMM_3(self, inputFastaFile, outLog=None, moltypes="ssu", kingdoms="arc,bac"): hmmInstallDir = self._config.get_value("MarkerGeneExtraction", 'rnaHmmInstallDir', is_path=True) hmmerBinDir = self._config.get_value("MarkerGeneExtraction", 'hmmerBinDir', is_path=True) assert isinstance(hmmInstallDir, basestring) assert isinstance(hmmerBinDir, basestring) regionsFile = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.gff')) hmmer_args = [ "-i '{}'".format(inputFastaFile), "-o '{}'".format(regionsFile), "-r '{}'".format(os.path.join(hmmerBinDir, "hmmsearch")), "-m '{}'".format(moltypes), "-k '{}'".format(kingdoms) ] cmd = "{wrapper} {args}".format( hmmerBinDir=hmmerBinDir, wrapper=os.path.join(hmmInstallDir, 'rna_hmm3.py'), args=" ".join(hmmer_args) ) if os.name != 'posix': print 'Cannot run HMM since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd return if outLog is not None: stdoutLog = open(outLog, 'a') else: stdoutLog = subprocess.PIPE # stdout=subprocess.STDOUT hmmProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=hmmInstallDir, stdout=stdoutLog) print 'run cmd:', cmd hmmProc.wait() if outLog is not None: stdoutLog.close() print 'HMM return code:', hmmProc.returncode if hmmProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (hmmProc.returncode, cmd)) handle = open(inputFastaFile, "rU") record_dict = SeqIO.to_dict(SeqIO.parse(handle, "fasta")) handle.close() # trunkoutputfilename = inputFastaFile.split( "/" )[-1] trunkoutputfilename = os.path.join(self._workingDir, os.path.basename(inputFastaFile)) # parse results file line by line for line in open(regionsFile, "rU"): if line.startswith("#"): continue line = line.split() ident = line[0] start = int(line[3]) stop = int(line[4]) strand = line[6] gene = line[8] seq = record_dict[ident].seq if strand == "+": subseq = seq[start-1:stop] elif strand == "-": subseq = seq[start-1:stop].reverse_complement() else: sys.stderr.write(" analysis16s: invalid strand symbol") exit(1) outfile = open(trunkoutputfilename + "." + gene + ".fna", "a") print >> outfile, ">%s_%i_%i_%s" % (ident, start, stop, strand) print >> outfile, subseq outfile.close() def classify16S(self, inputFastaFile, outLog=None): """ Run mothur to classify the sequences. """ try: self._classify(16, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 16S sequences.') def classify23S(self, inputFastaFile, outLog=None): try: self._classify(23, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 23S sequences.') def classify5S(self, inputFastaFile, outLog=None): try: self._classify(5, inputFastaFile, outLog) except Exception: print('Mothur was not able to classify 5S sequences.') def _classify(self, mode, inputFastaFile, outLog=None): mothur = os.path.join(os.path.normpath(self._config.get('mothurInstallDir')), 'mothur') if mode == 16: extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('16S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('16S_rRNA','templateDNA')][0])) #mothurPredFileName = str(extractedRegionsFasta[0:extractedRegionsFasta.rindex('.')] + '.taxonomy') mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16P')) #extractedRegionsFasta = str(inputFastaFile + '.16S_rRNA.fna') #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam16STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam16STaxonomy')) #mothurPredFileName = str(inputFastaFile + '.16S_rRNA.bacteria+archaea.taxonomy') #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.bacteria+archaea.taxonomy')) #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16S_rRNA.fasta.taxonomy')) #predFileName = str(inputFastaFile + '.16P') elif mode == 23: extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('23S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('23S_rRNA','templateDNA')][0])) #mothurPredFileName = str(extractedRegionsFasta[0:extractedRegionsFasta.rindex('.')] + '.taxonomy') mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23P')) #extractedRegionsFasta = str(inputFastaFile + '.23S_rRNA.fna') #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam23STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam23STaxonomy')) #mothurPredFileName = str(inputFastaFile + '.23S_rRNA.bacteria+archaea.taxonomy') #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.bacteria+archaea.taxonomy')) #mothurPredFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23S_rRNA.fasta.taxonomy')) #predFileName = str(inputFastaFile + '.23P') elif mode == 5: #extractedRegionsFasta = str(inputFastaFile + '.5S_rRNA.fna') extractedRegionsFasta = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5S_rRNA.fna')) taxonomyFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('5S_rRNA','taxonomyDNA')][0])) templateFile = os.path.join(self._refDir, os.path.normpath(self._refDict[('5S_rRNA','templateDNA')][0])) mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile) predFileName = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5P')) #templateFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam5STemplate')) #taxonomyFile = os.path.normpath(self._configRRNA16S.get('mothurClassifyParam5STaxonomy')) #mothurPredFileName = os.path.join(self._workingDir, # str(os.path.basename(inputFastaFile) + '.5S_rRNA.' + os.path.basename(taxonomyFile) + 'onomy'))#.taxonomy #predFileName = str(inputFastaFile + '.5P') else: raise Exception('Wrong branch') if not os.path.isfile(mothurPredFileName): mothurPredFileName = common.getMothurOutputFilePath(extractedRegionsFasta, taxonomyFile, suffix='.bayesian.taxonomy') param = self._config.get('mothurClassifyParamOther') cmd = str('time ' + mothur + ' "#classify.seqs(fasta=' + extractedRegionsFasta + ', template=' + templateFile + ', taxonomy=' + taxonomyFile + ', ' + param + ')"') if os.name == 'posix': if outLog is not None: stdoutLog = open(outLog, 'w') else: stdoutLog = subprocess.STDOUT mothurProc = subprocess.Popen(cmd, shell=True, bufsize=-1, cwd=self._workingDir, stdout=stdoutLog) print 'run cmd:', cmd mothurProc.wait() if outLog is not None: stdoutLog.close() print 'mothur return code:', mothurProc.returncode if mothurProc.returncode != 0: raise Exception("Command returned with non-zero %s status: %s" % (mothurProc.returncode, cmd)) else: print 'Cannot run mothur since your system is not "posix" but', str('"' + os.name + '"'), '\n', cmd #transform mothur prediction files to the tab separated files self.mothurPredToTabSepPred(mothurPredFileName, predFileName) def mothurPredToTabSepPred(self, mothurPredFileName, outPredFileName): """ Transforms the mothur output prediction file (*.taxonomy) to the tab separated prediction file seqName tab ncbid tab weight. """ try: fr = open(os.path.normpath(mothurPredFileName),'r') except Exception: sys.stderr.write("Cannot open file:" + mothurPredFileName + '\n') raise else: try: fw = open(os.path.normpath(outPredFileName), 'w') lineCount = 0 for line in fr: line = common.noNewLine(line) try: if re.match(r'^[0-9]+_[0-9]+_[0-9]+_[0-9]+.*', line): name = re.sub(r'([0-9]+_[0-9]+)_[0-9]+_[0-9]+_[\+\-\t ]+.*', r'\1' , line) tag = re.sub(r'[0-9]+_[0-9]+_([0-9]+_[0-9]+_[\+\-]+)[\t ]+.*', r'\1' , line) placementList = re.sub(r'[0-9]+_[0-9]+_[0-9]+_[0-9]+_[\+\-\t ]+(.*)', r'\1' , line.replace('unclassified;', '')).rsplit(';') if len(placementList) < 2: continue placement = placementList[-2] try: clade = int(re.sub('([0-9]+)$.*', r'\1' , placement)) except ValueError: continue weight = float(re.sub('[0-9]+\(([0-9\.]+)$', r'\1' , placement)) lineCount += 1 if lineCount == 1: fw.write(name + '\t' + str(clade) + '\t' + str(weight) + '\t' + str(tag)) else: fw.write('\n' + name + '\t' + str(clade) + '\t' + str(weight) + '\t' + str(tag)) except Exception: sys.stderr.write('Cannot parse line: ' + str(lineCount) + 'in file: ' + mothurPredFileName + '\n') raise except Exception: sys.stderr.write("Cannot write to file:" + outPredFileName + '\n') raise finally: fw.close() fr.close() # def setCandidatePlacementFrom16S23S5S(self, sequences, taxonomy, inputFastaFile): #set assigned by the 16S countList[0] #set assigned by the 23S countList[1] #intersection = both countList[3] #predFileName16S = str(inputFastaFile + '.16P') predFileName16S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.16P')) #predFileName23S = str(inputFastaFile + '.23P') predFileName23S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.23P')) #predFileName5S = str(inputFastaFile + '.5P') predFileName5S = os.path.join(self._workingDir, str(os.path.basename(inputFastaFile) + '.5P')) try: seqIdSet16S = self._setCandidatePlacement(sequences, taxonomy, predFileName16S, '16S_rRNA') except Exception: seqIdSet16S = set() print("Can't set candidate placement from 16S classification.") try: seqIdSet23S = self._setCandidatePlacement(sequences, taxonomy, predFileName23S, '23S_rRNA') except Exception: seqIdSet23S = set() print("Can't set candidate placement from 23S sequences.") try: seqIdSet5S = self._setCandidatePlacement(sequences, taxonomy, predFileName5S, '5S_rRNA') except Exception: seqIdSet5S = set() print("Can't set candidate placement from 5S sequences.") intersectSet = seqIdSet16S | seqIdSet23S | seqIdSet5S return [len(seqIdSet16S),len(seqIdSet23S),len(seqIdSet5S),len(intersectSet)] def _setCandidatePlacement(self, sequences, taxonomy, predFileName, source): assignedIdList = [] try: f = open(os.path.normpath(predFileName),'r') except Exception: print "Cannot open file:", predFileName raise else: for line in f: line = common.noNewLine(line) if re.match(r'^[0-9]+_[0-9]+\t[0-9]+\t[0-9\.]+\t[^\t]+$', line): scaffoldId = int(re.sub(r'^([0-9]+)_[0-9]+\t[0-9]+\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) contigId = int(re.sub(r'^[0-9]+_([0-9]+)\t[0-9]+\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) ncbid = int(re.sub(r'^[0-9]+_[0-9]+\t([0-9]+)\t[0-9\.]+\t[^\t]+$',r'\1' ,line)) weight = float(re.sub(r'^[0-9]+_[0-9]+\t[0-9]+\t([0-9\.]+)\t[^\t]+$',r'\1' ,line)) tag = str(re.sub(r'^[0-9]+_[0-9]+\t[0-9]+\t[0-9\.]+\t([^\t]+)$',r'\1' ,line)) if ncbid != 1: taxPathDict = taxonomy.getPathToRoot(ncbid) if taxPathDict is not None and taxPathDict.keys() >= 1: sequences.setCandidateTaxonomyPath(contigId, scaffoldId, taxPathDict, weight, source, tag) assignedIdList.append(contigId) else: sys.stderr.write(str('No taxonomic path found for ncbid: ' + str(ncbid))) finally: f.close() return set(assignedIdList) if __name__ == "__main__": line = '125_528_- 2157(100);28890(95.3333);183925(95.3333);2158(95.3333);2159(95.3333);2172(95.3333);unclassified;unclassified;' try: name = re.sub('([0-9]+_[0-9]+)_[\+\-\t ]+.*', r'\1' , line) placement = re.sub('[0-9]+_[0-9]+_[\+\-\t ]+(.*)', r'\1' , line.replace('unclassified;', '')).rsplit(';')[-2] clade = int(re.sub('([0-9]+)$.*', r'\1' , placement)) weight = float(re.sub('[0-9]+\(([0-9\.]+)$', r'\1' , placement)) except Exception: print sys.stderr.write('Cannot parse line in file x' + '\n') print name print placement print clade print weight

false

true

f72007805c2d3ca886d768516835db709c0dc08b

1,117

py

Python

python files/area_calcs.py

dhbesson/abc_visualization

b024bf551e0e331e3f7bd9d63dbe1437a3c25aa7

[ "MIT" ]

null

python files/area_calcs.py

dhbesson/abc_visualization

b024bf551e0e331e3f7bd9d63dbe1437a3c25aa7

[ "MIT" ]

null

python files/area_calcs.py

dhbesson/abc_visualization

b024bf551e0e331e3f7bd9d63dbe1437a3c25aa7

[ "MIT" ]

null

import requests, csv, sys, os, time, json, codecs server = "https://cloudrf.com" # dir = "calculations/antennas_1W_2m" # Open CSV file import codecs # csvfile = csv.reader(codecs.open('antennas.csv', 'rU', 'utf-16')) uid = 'YOUR CLOUDRF UID HERE' key = 'YOUR CLOUDRF KEY HERE' def calc_area(dir,csvfile_loc): n = 0 csvfile = csv.DictReader(open(csvfile_loc)) if not os.path.exists(dir): os.makedirs(dir) for row in csvfile: # Pause script. Important otherwise server will ban you. time.sleep(1) start_time = time.time() # Stopwatch start # print row r = requests.post(server + "/API/area", data=row) print(r.text) # try: j = json.loads(r.text) r = requests.get(j['kmz']) fn = dir + os.sep + str(row['nam']) + ".kmz" file = open(fn, "wb") file.write(r.content) file.close() print("Saved to %s" % fn) elapsed = round(time.time() - start_time, 1) # Stopwatch print("Elapsed: " + str(elapsed) + "s") n = n + 1

27.243902

68

0.554163

import requests, csv, sys, os, time, json, codecs server = "https://cloudrf.com" import codecs uid = 'YOUR CLOUDRF UID HERE' key = 'YOUR CLOUDRF KEY HERE' def calc_area(dir,csvfile_loc): n = 0 csvfile = csv.DictReader(open(csvfile_loc)) if not os.path.exists(dir): os.makedirs(dir) for row in csvfile: time.sleep(1) start_time = time.time() r = requests.post(server + "/API/area", data=row) print(r.text) j = json.loads(r.text) r = requests.get(j['kmz']) fn = dir + os.sep + str(row['nam']) + ".kmz" file = open(fn, "wb") file.write(r.content) file.close() print("Saved to %s" % fn) elapsed = round(time.time() - start_time, 1) print("Elapsed: " + str(elapsed) + "s") n = n + 1

true

f72008a164fc940a1cd12de39700a25410e41ad5

3,485

py

Python

src/the_tale/the_tale/game/bills/models.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

null

src/the_tale/the_tale/game/bills/models.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

null

src/the_tale/the_tale/game/bills/models.py

al-arz/the-tale

542770257eb6ebd56a5ac44ea1ef93ff4ab19eb5

[ "BSD-3-Clause" ]

null

import smart_imports smart_imports.all() class Bill(django_models.Model): CAPTION_MIN_LENGTH = 6 CAPTION_MAX_LENGTH = 256 created_at = django_models.DateTimeField(auto_now_add=True, null=False) updated_at = django_models.DateTimeField(auto_now_add=True, null=False) # MUST setupped by hand voting_end_at = django_models.DateTimeField(null=True, blank=True) created_at_turn = django_models.IntegerField(null=False) applyed_at_turn = django_models.IntegerField(null=True, blank=True) ended_at = django_models.DateTimeField(null=True, blank=True) owner = django_models.ForeignKey('accounts.Account', null=True, related_name='+', on_delete=django_models.SET_NULL) caption = django_models.CharField(max_length=CAPTION_MAX_LENGTH) type = rels_django.RelationIntegerField(relation=relations.BILL_TYPE, db_index=True) state = rels_django.RelationIntegerField(relation=relations.BILL_STATE, db_index=True) approved_by_moderator = django_models.BooleanField(default=False, db_index=True) remove_initiator = django_models.ForeignKey('accounts.Account', null=True, blank=True, related_name='+', on_delete=django_models.SET_NULL) technical_data = django_models.TextField(null=False, blank=True, default={}) chronicle_on_accepted = django_models.TextField(null=False, blank=True, default='') # we should not remove bill when ocasionally remove forum thread forum_thread = django_models.ForeignKey(forum_models.Thread, null=True, blank=True, related_name='+', on_delete=django_models.SET_NULL) votes_for = django_models.IntegerField(default=0) votes_against = django_models.IntegerField(default=0) votes_refrained = django_models.IntegerField(default=0) # fields to store config values after processing state (since they can be changed in future) min_votes_percents_required = django_models.FloatField(default=0.0) is_declined = django_models.BooleanField(blank=True, default=False) declined_by = django_models.ForeignKey('bills.Bill', null=True, default=None, related_name='+', blank=True, on_delete=django_models.SET_NULL) depends_on = django_models.ForeignKey('bills.Bill', null=True, default=None, related_name='+', blank=True, on_delete=django_models.SET_NULL) def __str__(self): return '{}-{}'.format(self.id, self.caption) class Meta: permissions = (("moderate_bill", "Может администрировать записи в Книге Судеб"), ) class Actor(django_models.Model): # ATTENTION: if you want to make building an actor, remember, that after it recreated # (for same person after destroying previouse building) # it first fully removed from base (previouse building) and only then created created_at = django_models.DateTimeField(auto_now_add=True, null=False) bill = django_models.ForeignKey(Bill, null=False, on_delete=django_models.CASCADE) place = django_models.ForeignKey('places.Place', null=True, related_name='+', on_delete=django_models.CASCADE) class Vote(django_models.Model): created_at = django_models.DateTimeField(auto_now_add=True, null=False) owner = django_models.ForeignKey('accounts.Account', null=True, related_name='+', on_delete=django_models.SET_NULL) bill = django_models.ForeignKey(Bill, null=False, on_delete=django_models.CASCADE) type = rels_django.RelationIntegerField(relation=relations.VOTE_TYPE, db_index=True) class Meta: unique_together = (('owner', 'bill'),)

42.5

145

0.766428

import smart_imports smart_imports.all() class Bill(django_models.Model): CAPTION_MIN_LENGTH = 6 CAPTION_MAX_LENGTH = 256 created_at = django_models.DateTimeField(auto_now_add=True, null=False) updated_at = django_models.DateTimeField(auto_now_add=True, null=False) voting_end_at = django_models.DateTimeField(null=True, blank=True) created_at_turn = django_models.IntegerField(null=False) applyed_at_turn = django_models.IntegerField(null=True, blank=True) ended_at = django_models.DateTimeField(null=True, blank=True) owner = django_models.ForeignKey('accounts.Account', null=True, related_name='+', on_delete=django_models.SET_NULL) caption = django_models.CharField(max_length=CAPTION_MAX_LENGTH) type = rels_django.RelationIntegerField(relation=relations.BILL_TYPE, db_index=True) state = rels_django.RelationIntegerField(relation=relations.BILL_STATE, db_index=True) approved_by_moderator = django_models.BooleanField(default=False, db_index=True) remove_initiator = django_models.ForeignKey('accounts.Account', null=True, blank=True, related_name='+', on_delete=django_models.SET_NULL) technical_data = django_models.TextField(null=False, blank=True, default={}) chronicle_on_accepted = django_models.TextField(null=False, blank=True, default='') forum_thread = django_models.ForeignKey(forum_models.Thread, null=True, blank=True, related_name='+', on_delete=django_models.SET_NULL) votes_for = django_models.IntegerField(default=0) votes_against = django_models.IntegerField(default=0) votes_refrained = django_models.IntegerField(default=0) min_votes_percents_required = django_models.FloatField(default=0.0) is_declined = django_models.BooleanField(blank=True, default=False) declined_by = django_models.ForeignKey('bills.Bill', null=True, default=None, related_name='+', blank=True, on_delete=django_models.SET_NULL) depends_on = django_models.ForeignKey('bills.Bill', null=True, default=None, related_name='+', blank=True, on_delete=django_models.SET_NULL) def __str__(self): return '{}-{}'.format(self.id, self.caption) class Meta: permissions = (("moderate_bill", "Может администрировать записи в Книге Судеб"), ) class Actor(django_models.Model): created_at = django_models.DateTimeField(auto_now_add=True, null=False) bill = django_models.ForeignKey(Bill, null=False, on_delete=django_models.CASCADE) place = django_models.ForeignKey('places.Place', null=True, related_name='+', on_delete=django_models.CASCADE) class Vote(django_models.Model): created_at = django_models.DateTimeField(auto_now_add=True, null=False) owner = django_models.ForeignKey('accounts.Account', null=True, related_name='+', on_delete=django_models.SET_NULL) bill = django_models.ForeignKey(Bill, null=False, on_delete=django_models.CASCADE) type = rels_django.RelationIntegerField(relation=relations.VOTE_TYPE, db_index=True) class Meta: unique_together = (('owner', 'bill'),)

true

f72008f42f54ea078b631fc42689eec8279d667b

486

py

Python

April 2021/Furthest Building You Can Reach.py

parikshitgupta1/leetcode

eba6c11740dc7597204af127c0f4c2163376294f

[ "MIT" ]

null

April 2021/Furthest Building You Can Reach.py

parikshitgupta1/leetcode

eba6c11740dc7597204af127c0f4c2163376294f

[ "MIT" ]

null

April 2021/Furthest Building You Can Reach.py

parikshitgupta1/leetcode

eba6c11740dc7597204af127c0f4c2163376294f

[ "MIT" ]

null

class Solution: def furthestBuilding(self, H, bricks, ladders): jumps_pq = [] for i in range(len(H) - 1): jump_height = H[i + 1] - H[i] if jump_height <= 0: continue heappush(jumps_pq, jump_height) if len(jumps_pq) > ladders: bricks -= heappop(jumps_pq) if(bricks < 0) : return i return len(H) - 1 a = Solution() print(a.furthestBuilding([4,12,2,7,3,18,20,3,19], 10, 2))

28.588235

57

0.522634

class Solution: def furthestBuilding(self, H, bricks, ladders): jumps_pq = [] for i in range(len(H) - 1): jump_height = H[i + 1] - H[i] if jump_height <= 0: continue heappush(jumps_pq, jump_height) if len(jumps_pq) > ladders: bricks -= heappop(jumps_pq) if(bricks < 0) : return i return len(H) - 1 a = Solution() print(a.furthestBuilding([4,12,2,7,3,18,20,3,19], 10, 2))

true

f72008f569b6e13c90d5063ad392e029122f1919

228

py

Python

Itertools/itertools.product.py

AndreasGeiger/hackerrank-python

a436c207e62b32f70a6b4279bb641a3c4d90e112

[ "MIT" ]

null

Itertools/itertools.product.py

AndreasGeiger/hackerrank-python

a436c207e62b32f70a6b4279bb641a3c4d90e112

[ "MIT" ]

null

Itertools/itertools.product.py

AndreasGeiger/hackerrank-python

a436c207e62b32f70a6b4279bb641a3c4d90e112

[ "MIT" ]

null

from itertools import product listA = list(map(int, input().split())) listB = list(map(int, input().split())) productLists = list(product(listA, listB)) for i in range(len(productLists)): print(productLists[i], end=" ")

20.727273

42

0.684211

from itertools import product listA = list(map(int, input().split())) listB = list(map(int, input().split())) productLists = list(product(listA, listB)) for i in range(len(productLists)): print(productLists[i], end=" ")

true

f7200945eace3e7c67af32832e8436d62e73a7ee

3,102

py

Python

nativepython/type_wrappers/range_wrapper.py

szymonlipinski/nativepython

5f0bcc709b99a43681488f2753eccc2ac37a0334

[ "Apache-2.0" ]

null

nativepython/type_wrappers/range_wrapper.py

szymonlipinski/nativepython

5f0bcc709b99a43681488f2753eccc2ac37a0334

[ "Apache-2.0" ]

null

nativepython/type_wrappers/range_wrapper.py

szymonlipinski/nativepython

5f0bcc709b99a43681488f2753eccc2ac37a0334

[ "Apache-2.0" ]

null

# Coyright 2017-2019 Nativepython Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from nativepython.type_wrappers.wrapper import Wrapper import nativepython.native_ast as native_ast class RangeWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = False def __init__(self): super().__init__((range, "type")) def getNativeLayoutType(self): return native_ast.Type.Void() def convert_call(self, context, expr, args, kwargs): if len(args) == 1 and not kwargs: arg = args[0].toInt64() if not arg: return None return context.pushPod( _RangeInstanceWrapper, arg.nonref_expr ) return super().convert_call(context, expr, args, kwargs) class RangeInstanceWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = False def __init__(self): super().__init__((range, "instance")) def getNativeLayoutType(self): return native_ast.Int64 def convert_method_call(self, context, expr, methodname, args, kwargs): if methodname == "__iter__" and not args and not kwargs: return context.push( _RangeIteratorWrapper, lambda instance: instance.expr.ElementPtrIntegers(0, 0).store(-1) >> instance.expr.ElementPtrIntegers(0, 1).store(expr.nonref_expr) ) return super().convert_method_call(context, expr, methodname, args, kwargs) class RangeIteratorWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = True def __init__(self): super().__init__((range, "iterator")) def getNativeLayoutType(self): return native_ast.Type.Struct( element_types=(("count", native_ast.Int64), ("len", native_ast.Int64)), name="range_storage" ) def convert_next(self, context, expr): context.pushEffect( expr.expr.ElementPtrIntegers(0, 0).store( expr.expr.ElementPtrIntegers(0, 0).load().add(1) ) ) canContinue = context.pushPod( bool, expr.expr.ElementPtrIntegers(0, 0).load().lt( expr.expr.ElementPtrIntegers(0, 1).load() ) ) nextExpr = context.pushReference(int, expr.expr.ElementPtrIntegers(0, 0)) return nextExpr, canContinue _RangeWrapper = RangeWrapper() _RangeInstanceWrapper = RangeInstanceWrapper() _RangeIteratorWrapper = RangeIteratorWrapper()

31.333333

83

0.640554

from nativepython.type_wrappers.wrapper import Wrapper import nativepython.native_ast as native_ast class RangeWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = False def __init__(self): super().__init__((range, "type")) def getNativeLayoutType(self): return native_ast.Type.Void() def convert_call(self, context, expr, args, kwargs): if len(args) == 1 and not kwargs: arg = args[0].toInt64() if not arg: return None return context.pushPod( _RangeInstanceWrapper, arg.nonref_expr ) return super().convert_call(context, expr, args, kwargs) class RangeInstanceWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = False def __init__(self): super().__init__((range, "instance")) def getNativeLayoutType(self): return native_ast.Int64 def convert_method_call(self, context, expr, methodname, args, kwargs): if methodname == "__iter__" and not args and not kwargs: return context.push( _RangeIteratorWrapper, lambda instance: instance.expr.ElementPtrIntegers(0, 0).store(-1) >> instance.expr.ElementPtrIntegers(0, 1).store(expr.nonref_expr) ) return super().convert_method_call(context, expr, methodname, args, kwargs) class RangeIteratorWrapper(Wrapper): is_pod = True is_empty = False is_pass_by_ref = True def __init__(self): super().__init__((range, "iterator")) def getNativeLayoutType(self): return native_ast.Type.Struct( element_types=(("count", native_ast.Int64), ("len", native_ast.Int64)), name="range_storage" ) def convert_next(self, context, expr): context.pushEffect( expr.expr.ElementPtrIntegers(0, 0).store( expr.expr.ElementPtrIntegers(0, 0).load().add(1) ) ) canContinue = context.pushPod( bool, expr.expr.ElementPtrIntegers(0, 0).load().lt( expr.expr.ElementPtrIntegers(0, 1).load() ) ) nextExpr = context.pushReference(int, expr.expr.ElementPtrIntegers(0, 0)) return nextExpr, canContinue _RangeWrapper = RangeWrapper() _RangeInstanceWrapper = RangeInstanceWrapper() _RangeIteratorWrapper = RangeIteratorWrapper()

true

f72009f2a950749e199cacf800fa7cbce9a95e33

1,856

py

Python

ml-agents-envs/mlagents_envs/registry/base_registry_entry.py

bobcy2015/ml-agents

5d02292ad889f1884fa98bd92f127f17cbfe0112

[ "Apache-2.0" ]

13,653

2017-09-19T15:56:02.000Z

2022-03-31T18:55:07.000Z

ml-agents-envs/mlagents_envs/registry/base_registry_entry.py

bobcy2015/ml-agents

5d02292ad889f1884fa98bd92f127f17cbfe0112

[ "Apache-2.0" ]

3,623

2017-09-20T02:50:20.000Z

2022-03-31T06:37:25.000Z

ml-agents-envs/mlagents_envs/registry/base_registry_entry.py

bobcy2015/ml-agents

5d02292ad889f1884fa98bd92f127f17cbfe0112

[ "Apache-2.0" ]

4,130

2017-09-19T17:36:34.000Z

2022-03-31T12:54:55.000Z

from abc import abstractmethod from typing import Any, Optional from mlagents_envs.base_env import BaseEnv class BaseRegistryEntry: def __init__( self, identifier: str, expected_reward: Optional[float], description: Optional[str], ): """ BaseRegistryEntry allows launching a Unity Environment with its make method. :param identifier: The name of the Unity Environment. :param expected_reward: The cumulative reward that an Agent must receive for the task to be considered solved. :param description: A description of the Unity Environment. Contains human readable information about potential special arguments that the make method can take as well as information regarding the observation, reward, actions, behaviors and number of agents in the Environment. """ self._identifier = identifier self._expected_reward = expected_reward self._description = description @property def identifier(self) -> str: """ The unique identifier of the entry """ return self._identifier @property def expected_reward(self) -> Optional[float]: """ The cumulative reward that an Agent must receive for the task to be considered solved. """ return self._expected_reward @property def description(self) -> Optional[str]: """ A description of the Unity Environment the entry can make. """ return self._description @abstractmethod def make(self, **kwargs: Any) -> BaseEnv: """ This method creates a Unity BaseEnv (usually a UnityEnvironment). """ raise NotImplementedError( f"The make() method not implemented for entry {self.identifier}" )

32.561404

87

0.649246

from abc import abstractmethod from typing import Any, Optional from mlagents_envs.base_env import BaseEnv class BaseRegistryEntry: def __init__( self, identifier: str, expected_reward: Optional[float], description: Optional[str], ): self._identifier = identifier self._expected_reward = expected_reward self._description = description @property def identifier(self) -> str: return self._identifier @property def expected_reward(self) -> Optional[float]: return self._expected_reward @property def description(self) -> Optional[str]: return self._description @abstractmethod def make(self, **kwargs: Any) -> BaseEnv: raise NotImplementedError( f"The make() method not implemented for entry {self.identifier}" )

true

f7200a2703afba3d344293f747d73f0e8c66c472

14,266

py

Python

sdk/python/pulumi_azure_native/storagesync/v20180701/get_registered_server.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/storagesync/v20180701/get_registered_server.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/storagesync/v20180701/get_registered_server.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'GetRegisteredServerResult', 'AwaitableGetRegisteredServerResult', 'get_registered_server', ] @pulumi.output_type class GetRegisteredServerResult: """ Registered Server resource. """ def __init__(__self__, agent_version=None, cluster_id=None, cluster_name=None, discovery_endpoint_uri=None, friendly_name=None, id=None, last_heart_beat=None, last_operation_name=None, last_workflow_id=None, management_endpoint_uri=None, monitoring_configuration=None, name=None, provisioning_state=None, resource_location=None, server_certificate=None, server_id=None, server_managementt_error_code=None, server_os_version=None, server_role=None, service_location=None, storage_sync_service_uid=None, type=None): if agent_version and not isinstance(agent_version, str): raise TypeError("Expected argument 'agent_version' to be a str") pulumi.set(__self__, "agent_version", agent_version) if cluster_id and not isinstance(cluster_id, str): raise TypeError("Expected argument 'cluster_id' to be a str") pulumi.set(__self__, "cluster_id", cluster_id) if cluster_name and not isinstance(cluster_name, str): raise TypeError("Expected argument 'cluster_name' to be a str") pulumi.set(__self__, "cluster_name", cluster_name) if discovery_endpoint_uri and not isinstance(discovery_endpoint_uri, str): raise TypeError("Expected argument 'discovery_endpoint_uri' to be a str") pulumi.set(__self__, "discovery_endpoint_uri", discovery_endpoint_uri) if friendly_name and not isinstance(friendly_name, str): raise TypeError("Expected argument 'friendly_name' to be a str") pulumi.set(__self__, "friendly_name", friendly_name) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_heart_beat and not isinstance(last_heart_beat, str): raise TypeError("Expected argument 'last_heart_beat' to be a str") pulumi.set(__self__, "last_heart_beat", last_heart_beat) if last_operation_name and not isinstance(last_operation_name, str): raise TypeError("Expected argument 'last_operation_name' to be a str") pulumi.set(__self__, "last_operation_name", last_operation_name) if last_workflow_id and not isinstance(last_workflow_id, str): raise TypeError("Expected argument 'last_workflow_id' to be a str") pulumi.set(__self__, "last_workflow_id", last_workflow_id) if management_endpoint_uri and not isinstance(management_endpoint_uri, str): raise TypeError("Expected argument 'management_endpoint_uri' to be a str") pulumi.set(__self__, "management_endpoint_uri", management_endpoint_uri) if monitoring_configuration and not isinstance(monitoring_configuration, str): raise TypeError("Expected argument 'monitoring_configuration' to be a str") pulumi.set(__self__, "monitoring_configuration", monitoring_configuration) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if resource_location and not isinstance(resource_location, str): raise TypeError("Expected argument 'resource_location' to be a str") pulumi.set(__self__, "resource_location", resource_location) if server_certificate and not isinstance(server_certificate, str): raise TypeError("Expected argument 'server_certificate' to be a str") pulumi.set(__self__, "server_certificate", server_certificate) if server_id and not isinstance(server_id, str): raise TypeError("Expected argument 'server_id' to be a str") pulumi.set(__self__, "server_id", server_id) if server_managementt_error_code and not isinstance(server_managementt_error_code, int): raise TypeError("Expected argument 'server_managementt_error_code' to be a int") pulumi.set(__self__, "server_managementt_error_code", server_managementt_error_code) if server_os_version and not isinstance(server_os_version, str): raise TypeError("Expected argument 'server_os_version' to be a str") pulumi.set(__self__, "server_os_version", server_os_version) if server_role and not isinstance(server_role, str): raise TypeError("Expected argument 'server_role' to be a str") pulumi.set(__self__, "server_role", server_role) if service_location and not isinstance(service_location, str): raise TypeError("Expected argument 'service_location' to be a str") pulumi.set(__self__, "service_location", service_location) if storage_sync_service_uid and not isinstance(storage_sync_service_uid, str): raise TypeError("Expected argument 'storage_sync_service_uid' to be a str") pulumi.set(__self__, "storage_sync_service_uid", storage_sync_service_uid) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="agentVersion") def agent_version(self) -> Optional[str]: """ Registered Server Agent Version """ return pulumi.get(self, "agent_version") @property @pulumi.getter(name="clusterId") def cluster_id(self) -> Optional[str]: """ Registered Server clusterId """ return pulumi.get(self, "cluster_id") @property @pulumi.getter(name="clusterName") def cluster_name(self) -> Optional[str]: """ Registered Server clusterName """ return pulumi.get(self, "cluster_name") @property @pulumi.getter(name="discoveryEndpointUri") def discovery_endpoint_uri(self) -> Optional[str]: """ Resource discoveryEndpointUri """ return pulumi.get(self, "discovery_endpoint_uri") @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> Optional[str]: """ Friendly Name """ return pulumi.get(self, "friendly_name") @property @pulumi.getter def id(self) -> str: """ Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName} """ return pulumi.get(self, "id") @property @pulumi.getter(name="lastHeartBeat") def last_heart_beat(self) -> Optional[str]: """ Registered Server last heart beat """ return pulumi.get(self, "last_heart_beat") @property @pulumi.getter(name="lastOperationName") def last_operation_name(self) -> Optional[str]: """ Resource Last Operation Name """ return pulumi.get(self, "last_operation_name") @property @pulumi.getter(name="lastWorkflowId") def last_workflow_id(self) -> Optional[str]: """ Registered Server lastWorkflowId """ return pulumi.get(self, "last_workflow_id") @property @pulumi.getter(name="managementEndpointUri") def management_endpoint_uri(self) -> Optional[str]: """ Management Endpoint Uri """ return pulumi.get(self, "management_endpoint_uri") @property @pulumi.getter(name="monitoringConfiguration") def monitoring_configuration(self) -> Optional[str]: """ Monitoring Configuration """ return pulumi.get(self, "monitoring_configuration") @property @pulumi.getter def name(self) -> str: """ The name of the resource """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> Optional[str]: """ Registered Server Provisioning State """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[str]: """ Resource Location """ return pulumi.get(self, "resource_location") @property @pulumi.getter(name="serverCertificate") def server_certificate(self) -> Optional[str]: """ Registered Server Certificate """ return pulumi.get(self, "server_certificate") @property @pulumi.getter(name="serverId") def server_id(self) -> Optional[str]: """ Registered Server serverId """ return pulumi.get(self, "server_id") @property @pulumi.getter(name="serverManagementtErrorCode") def server_managementt_error_code(self) -> Optional[int]: """ Registered Server Management Error Code """ return pulumi.get(self, "server_managementt_error_code") @property @pulumi.getter(name="serverOSVersion") def server_os_version(self) -> Optional[str]: """ Registered Server OS Version """ return pulumi.get(self, "server_os_version") @property @pulumi.getter(name="serverRole") def server_role(self) -> Optional[str]: """ Registered Server serverRole """ return pulumi.get(self, "server_role") @property @pulumi.getter(name="serviceLocation") def service_location(self) -> Optional[str]: """ Service Location """ return pulumi.get(self, "service_location") @property @pulumi.getter(name="storageSyncServiceUid") def storage_sync_service_uid(self) -> Optional[str]: """ Registered Server storageSyncServiceUid """ return pulumi.get(self, "storage_sync_service_uid") @property @pulumi.getter def type(self) -> str: """ The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts" """ return pulumi.get(self, "type") class AwaitableGetRegisteredServerResult(GetRegisteredServerResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetRegisteredServerResult( agent_version=self.agent_version, cluster_id=self.cluster_id, cluster_name=self.cluster_name, discovery_endpoint_uri=self.discovery_endpoint_uri, friendly_name=self.friendly_name, id=self.id, last_heart_beat=self.last_heart_beat, last_operation_name=self.last_operation_name, last_workflow_id=self.last_workflow_id, management_endpoint_uri=self.management_endpoint_uri, monitoring_configuration=self.monitoring_configuration, name=self.name, provisioning_state=self.provisioning_state, resource_location=self.resource_location, server_certificate=self.server_certificate, server_id=self.server_id, server_managementt_error_code=self.server_managementt_error_code, server_os_version=self.server_os_version, server_role=self.server_role, service_location=self.service_location, storage_sync_service_uid=self.storage_sync_service_uid, type=self.type) def get_registered_server(resource_group_name: Optional[str] = None, server_id: Optional[str] = None, storage_sync_service_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRegisteredServerResult: """ Registered Server resource. :param str resource_group_name: The name of the resource group. The name is case insensitive. :param str server_id: GUID identifying the on-premises server. :param str storage_sync_service_name: Name of Storage Sync Service resource. """ __args__ = dict() __args__['resourceGroupName'] = resource_group_name __args__['serverId'] = server_id __args__['storageSyncServiceName'] = storage_sync_service_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:storagesync/v20180701:getRegisteredServer', __args__, opts=opts, typ=GetRegisteredServerResult).value return AwaitableGetRegisteredServerResult( agent_version=__ret__.agent_version, cluster_id=__ret__.cluster_id, cluster_name=__ret__.cluster_name, discovery_endpoint_uri=__ret__.discovery_endpoint_uri, friendly_name=__ret__.friendly_name, id=__ret__.id, last_heart_beat=__ret__.last_heart_beat, last_operation_name=__ret__.last_operation_name, last_workflow_id=__ret__.last_workflow_id, management_endpoint_uri=__ret__.management_endpoint_uri, monitoring_configuration=__ret__.monitoring_configuration, name=__ret__.name, provisioning_state=__ret__.provisioning_state, resource_location=__ret__.resource_location, server_certificate=__ret__.server_certificate, server_id=__ret__.server_id, server_managementt_error_code=__ret__.server_managementt_error_code, server_os_version=__ret__.server_os_version, server_role=__ret__.server_role, service_location=__ret__.service_location, storage_sync_service_uid=__ret__.storage_sync_service_uid, type=__ret__.type)

41.71345

517

0.683233

import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'GetRegisteredServerResult', 'AwaitableGetRegisteredServerResult', 'get_registered_server', ] @pulumi.output_type class GetRegisteredServerResult: def __init__(__self__, agent_version=None, cluster_id=None, cluster_name=None, discovery_endpoint_uri=None, friendly_name=None, id=None, last_heart_beat=None, last_operation_name=None, last_workflow_id=None, management_endpoint_uri=None, monitoring_configuration=None, name=None, provisioning_state=None, resource_location=None, server_certificate=None, server_id=None, server_managementt_error_code=None, server_os_version=None, server_role=None, service_location=None, storage_sync_service_uid=None, type=None): if agent_version and not isinstance(agent_version, str): raise TypeError("Expected argument 'agent_version' to be a str") pulumi.set(__self__, "agent_version", agent_version) if cluster_id and not isinstance(cluster_id, str): raise TypeError("Expected argument 'cluster_id' to be a str") pulumi.set(__self__, "cluster_id", cluster_id) if cluster_name and not isinstance(cluster_name, str): raise TypeError("Expected argument 'cluster_name' to be a str") pulumi.set(__self__, "cluster_name", cluster_name) if discovery_endpoint_uri and not isinstance(discovery_endpoint_uri, str): raise TypeError("Expected argument 'discovery_endpoint_uri' to be a str") pulumi.set(__self__, "discovery_endpoint_uri", discovery_endpoint_uri) if friendly_name and not isinstance(friendly_name, str): raise TypeError("Expected argument 'friendly_name' to be a str") pulumi.set(__self__, "friendly_name", friendly_name) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if last_heart_beat and not isinstance(last_heart_beat, str): raise TypeError("Expected argument 'last_heart_beat' to be a str") pulumi.set(__self__, "last_heart_beat", last_heart_beat) if last_operation_name and not isinstance(last_operation_name, str): raise TypeError("Expected argument 'last_operation_name' to be a str") pulumi.set(__self__, "last_operation_name", last_operation_name) if last_workflow_id and not isinstance(last_workflow_id, str): raise TypeError("Expected argument 'last_workflow_id' to be a str") pulumi.set(__self__, "last_workflow_id", last_workflow_id) if management_endpoint_uri and not isinstance(management_endpoint_uri, str): raise TypeError("Expected argument 'management_endpoint_uri' to be a str") pulumi.set(__self__, "management_endpoint_uri", management_endpoint_uri) if monitoring_configuration and not isinstance(monitoring_configuration, str): raise TypeError("Expected argument 'monitoring_configuration' to be a str") pulumi.set(__self__, "monitoring_configuration", monitoring_configuration) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if resource_location and not isinstance(resource_location, str): raise TypeError("Expected argument 'resource_location' to be a str") pulumi.set(__self__, "resource_location", resource_location) if server_certificate and not isinstance(server_certificate, str): raise TypeError("Expected argument 'server_certificate' to be a str") pulumi.set(__self__, "server_certificate", server_certificate) if server_id and not isinstance(server_id, str): raise TypeError("Expected argument 'server_id' to be a str") pulumi.set(__self__, "server_id", server_id) if server_managementt_error_code and not isinstance(server_managementt_error_code, int): raise TypeError("Expected argument 'server_managementt_error_code' to be a int") pulumi.set(__self__, "server_managementt_error_code", server_managementt_error_code) if server_os_version and not isinstance(server_os_version, str): raise TypeError("Expected argument 'server_os_version' to be a str") pulumi.set(__self__, "server_os_version", server_os_version) if server_role and not isinstance(server_role, str): raise TypeError("Expected argument 'server_role' to be a str") pulumi.set(__self__, "server_role", server_role) if service_location and not isinstance(service_location, str): raise TypeError("Expected argument 'service_location' to be a str") pulumi.set(__self__, "service_location", service_location) if storage_sync_service_uid and not isinstance(storage_sync_service_uid, str): raise TypeError("Expected argument 'storage_sync_service_uid' to be a str") pulumi.set(__self__, "storage_sync_service_uid", storage_sync_service_uid) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) @property @pulumi.getter(name="agentVersion") def agent_version(self) -> Optional[str]: return pulumi.get(self, "agent_version") @property @pulumi.getter(name="clusterId") def cluster_id(self) -> Optional[str]: return pulumi.get(self, "cluster_id") @property @pulumi.getter(name="clusterName") def cluster_name(self) -> Optional[str]: return pulumi.get(self, "cluster_name") @property @pulumi.getter(name="discoveryEndpointUri") def discovery_endpoint_uri(self) -> Optional[str]: return pulumi.get(self, "discovery_endpoint_uri") @property @pulumi.getter(name="friendlyName") def friendly_name(self) -> Optional[str]: return pulumi.get(self, "friendly_name") @property @pulumi.getter def id(self) -> str: return pulumi.get(self, "id") @property @pulumi.getter(name="lastHeartBeat") def last_heart_beat(self) -> Optional[str]: return pulumi.get(self, "last_heart_beat") @property @pulumi.getter(name="lastOperationName") def last_operation_name(self) -> Optional[str]: return pulumi.get(self, "last_operation_name") @property @pulumi.getter(name="lastWorkflowId") def last_workflow_id(self) -> Optional[str]: return pulumi.get(self, "last_workflow_id") @property @pulumi.getter(name="managementEndpointUri") def management_endpoint_uri(self) -> Optional[str]: return pulumi.get(self, "management_endpoint_uri") @property @pulumi.getter(name="monitoringConfiguration") def monitoring_configuration(self) -> Optional[str]: return pulumi.get(self, "monitoring_configuration") @property @pulumi.getter def name(self) -> str: return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> Optional[str]: return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="resourceLocation") def resource_location(self) -> Optional[str]: return pulumi.get(self, "resource_location") @property @pulumi.getter(name="serverCertificate") def server_certificate(self) -> Optional[str]: return pulumi.get(self, "server_certificate") @property @pulumi.getter(name="serverId") def server_id(self) -> Optional[str]: return pulumi.get(self, "server_id") @property @pulumi.getter(name="serverManagementtErrorCode") def server_managementt_error_code(self) -> Optional[int]: return pulumi.get(self, "server_managementt_error_code") @property @pulumi.getter(name="serverOSVersion") def server_os_version(self) -> Optional[str]: return pulumi.get(self, "server_os_version") @property @pulumi.getter(name="serverRole") def server_role(self) -> Optional[str]: return pulumi.get(self, "server_role") @property @pulumi.getter(name="serviceLocation") def service_location(self) -> Optional[str]: return pulumi.get(self, "service_location") @property @pulumi.getter(name="storageSyncServiceUid") def storage_sync_service_uid(self) -> Optional[str]: return pulumi.get(self, "storage_sync_service_uid") @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") class AwaitableGetRegisteredServerResult(GetRegisteredServerResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetRegisteredServerResult( agent_version=self.agent_version, cluster_id=self.cluster_id, cluster_name=self.cluster_name, discovery_endpoint_uri=self.discovery_endpoint_uri, friendly_name=self.friendly_name, id=self.id, last_heart_beat=self.last_heart_beat, last_operation_name=self.last_operation_name, last_workflow_id=self.last_workflow_id, management_endpoint_uri=self.management_endpoint_uri, monitoring_configuration=self.monitoring_configuration, name=self.name, provisioning_state=self.provisioning_state, resource_location=self.resource_location, server_certificate=self.server_certificate, server_id=self.server_id, server_managementt_error_code=self.server_managementt_error_code, server_os_version=self.server_os_version, server_role=self.server_role, service_location=self.service_location, storage_sync_service_uid=self.storage_sync_service_uid, type=self.type) def get_registered_server(resource_group_name: Optional[str] = None, server_id: Optional[str] = None, storage_sync_service_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRegisteredServerResult: __args__ = dict() __args__['resourceGroupName'] = resource_group_name __args__['serverId'] = server_id __args__['storageSyncServiceName'] = storage_sync_service_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:storagesync/v20180701:getRegisteredServer', __args__, opts=opts, typ=GetRegisteredServerResult).value return AwaitableGetRegisteredServerResult( agent_version=__ret__.agent_version, cluster_id=__ret__.cluster_id, cluster_name=__ret__.cluster_name, discovery_endpoint_uri=__ret__.discovery_endpoint_uri, friendly_name=__ret__.friendly_name, id=__ret__.id, last_heart_beat=__ret__.last_heart_beat, last_operation_name=__ret__.last_operation_name, last_workflow_id=__ret__.last_workflow_id, management_endpoint_uri=__ret__.management_endpoint_uri, monitoring_configuration=__ret__.monitoring_configuration, name=__ret__.name, provisioning_state=__ret__.provisioning_state, resource_location=__ret__.resource_location, server_certificate=__ret__.server_certificate, server_id=__ret__.server_id, server_managementt_error_code=__ret__.server_managementt_error_code, server_os_version=__ret__.server_os_version, server_role=__ret__.server_role, service_location=__ret__.service_location, storage_sync_service_uid=__ret__.storage_sync_service_uid, type=__ret__.type)

true

f7200af076a3e5be760cbc8be2e5197296b523de

428

py

Python

exams/forms.py

WillOnGit/exam-timetable

fec170025c39144299d61ea323eed3a000b61cf9

[ "MIT" ]

null

exams/forms.py

WillOnGit/exam-timetable

fec170025c39144299d61ea323eed3a000b61cf9

[ "MIT" ]

null

exams/forms.py

WillOnGit/exam-timetable

fec170025c39144299d61ea323eed3a000b61cf9

[ "MIT" ]

null

from .models import ExamVenue from django.forms import ModelForm class RestrictedResponseForm(ModelForm): def __init__(self, *args, **kwargs): super(RestrictedResponseForm, self).__init__(*args,**kwargs) try: self.fields['assigned_venue'].queryset = ExamVenue.objects.filter(exam=self.instance.exam) except: self.fields['assigned_venue'].queryset = ExamVenue.objects.none()

35.666667

102

0.700935

from .models import ExamVenue from django.forms import ModelForm class RestrictedResponseForm(ModelForm): def __init__(self, *args, **kwargs): super(RestrictedResponseForm, self).__init__(*args,**kwargs) try: self.fields['assigned_venue'].queryset = ExamVenue.objects.filter(exam=self.instance.exam) except: self.fields['assigned_venue'].queryset = ExamVenue.objects.none()

true

f7200b2aa450884bed39014c6f0f6fc44dd2a5aa

4,253

py

Python

src/Infraestructura/ccutils/databases/configuration.py

lbarriosh/cygnus-cloud

1a17fbb55de69adba2ec42db4c9a063865af4fbd

[ "Apache-2.0" ]

3

2017-09-03T22:01:35.000Z

2019-01-10T05:40:44.000Z

src/web/CygnusCloud/modules/ccutils/databases/configuration.py

lbarriosh/cygnus-cloud

1a17fbb55de69adba2ec42db4c9a063865af4fbd

[ "Apache-2.0" ]

null

src/web/CygnusCloud/modules/ccutils/databases/configuration.py

lbarriosh/cygnus-cloud

1a17fbb55de69adba2ec42db4c9a063865af4fbd

[ "Apache-2.0" ]

null

# -*- coding: utf8 -*- ''' ======================================================================== CygnusCloud ======================================================================== File: configuration.py Version: 3.0 Description: Database configurator definitions Copyright 2012-13 Luis Barrios Hernández, Adrián Fernández Hernández, Samuel Guayerbas Martín Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import MySQLdb import os.path from ccutils.processes.childProcessManager import ChildProcessManager class DBConfigurator(object): """ This class provides methods to configure databases. """ def __init__(self, rootPassword): ''' Initializes the configurator's state Args: rootPassword: root's password ''' self.__rootPassword = rootPassword def addUser(self, user, password, databaseName, allPrivileges=True): ''' Adds a new MySQL user Args: user: the new user's name password: the new user's password databaseName: the database's name allPrivileges: if True, the new user will be able to do everything with the database. If False, the new user will only be able to execute queries in the database. Returns: Nothing ''' conn = MySQLdb.Connection(host="localhost", user="root", passwd=self.__rootPassword) cursor = conn.cursor() if (allPrivileges): privileges = "ALL" else : privileges = "SELECT" cursor.execute("GRANT " + privileges + " ON " + databaseName + ".* TO '" + user + "'@'" + "localhost" + "' IDENTIFIED BY '" + password + "';") cursor.close() conn.close() def createDatabase(self, databaseName): ''' Creates a MySQL database Args: databaseName: the new database's name Returns: Nothing ''' db = MySQLdb.connect(host='localhost',user="root",passwd=self.__rootPassword) command = "CREATE DATABASE " + databaseName + ";" cursor = db.cursor() cursor.execute(command) cursor.close() db.close() def dropDatabase(self, databaseName): ''' Deletes a MySQL database Args: databaseName: the database to delete's name Returns: Nothing ''' db = MySQLdb.connect(host='localhost',user="root",passwd=self.__rootPassword) command = "DROP DATABASE " + databaseName + ";" cursor = db.cursor() cursor.execute(command) cursor.close() db.close() def runSQLScript(self, database, sqlFilePath, username="root", password=None): ''' Runs a SQL script Args: databaseName: the MySQL database to use sqlFilePath: the SQL script path username: a MySQL user name. password: the user's password ''' passwordCommand = "" if (password != None and len(password) != 0) : passwordCommand = "-p" + str(password) filePath = os.path.abspath(sqlFilePath) command = "mysql -uroot {0} -e \"source {1}\"".format(passwordCommand, filePath) ChildProcessManager.runCommandInForeground(command, Exception) @staticmethod def __isEmpty__(string): for c in string : if (c != ' ' and c != '\n' and c != '\r' and c != 't') : return False return True

36.350427

151

0.556078

import MySQLdb import os.path from ccutils.processes.childProcessManager import ChildProcessManager class DBConfigurator(object): def __init__(self, rootPassword): self.__rootPassword = rootPassword def addUser(self, user, password, databaseName, allPrivileges=True): conn = MySQLdb.Connection(host="localhost", user="root", passwd=self.__rootPassword) cursor = conn.cursor() if (allPrivileges): privileges = "ALL" else : privileges = "SELECT" cursor.execute("GRANT " + privileges + " ON " + databaseName + ".* TO '" + user + "'@'" + "localhost" + "' IDENTIFIED BY '" + password + "';") cursor.close() conn.close() def createDatabase(self, databaseName): db = MySQLdb.connect(host='localhost',user="root",passwd=self.__rootPassword) command = "CREATE DATABASE " + databaseName + ";" cursor = db.cursor() cursor.execute(command) cursor.close() db.close() def dropDatabase(self, databaseName): db = MySQLdb.connect(host='localhost',user="root",passwd=self.__rootPassword) command = "DROP DATABASE " + databaseName + ";" cursor = db.cursor() cursor.execute(command) cursor.close() db.close() def runSQLScript(self, database, sqlFilePath, username="root", password=None): passwordCommand = "" if (password != None and len(password) != 0) : passwordCommand = "-p" + str(password) filePath = os.path.abspath(sqlFilePath) command = "mysql -uroot {0} -e \"source {1}\"".format(passwordCommand, filePath) ChildProcessManager.runCommandInForeground(command, Exception) @staticmethod def __isEmpty__(string): for c in string : if (c != ' ' and c != '\n' and c != '\r' and c != 't') : return False return True

true

f7200b3bdc7b35e8600608ed9b126abac2ec17a1

557

py

Python

shop/migrations/0024_auto_20200311_0154.py

manson800819/test

6df7d92eababe76a54585cb8102a00a6d79ca467

[ "MIT" ]

null

shop/migrations/0024_auto_20200311_0154.py

manson800819/test

6df7d92eababe76a54585cb8102a00a6d79ca467

[ "MIT" ]

null

shop/migrations/0024_auto_20200311_0154.py

manson800819/test

6df7d92eababe76a54585cb8102a00a6d79ca467

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.29 on 2020-03-11 01:54 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('shop', '0023_auto_20200311_0137'), ] operations = [ migrations.AlterField( model_name='product', name='type1', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='products_t', to='shop.Type1'), ), ]

25.318182

125

0.653501

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('shop', '0023_auto_20200311_0137'), ] operations = [ migrations.AlterField( model_name='product', name='type1', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='products_t', to='shop.Type1'), ), ]

true

f7200b4756a033cb419019d6f292992490dafe65

28,992

py

Python

geoopt/manifolds/base.py

grapefroot/geoopt

8f219a820e24b87ac68136ff66af11b25d5c04c5

[ "Apache-2.0" ]

4

2020-01-27T15:37:19.000Z

2020-12-06T02:51:03.000Z

geoopt/manifolds/base.py

grapefroot/geoopt

8f219a820e24b87ac68136ff66af11b25d5c04c5

[ "Apache-2.0" ]

null

geoopt/manifolds/base.py

grapefroot/geoopt

8f219a820e24b87ac68136ff66af11b25d5c04c5

[ "Apache-2.0" ]

1

2021-05-07T22:01:41.000Z

import abc import torch.nn import itertools from typing import Optional, Tuple, Union __all__ = ["Manifold", "ScalingInfo"] class ScalingInfo(object): """ Scaling info for each argument that requires rescaling. .. code:: python scaled_value = value * scaling ** power if power != 0 else value For results it is not always required to set powers of scaling, then it is no-op. The convention for this info is the following. The output of a function is either a tuple or a single object. In any case, outputs are treated as positionals. Function inputs, in contrast, are treated by keywords. It is a common practice to maintain function signature when overriding, so this way may be considered as a sufficient in this particular scenario. The only required info for formula above is ``power``. """ # marks method to be not working with Scaled wrapper NotCompatible = object() __slots__ = ["kwargs", "results"] def __init__(self, *results: float, **kwargs: float): self.results = results self.kwargs = kwargs class ScalingStorage(dict): """ Helper class to make implementation transparent. This is just a dictionary with additional overriden ``__call__`` for more explicit and elegant API to declare members. A usage example may be found in :class:`Manifold`. Methods that require rescaling when wrapped into :class:`Scaled` should be defined as follows 1. Regular methods like ``dist``, ``dist2``, ``expmap``, ``retr`` etc. that are already present in the base class do not require registration, it has already happened in the base :class:`Manifold` class. 2. New methods (like in :class:`PoincareBall`) should be treated with care. .. code-block:: python class PoincareBall(Manifold): # make a class copy of __scaling__ info. Default methods are already present there __scaling__ = Manifold.__scaling__.copy() ... # here come regular implementation of the required methods @__scaling__(ScalingInfo(1)) # rescale output according to rule `out * scaling ** 1` def dist0(self, x: torch.Tensor, *, dim=-1, keepdim=False): return math.dist0(x, c=self.c, dim=dim, keepdim=keepdim) @__scaling__(ScalingInfo(u=-1)) # rescale argument `u` according to the rule `out * scaling ** -1` def expmap0(self, u: torch.Tensor, *, dim=-1, project=True): res = math.expmap0(u, c=self.c, dim=dim) if project: return math.project(res, c=self.c, dim=dim) else: return res ... # other special methods implementation 3. Some methods are not compliant with the above rescaling rules. We should mark them as `NotCompatible` .. code-block:: python # continuation of the PoincareBall definition @__scaling__(ScalingInfo.NotCompatible) def mobius_fn_apply( self, fn: callable, x: torch.Tensor, *args, dim=-1, project=True, **kwargs ): res = math.mobius_fn_apply(fn, x, *args, c=self.c, dim=dim, **kwargs) if project: return math.project(res, c=self.c, dim=dim) else: return res """ def __call__(self, scaling_info: ScalingInfo): def register(fn): self[fn.__name__] = scaling_info return fn return register def copy(self): return self.__class__(self) class Manifold(torch.nn.Module, metaclass=abc.ABCMeta): __scaling__ = ScalingStorage() # will be filled along with implementation below name = None ndim = None reversible = None forward = NotImplemented def __init__(self, **kwargs): super().__init__() @property def device(self) -> Optional[torch.device]: """ Manifold device. Returns ------- Optional[torch.device] """ p = next(itertools.chain(self.buffers(), self.parameters()), None) if p is not None: return p.device else: return None @property def dtype(self) -> Optional[torch.dtype]: """ Manifold dtype. Returns ------- Optional[torch.dtype] """ p = next(itertools.chain(self.buffers(), self.parameters()), None) if p is not None: return p.dtype else: return None def check_point( self, x: torch.Tensor, *, explain=False ) -> Union[Tuple[bool, Optional[str]], bool]: """ Check if point is valid to be used with the manifold. Parameters ---------- x : torch.Tensor point on the manifold explain: bool return an additional information on check Returns ------- bool boolean indicating if tensor is valid and reason of failure if False Notes ----- This check is compatible to what optimizer expects, last dimensions are treated as manifold dimensions """ ok, reason = self._check_shape(x.shape, "x") if explain: return ok, reason else: return ok def assert_check_point(self, x: torch.Tensor): """ Check if point is valid to be used with the manifold and raise an error with informative message on failure. Parameters ---------- x : torch.Tensor point on the manifold Notes ----- This check is compatible to what optimizer expects, last dimensions are treated as manifold dimensions """ ok, reason = self._check_shape(x.shape, "x") if not ok: raise ValueError( "`x` seems to be not valid " "tensor for {} manifold.\nerror: {}".format(self.name, reason) ) def check_vector(self, u: torch.Tensor, *, explain=False): """ Check if vector is valid to be used with the manifold. Parameters ---------- u : torch.Tensor vector on the tangent plane explain: bool return an additional information on check Returns ------- bool boolean indicating if tensor is valid and reason of failure if False Notes ----- This check is compatible to what optimizer expects, last dimensions are treated as manifold dimensions """ ok, reason = self._check_shape(u.shape, "u") if explain: return ok, reason else: return ok def assert_check_vector(self, u: torch.Tensor): """ Check if vector is valid to be used with the manifold and raise an error with informative message on failure. Parameters ---------- u : torch.Tensor vector on the tangent plane Notes ----- This check is compatible to what optimizer expects, last dimensions are treated as manifold dimensions """ ok, reason = self._check_shape(u.shape, "u") if not ok: raise ValueError( "`u` seems to be not valid " "tensor for {} manifold.\nerror: {}".format(self.name, reason) ) def check_point_on_manifold( self, x: torch.Tensor, *, explain=False, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: """ Check if point :math:`x` is lying on the manifold. Parameters ---------- x : torch.Tensor point on the manifold atol: float absolute tolerance as in :func:`numpy.allclose` rtol: float relative tolerance as in :func:`numpy.allclose` explain: bool return an additional information on check Returns ------- bool boolean indicating if tensor is valid and reason of failure if False Notes ----- This check is compatible to what optimizer expects, last dimensions are treated as manifold dimensions """ ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) if explain: return ok, reason else: return ok def assert_check_point_on_manifold(self, x: torch.Tensor, *, atol=1e-5, rtol=1e-5): """ Check if point :math`x` is lying on the manifold and raise an error with informative message on failure. Parameters ---------- x : torch.Tensor point on the manifold atol: float absolute tolerance as in :func:`numpy.allclose` rtol: float relative tolerance as in :func:`numpy.allclose` """ self.assert_check_point(x) ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) if not ok: raise ValueError( "`x` seems to be a tensor " "not lying on {} manifold.\nerror: {}".format(self.name, reason) ) def check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, ok_point=False, explain=False, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: """ Check if :math:`u` is lying on the tangent space to x. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor vector on the tangent space to :math:`x` atol: float absolute tolerance as in :func:`numpy.allclose` rtol: float relative tolerance as in :func:`numpy.allclose` explain: bool return an additional information on check ok_point: bool is a check for point required? Returns ------- bool boolean indicating if tensor is valid and reason of failure if False """ if not ok_point: ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_shape(u.shape, "u") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) else: ok = True reason = None if ok: ok, reason = self._check_vector_on_tangent(x, u, atol=atol, rtol=rtol) if explain: return ok, reason else: return ok def assert_check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, ok_point=False, atol=1e-5, rtol=1e-5 ): """ Check if u :math:`u` is lying on the tangent space to x and raise an error on fail. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor vector on the tangent space to :math:`x` atol: float absolute tolerance as in :func:`numpy.allclose` rtol: float relative tolerance as in :func:`numpy.allclose` ok_point: bool is a check for point required? """ if not ok_point: ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_shape(u.shape, "u") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) else: ok = True reason = None if ok: ok, reason = self._check_vector_on_tangent(x, u, atol=atol, rtol=rtol) if not ok: raise ValueError( "`u` seems to be a tensor " "not lying on tangent space to `x` for {} manifold.\nerror: {}".format( self.name, reason ) ) @__scaling__(ScalingInfo(1)) def dist(self, x: torch.Tensor, y: torch.Tensor, *, keepdim=False) -> torch.Tensor: """ Compute distance between 2 points on the manifold that is the shortest path along geodesics. Parameters ---------- x : torch.Tensor point on the manifold y : torch.Tensor point on the manifold keepdim : bool keep the last dim? Returns ------- torch.Tensor distance between two points """ raise NotImplementedError @__scaling__(ScalingInfo(2)) def dist2(self, x: torch.Tensor, y: torch.Tensor, *, keepdim=False) -> torch.Tensor: """ Compute squared distance between 2 points on the manifold that is the shortest path along geodesics. Parameters ---------- x : torch.Tensor point on the manifold y : torch.Tensor point on the manifold keepdim : bool keep the last dim? Returns ------- torch.Tensor squared distance between two points """ return self.dist(x, y, keepdim=keepdim) ** 2 @abc.abstractmethod @__scaling__(ScalingInfo(u=-1)) def retr(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: """ Perform a retraction from point :math:`x` with given direction :math:`u`. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` Returns ------- torch.Tensor transported point """ raise NotImplementedError @abc.abstractmethod @__scaling__(ScalingInfo(u=-1)) def expmap(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: r""" Perform an exponential map :math:`\operatorname{Exp}_x(u)`. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` Returns ------- torch.Tensor transported point """ raise NotImplementedError @__scaling__(ScalingInfo(1)) def logmap(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: r""" Perform an logarithmic map :math:`\operatorname{Log}_{x}(y)`. Parameters ---------- x : torch.Tensor point on the manifold y : torch.Tensor point on the manifold Returns ------- torch.Tensor tangent vector """ raise NotImplementedError @__scaling__(ScalingInfo(u=-1)) def expmap_transp( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: """ Perform an exponential map and vector transport from point :math:`x` with given direction :math:`u`. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : torch.Tensor tangent vector at point :math:`x` to be transported Returns ------- torch.Tensor transported point """ y = self.expmap(x, u) v_transp = self.transp(x, y, v) return y, v_transp @__scaling__(ScalingInfo(u=-1)) def retr_transp( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: """ Perform a retraction + vector transport at once. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : torch.Tensor tangent vector at point :math:`x` to be transported Returns ------- Tuple[torch.Tensor, torch.Tensor] transported point and vectors Notes ----- Sometimes this is a far more optimal way to preform retraction + vector transport """ y = self.retr(x, u) v_transp = self.transp(x, y, v) return y, v_transp @__scaling__(ScalingInfo(u=-1)) def transp_follow_retr( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> torch.Tensor: r""" Perform vector transport following :math:`u`: :math:`\mathfrak{T}_{x\to\operatorname{retr}(x, u)}(v)`. This operation is sometimes is much more simpler and can be optimized. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : torch.Tensor tangent vector at point :math:`x` to be transported Returns ------- torch.Tensor transported tensor """ y = self.retr(x, u) return self.transp(x, y, v) @__scaling__(ScalingInfo(u=-1)) def transp_follow_expmap( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> torch.Tensor: r""" Perform vector transport following :math:`u`: :math:`\mathfrak{T}_{x\to\operatorname{Exp}(x, u)}(v)`. Here, :math:`\operatorname{Exp}` is the best possible approximation of the true exponential map. There are cases when the exact variant is hard or impossible implement, therefore a fallback, non-exact, implementation is used. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : torch.Tensor tangent vector at point :math:`x` to be transported Returns ------- torch.Tensor transported tensor """ y = self.expmap(x, u) return self.transp(x, y, v) def transp(self, x: torch.Tensor, y: torch.Tensor, v: torch.Tensor) -> torch.Tensor: r""" Perform vector transport :math:`\mathfrak{T}_{x\to y}(v)`. Parameters ---------- x : torch.Tensor start point on the manifold y : torch.Tensor target point on the manifold v : torch.Tensor tangent vector at point :math:`x` Returns ------- torch.Tensor transported tensor """ raise NotImplementedError @abc.abstractmethod def inner( self, x: torch.Tensor, u: torch.Tensor, v=None, *, keepdim=False ) -> torch.Tensor: """ Inner product for tangent vectors at point :math:`x`. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : Optional[torch.Tensor] tangent vector at point :math:`x` keepdim : bool keep the last dim? Returns ------- torch.Tensor inner product (broadcasted) """ raise NotImplementedError def component_inner( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor = None ) -> torch.Tensor: """ Inner product for tangent vectors at point :math:`x` according to components of the manifold. The result of the function is same as ``inner`` with ``keepdim=True`` for all the manifolds except ProductManifold. For this manifold it acts different way computing inner product for each component and then building an output correctly tiling and reshaping the result. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` v : Optional[torch.Tensor] tangent vector at point :math:`x` Returns ------- torch.Tensor inner product component wise (broadcasted) Notes ----- The purpose of this method is better adaptive properties in optimization since ProductManifold will "hide" the structure in public API. """ return self.inner(x, u, v, keepdim=True) def norm(self, x: torch.Tensor, u: torch.Tensor, *, keepdim=False) -> torch.Tensor: """ Norm of a tangent vector at point :math:`x`. Parameters ---------- x : torch.Tensor point on the manifold u : torch.Tensor tangent vector at point :math:`x` keepdim : bool keep the last dim? Returns ------- torch.Tensor inner product (broadcasted) """ return self.inner(x, u, keepdim=keepdim) ** 0.5 @abc.abstractmethod def proju(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: """ Project vector :math:`u` on a tangent space for :math:`x`, usually is the same as :meth:`egrad2rgrad`. Parameters ---------- x torch.Tensor point on the manifold u torch.Tensor vector to be projected Returns ------- torch.Tensor projected vector """ raise NotImplementedError @abc.abstractmethod def egrad2rgrad(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: """ Transform gradient computed using autodiff to the correct Riemannian gradient for the point :math:`x`. Parameters ---------- x torch.Tensor point on the manifold u torch.Tensor gradient to be projected Returns ------- torch.Tensor grad vector in the Riemannian manifold """ raise NotImplementedError @abc.abstractmethod def projx(self, x: torch.Tensor) -> torch.Tensor: """ Project point :math:`x` on the manifold. Parameters ---------- x torch.Tensor point to be projected Returns ------- torch.Tensor projected point """ raise NotImplementedError def _check_shape( self, shape: Tuple[int], name: str ) -> Union[Tuple[bool, Optional[str]], bool]: """ Util to check shape. Exhaustive implementation for checking if a given point has valid dimension size, shape, etc. It should return boolean and a reason of failure if check is not passed Parameters ---------- shape : Tuple[int] shape of point on the manifold name : str name to be present in errors Returns ------- bool, str or None check result and the reason of fail if any """ ok = len(shape) >= self.ndim if not ok: reason = "'{}' on the {} requires more than {} dim".format( name, self, self.ndim ) else: reason = None return ok, reason def _assert_check_shape(self, shape: Tuple[int], name: str): """ Util to check shape and raise an error if needed. Exhaustive implementation for checking if a given point has valid dimension size, shape, etc. It will raise a ValueError if check is not passed Parameters ---------- shape : tuple shape of point on the manifold name : str name to be present in errors Raises ------ ValueError """ ok, reason = self._check_shape(shape, name) if not ok: raise ValueError(reason) @abc.abstractmethod def _check_point_on_manifold( self, x: torch.Tensor, *, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: """ Util to check point lies on the manifold. Exhaustive implementation for checking if a given point lies on the manifold. It should return boolean and a reason of failure if check is not passed. You can assume assert_check_point is already passed beforehand Parameters ---------- x torch.Tensor point on the manifold atol: float absolute tolerance as in :func:`numpy.allclose` rtol: float relative tolerance as in :func:`numpy.allclose` Returns ------- bool, str or None check result and the reason of fail if any """ # return True, None raise NotImplementedError @abc.abstractmethod def _check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: """ Util to check a vector belongs to the tangent space of a point. Exhaustive implementation for checking if a given point lies in the tangent space at x of the manifold. It should return a boolean indicating whether the test was passed and a reason of failure if check is not passed. You can assume assert_check_point is already passed beforehand Parameters ---------- x torch.Tensor u torch.Tensor atol : float absolute tolerance rtol : relative tolerance Returns ------- bool, str or None check result and the reason of fail if any """ # return True, None raise NotImplementedError def extra_repr(self): return "" def __repr__(self): extra = self.extra_repr() if extra: return self.name + "({}) manifold".format(extra) else: return self.name + " manifold" def unpack_tensor(self, tensor: torch.Tensor) -> torch.Tensor: """ Construct a point on the manifold. This method should help to work with product and compound manifolds. Internally all points on the manifold are stored in an intuitive format. However, there might be cases, when this representation is simpler or more efficient to store in a different way that is hard to use in practice. Parameters ---------- tensor : torch.Tensor Returns ------- torch.Tensor """ return tensor def pack_point(self, *tensors: torch.Tensor) -> torch.Tensor: """ Construct a tensor representation of a manifold point. In case of regular manifolds this will return the same tensor. However, for e.g. Product manifold this function will pack all non-batch dimensions. Parameters ---------- tensors : Tuple[torch.Tensor] Returns ------- torch.Tensor """ if len(tensors) != 1: raise ValueError("1 tensor expected, got {}".format(len(tensors))) return tensors[0] def random(self, *size, dtype=None, device=None, **kwargs) -> torch.Tensor: """ Random sampling on the manifold. The exact implementation depends on manifold and usually does not follow all assumptions about uniform measure, etc. """ raise NotImplementedError def origin( self, *size: Union[int, Tuple[int]], dtype=None, device=None, seed: Optional[int] = 42 ) -> torch.Tensor: """ Create some reasonable point on the manifold in a deterministic way. For some manifolds there may exist e.g. zero vector or some analogy. In case it is possible to define this special point, this point is returned with the desired size. In other case, the returned point is sampled on the manifold in a deterministic way. Parameters ---------- size : Union[int, Tuple[int]] the desired shape device : torch.device the desired device dtype : torch.dtype the desired dtype seed : Optional[int] A parameter controlling deterministic randomness for manifolds that do not provide ``.origin``, but provide ``.random``. (default: 42) Returns ------- torch.Tensor """ if seed is not None: # we promise pseudorandom behaviour but do not want to modify global seed state = torch.random.get_rng_state() torch.random.manual_seed(seed) try: return self.random(*size, dtype=dtype, device=device) finally: torch.random.set_rng_state(state) else: return self.random(*size, dtype=dtype, device=device)

30.485804

117

0.559051

import abc import torch.nn import itertools from typing import Optional, Tuple, Union __all__ = ["Manifold", "ScalingInfo"] class ScalingInfo(object): NotCompatible = object() __slots__ = ["kwargs", "results"] def __init__(self, *results: float, **kwargs: float): self.results = results self.kwargs = kwargs class ScalingStorage(dict): def __call__(self, scaling_info: ScalingInfo): def register(fn): self[fn.__name__] = scaling_info return fn return register def copy(self): return self.__class__(self) class Manifold(torch.nn.Module, metaclass=abc.ABCMeta): __scaling__ = ScalingStorage() name = None ndim = None reversible = None forward = NotImplemented def __init__(self, **kwargs): super().__init__() @property def device(self) -> Optional[torch.device]: p = next(itertools.chain(self.buffers(), self.parameters()), None) if p is not None: return p.device else: return None @property def dtype(self) -> Optional[torch.dtype]: p = next(itertools.chain(self.buffers(), self.parameters()), None) if p is not None: return p.dtype else: return None def check_point( self, x: torch.Tensor, *, explain=False ) -> Union[Tuple[bool, Optional[str]], bool]: ok, reason = self._check_shape(x.shape, "x") if explain: return ok, reason else: return ok def assert_check_point(self, x: torch.Tensor): ok, reason = self._check_shape(x.shape, "x") if not ok: raise ValueError( "`x` seems to be not valid " "tensor for {} manifold.\nerror: {}".format(self.name, reason) ) def check_vector(self, u: torch.Tensor, *, explain=False): ok, reason = self._check_shape(u.shape, "u") if explain: return ok, reason else: return ok def assert_check_vector(self, u: torch.Tensor): ok, reason = self._check_shape(u.shape, "u") if not ok: raise ValueError( "`u` seems to be not valid " "tensor for {} manifold.\nerror: {}".format(self.name, reason) ) def check_point_on_manifold( self, x: torch.Tensor, *, explain=False, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) if explain: return ok, reason else: return ok def assert_check_point_on_manifold(self, x: torch.Tensor, *, atol=1e-5, rtol=1e-5): self.assert_check_point(x) ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) if not ok: raise ValueError( "`x` seems to be a tensor " "not lying on {} manifold.\nerror: {}".format(self.name, reason) ) def check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, ok_point=False, explain=False, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: if not ok_point: ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_shape(u.shape, "u") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) else: ok = True reason = None if ok: ok, reason = self._check_vector_on_tangent(x, u, atol=atol, rtol=rtol) if explain: return ok, reason else: return ok def assert_check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, ok_point=False, atol=1e-5, rtol=1e-5 ): if not ok_point: ok, reason = self._check_shape(x.shape, "x") if ok: ok, reason = self._check_shape(u.shape, "u") if ok: ok, reason = self._check_point_on_manifold(x, atol=atol, rtol=rtol) else: ok = True reason = None if ok: ok, reason = self._check_vector_on_tangent(x, u, atol=atol, rtol=rtol) if not ok: raise ValueError( "`u` seems to be a tensor " "not lying on tangent space to `x` for {} manifold.\nerror: {}".format( self.name, reason ) ) @__scaling__(ScalingInfo(1)) def dist(self, x: torch.Tensor, y: torch.Tensor, *, keepdim=False) -> torch.Tensor: raise NotImplementedError @__scaling__(ScalingInfo(2)) def dist2(self, x: torch.Tensor, y: torch.Tensor, *, keepdim=False) -> torch.Tensor: return self.dist(x, y, keepdim=keepdim) ** 2 @abc.abstractmethod @__scaling__(ScalingInfo(u=-1)) def retr(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abc.abstractmethod @__scaling__(ScalingInfo(u=-1)) def expmap(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: raise NotImplementedError @__scaling__(ScalingInfo(1)) def logmap(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: raise NotImplementedError @__scaling__(ScalingInfo(u=-1)) def expmap_transp( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: y = self.expmap(x, u) v_transp = self.transp(x, y, v) return y, v_transp @__scaling__(ScalingInfo(u=-1)) def retr_transp( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor]: y = self.retr(x, u) v_transp = self.transp(x, y, v) return y, v_transp @__scaling__(ScalingInfo(u=-1)) def transp_follow_retr( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> torch.Tensor: y = self.retr(x, u) return self.transp(x, y, v) @__scaling__(ScalingInfo(u=-1)) def transp_follow_expmap( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor ) -> torch.Tensor: y = self.expmap(x, u) return self.transp(x, y, v) def transp(self, x: torch.Tensor, y: torch.Tensor, v: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abc.abstractmethod def inner( self, x: torch.Tensor, u: torch.Tensor, v=None, *, keepdim=False ) -> torch.Tensor: raise NotImplementedError def component_inner( self, x: torch.Tensor, u: torch.Tensor, v: torch.Tensor = None ) -> torch.Tensor: return self.inner(x, u, v, keepdim=True) def norm(self, x: torch.Tensor, u: torch.Tensor, *, keepdim=False) -> torch.Tensor: return self.inner(x, u, keepdim=keepdim) ** 0.5 @abc.abstractmethod def proju(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abc.abstractmethod def egrad2rgrad(self, x: torch.Tensor, u: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abc.abstractmethod def projx(self, x: torch.Tensor) -> torch.Tensor: raise NotImplementedError def _check_shape( self, shape: Tuple[int], name: str ) -> Union[Tuple[bool, Optional[str]], bool]: ok = len(shape) >= self.ndim if not ok: reason = "'{}' on the {} requires more than {} dim".format( name, self, self.ndim ) else: reason = None return ok, reason def _assert_check_shape(self, shape: Tuple[int], name: str): ok, reason = self._check_shape(shape, name) if not ok: raise ValueError(reason) @abc.abstractmethod def _check_point_on_manifold( self, x: torch.Tensor, *, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: raise NotImplementedError @abc.abstractmethod def _check_vector_on_tangent( self, x: torch.Tensor, u: torch.Tensor, *, atol=1e-5, rtol=1e-5 ) -> Union[Tuple[bool, Optional[str]], bool]: raise NotImplementedError def extra_repr(self): return "" def __repr__(self): extra = self.extra_repr() if extra: return self.name + "({}) manifold".format(extra) else: return self.name + " manifold" def unpack_tensor(self, tensor: torch.Tensor) -> torch.Tensor: return tensor def pack_point(self, *tensors: torch.Tensor) -> torch.Tensor: if len(tensors) != 1: raise ValueError("1 tensor expected, got {}".format(len(tensors))) return tensors[0] def random(self, *size, dtype=None, device=None, **kwargs) -> torch.Tensor: raise NotImplementedError def origin( self, *size: Union[int, Tuple[int]], dtype=None, device=None, seed: Optional[int] = 42 ) -> torch.Tensor: if seed is not None: state = torch.random.get_rng_state() torch.random.manual_seed(seed) try: return self.random(*size, dtype=dtype, device=device) finally: torch.random.set_rng_state(state) else: return self.random(*size, dtype=dtype, device=device)

true

f7200b476d9f3051e09445e9614d3e89cf93fb90

256

py

Python

app/recipe/urls.py

rahulsudhakar10/receipe-api-project

29f205607905bbee347ea9ca505751f4d4cd508a

[ "MIT" ]

null

app/recipe/urls.py

rahulsudhakar10/receipe-api-project

29f205607905bbee347ea9ca505751f4d4cd508a

[ "MIT" ]

null

app/recipe/urls.py

rahulsudhakar10/receipe-api-project

29f205607905bbee347ea9ca505751f4d4cd508a

[ "MIT" ]

null

from django.urls import path, include from rest_framework.routers import DefaultRouter from . import views router = DefaultRouter() router.register('tags', views.TagViewSet) app_name = 'recipe' urlpatterns = [ path('', include(router.urls)) ]

17.066667

48

0.734375

from django.urls import path, include from rest_framework.routers import DefaultRouter from . import views router = DefaultRouter() router.register('tags', views.TagViewSet) app_name = 'recipe' urlpatterns = [ path('', include(router.urls)) ]

true

f7200bcd5453259f229956e2f1faa902e06be465

6,058

py

Python

selfdrive/controls/lib/longitudinal_planner.py

mogorman/openpilot-1

1d19166992149a7dea3536644d67e9e0e2e385fd

[ "MIT" ]

1

2021-06-10T18:00:03.000Z

selfdrive/controls/lib/longitudinal_planner.py

mogorman/openpilot-1

1d19166992149a7dea3536644d67e9e0e2e385fd

[ "MIT" ]

1

2021-05-29T00:57:16.000Z

selfdrive/controls/lib/longitudinal_planner.py

mogorman/openpilot-1

1d19166992149a7dea3536644d67e9e0e2e385fd

[ "MIT" ]

2

2021-11-16T01:49:54.000Z

2022-01-14T04:03:23.000Z

#!/usr/bin/env python3 import math import numpy as np from common.numpy_fast import interp from common.cached_params import CachedParams import cereal.messaging as messaging from common.realtime import DT_MDL from selfdrive.modeld.constants import T_IDXS from selfdrive.config import Conversions as CV from selfdrive.controls.lib.longcontrol import LongCtrlState from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC from selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, CONTROL_N from selfdrive.swaglog import cloudlog LON_MPC_STEP = 0.2 # first step is 0.2s AWARENESS_DECEL = -0.2 # car smoothly decel at .2m/s^2 when user is distracted A_CRUISE_MIN = -1.2 A_CRUISE_MAX_VALS = [1.2, 1.2, 0.8, 0.6] A_CRUISE_MAX_BP = [0., 15., 25., 40.] # Lookup table for turns _A_TOTAL_MAX_V = [1.7, 3.2] _A_TOTAL_MAX_BP = [20., 40.] def get_max_accel(v_ego): return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS) def limit_accel_in_turns(v_ego, angle_steers, a_target, CP): """ This function returns a limited long acceleration allowed, depending on the existing lateral acceleration this should avoid accelerating when losing the target in turns """ a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V) a_y = v_ego**2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase) a_x_allowed = math.sqrt(max(a_total_max**2 - a_y**2, 0.)) return [a_target[0], min(a_target[1], a_x_allowed)] class Planner(): def __init__(self, CP, init_v=0.0, init_a=0.0): self.CP = CP self.mpc = LongitudinalMpc() self.fcw = False self.cachedParams = CachedParams() self.v_desired = init_v self.a_desired = init_a self.alpha = np.exp(-DT_MDL/2.0) self.v_desired_trajectory = np.zeros(CONTROL_N) self.a_desired_trajectory = np.zeros(CONTROL_N) self.j_desired_trajectory = np.zeros(CONTROL_N) def update(self, sm, CP, lateral_planner): v_ego = sm['carState'].vEgo a_ego = sm['carState'].aEgo v_cruise_kph = sm['controlsState'].vCruise v_cruise_kph = min(v_cruise_kph, V_CRUISE_MAX) v_cruise = v_cruise_kph * CV.KPH_TO_MS long_control_state = sm['controlsState'].longControlState force_slow_decel = sm['controlsState'].forceDecel enabled = (long_control_state == LongCtrlState.pid) or (long_control_state == LongCtrlState.stopping) if not enabled or sm['carState'].gasPressed: self.v_desired = v_ego self.a_desired = a_ego # Prevent divergence, smooth in current v_ego self.v_desired = self.alpha * self.v_desired + (1 - self.alpha) * v_ego self.v_desired = max(0.0, self.v_desired) accel_limits = [A_CRUISE_MIN, get_max_accel(v_ego)] if not self.cachedParams.get('jvePilot.settings.slowInCurves', 5000) == "1": accel_limits = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP) if force_slow_decel: # if required so, force a smooth deceleration accel_limits[1] = min(accel_limits[1], AWARENESS_DECEL) accel_limits[0] = min(accel_limits[0], accel_limits[1]) # clip limits, cannot init MPC outside of bounds accel_limits[0] = min(accel_limits[0], self.a_desired + 0.05) accel_limits[1] = max(accel_limits[1], self.a_desired - 0.05) self.mpc.set_accel_limits(accel_limits[0], accel_limits[1]) self.mpc.set_cur_state(self.v_desired, self.a_desired) self.mpc.update(sm['carState'], sm['radarState'], v_cruise) self.v_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC, self.mpc.v_solution) self.a_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC, self.mpc.a_solution) self.j_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC[:-1], self.mpc.j_solution) #TODO counter is only needed because radar is glitchy, remove once radar is gone self.fcw = self.mpc.crash_cnt > 5 if self.fcw: cloudlog.info("FCW triggered") # Interpolate 0.05 seconds and save as starting point for next iteration a_prev = self.a_desired self.a_desired = float(interp(DT_MDL, T_IDXS[:CONTROL_N], self.a_desired_trajectory)) self.v_desired = self.v_desired + DT_MDL * (self.a_desired + a_prev)/2.0 if lateral_planner.lateralPlan and self.cachedParams.get('jvePilot.settings.slowInCurves', 5000) == "1": curvs = list(lateral_planner.lateralPlan.curvatures) if len(curvs): # find the largest curvature in the solution and use that. curv = abs(curvs[-1]) if curv != 0: self.v_desired = float(min(self.v_desired, self.limit_speed_in_curv(sm, curv))) def publish(self, sm, pm): plan_send = messaging.new_message('longitudinalPlan') plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState']) longitudinalPlan = plan_send.longitudinalPlan longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2'] longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2'] longitudinalPlan.speeds = [float(x) for x in self.v_desired_trajectory] longitudinalPlan.accels = [float(x) for x in self.a_desired_trajectory] longitudinalPlan.jerks = [float(x) for x in self.j_desired_trajectory] longitudinalPlan.hasLead = sm['radarState'].leadOne.status longitudinalPlan.longitudinalPlanSource = self.mpc.source longitudinalPlan.fcw = self.fcw pm.send('longitudinalPlan', plan_send) def limit_speed_in_curv(self, sm, curv): v_ego = sm['carState'].vEgo a_y_max = 2.975 - v_ego * 0.0375 # ~1.85 @ 75mph, ~2.6 @ 25mph # drop off drop_off = self.cachedParams.get_float('jvePilot.settings.slowInCurves.speedDropOff', 5000) if drop_off != 2 and a_y_max > 0: a_y_max = np.sqrt(a_y_max) ** drop_off v_curvature = np.sqrt(a_y_max / np.clip(curv, 1e-4, None)) model_speed = np.min(v_curvature) return model_speed * self.cachedParams.get_float('jvePilot.settings.slowInCurves.speedRatio', 5000)

40.657718

108

0.73176

import math import numpy as np from common.numpy_fast import interp from common.cached_params import CachedParams import cereal.messaging as messaging from common.realtime import DT_MDL from selfdrive.modeld.constants import T_IDXS from selfdrive.config import Conversions as CV from selfdrive.controls.lib.longcontrol import LongCtrlState from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc from selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC from selfdrive.controls.lib.drive_helpers import V_CRUISE_MAX, CONTROL_N from selfdrive.swaglog import cloudlog LON_MPC_STEP = 0.2 AWARENESS_DECEL = -0.2 A_CRUISE_MIN = -1.2 A_CRUISE_MAX_VALS = [1.2, 1.2, 0.8, 0.6] A_CRUISE_MAX_BP = [0., 15., 25., 40.] _A_TOTAL_MAX_V = [1.7, 3.2] _A_TOTAL_MAX_BP = [20., 40.] def get_max_accel(v_ego): return interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS) def limit_accel_in_turns(v_ego, angle_steers, a_target, CP): a_total_max = interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V) a_y = v_ego**2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase) a_x_allowed = math.sqrt(max(a_total_max**2 - a_y**2, 0.)) return [a_target[0], min(a_target[1], a_x_allowed)] class Planner(): def __init__(self, CP, init_v=0.0, init_a=0.0): self.CP = CP self.mpc = LongitudinalMpc() self.fcw = False self.cachedParams = CachedParams() self.v_desired = init_v self.a_desired = init_a self.alpha = np.exp(-DT_MDL/2.0) self.v_desired_trajectory = np.zeros(CONTROL_N) self.a_desired_trajectory = np.zeros(CONTROL_N) self.j_desired_trajectory = np.zeros(CONTROL_N) def update(self, sm, CP, lateral_planner): v_ego = sm['carState'].vEgo a_ego = sm['carState'].aEgo v_cruise_kph = sm['controlsState'].vCruise v_cruise_kph = min(v_cruise_kph, V_CRUISE_MAX) v_cruise = v_cruise_kph * CV.KPH_TO_MS long_control_state = sm['controlsState'].longControlState force_slow_decel = sm['controlsState'].forceDecel enabled = (long_control_state == LongCtrlState.pid) or (long_control_state == LongCtrlState.stopping) if not enabled or sm['carState'].gasPressed: self.v_desired = v_ego self.a_desired = a_ego self.v_desired = self.alpha * self.v_desired + (1 - self.alpha) * v_ego self.v_desired = max(0.0, self.v_desired) accel_limits = [A_CRUISE_MIN, get_max_accel(v_ego)] if not self.cachedParams.get('jvePilot.settings.slowInCurves', 5000) == "1": accel_limits = limit_accel_in_turns(v_ego, sm['carState'].steeringAngleDeg, accel_limits, self.CP) if force_slow_decel: accel_limits[1] = min(accel_limits[1], AWARENESS_DECEL) accel_limits[0] = min(accel_limits[0], accel_limits[1]) accel_limits[0] = min(accel_limits[0], self.a_desired + 0.05) accel_limits[1] = max(accel_limits[1], self.a_desired - 0.05) self.mpc.set_accel_limits(accel_limits[0], accel_limits[1]) self.mpc.set_cur_state(self.v_desired, self.a_desired) self.mpc.update(sm['carState'], sm['radarState'], v_cruise) self.v_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC, self.mpc.v_solution) self.a_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC, self.mpc.a_solution) self.j_desired_trajectory = np.interp(T_IDXS[:CONTROL_N], T_IDXS_MPC[:-1], self.mpc.j_solution) self.fcw = self.mpc.crash_cnt > 5 if self.fcw: cloudlog.info("FCW triggered") a_prev = self.a_desired self.a_desired = float(interp(DT_MDL, T_IDXS[:CONTROL_N], self.a_desired_trajectory)) self.v_desired = self.v_desired + DT_MDL * (self.a_desired + a_prev)/2.0 if lateral_planner.lateralPlan and self.cachedParams.get('jvePilot.settings.slowInCurves', 5000) == "1": curvs = list(lateral_planner.lateralPlan.curvatures) if len(curvs): curv = abs(curvs[-1]) if curv != 0: self.v_desired = float(min(self.v_desired, self.limit_speed_in_curv(sm, curv))) def publish(self, sm, pm): plan_send = messaging.new_message('longitudinalPlan') plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState']) longitudinalPlan = plan_send.longitudinalPlan longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2'] longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2'] longitudinalPlan.speeds = [float(x) for x in self.v_desired_trajectory] longitudinalPlan.accels = [float(x) for x in self.a_desired_trajectory] longitudinalPlan.jerks = [float(x) for x in self.j_desired_trajectory] longitudinalPlan.hasLead = sm['radarState'].leadOne.status longitudinalPlan.longitudinalPlanSource = self.mpc.source longitudinalPlan.fcw = self.fcw pm.send('longitudinalPlan', plan_send) def limit_speed_in_curv(self, sm, curv): v_ego = sm['carState'].vEgo a_y_max = 2.975 - v_ego * 0.0375 drop_off = self.cachedParams.get_float('jvePilot.settings.slowInCurves.speedDropOff', 5000) if drop_off != 2 and a_y_max > 0: a_y_max = np.sqrt(a_y_max) ** drop_off v_curvature = np.sqrt(a_y_max / np.clip(curv, 1e-4, None)) model_speed = np.min(v_curvature) return model_speed * self.cachedParams.get_float('jvePilot.settings.slowInCurves.speedRatio', 5000)

true

f7200bced83e516dd159f591c79e95855c52a38f

23,009

py

Python

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/aio/operations/_application_gateway_private_endpoint_connections_operations.py

praveenkuttappan/azure-sdk-for-python

4b79413667b7539750a6c7dde15737013a3d4bd5

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/aio/operations/_application_gateway_private_endpoint_connections_operations.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/network/azure-mgmt-network/azure/mgmt/network/v2020_08_01/aio/operations/_application_gateway_private_endpoint_connections_operations.py

v-xuto/azure-sdk-for-python

9c6296d22094c5ede410bc83749e8df8694ccacc

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ApplicationGatewayPrivateEndpointConnectionsOperations: """ApplicationGatewayPrivateEndpointConnectionsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.network.v2020_08_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _delete_initial( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} # type: ignore async def begin_delete( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> AsyncLROPoller[None]: """Deletes the specified private endpoint connection on application gateway. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param application_gateway_name: The name of the application gateway. :type application_gateway_name: str :param connection_name: The name of the application gateway private endpoint connection. :type connection_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either None or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[None] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, application_gateway_name=application_gateway_name, connection_name=connection_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} # type: ignore async def _update_initial( self, resource_group_name: str, application_gateway_name: str, connection_name: str, parameters: "_models.ApplicationGatewayPrivateEndpointConnection", **kwargs: Any ) -> Optional["_models.ApplicationGatewayPrivateEndpointConnection"]: cls = kwargs.pop('cls', None) # type: ClsType[Optional["_models.ApplicationGatewayPrivateEndpointConnection"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self._update_initial.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(parameters, 'ApplicationGatewayPrivateEndpointConnection') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} # type: ignore async def begin_update( self, resource_group_name: str, application_gateway_name: str, connection_name: str, parameters: "_models.ApplicationGatewayPrivateEndpointConnection", **kwargs: Any ) -> AsyncLROPoller["_models.ApplicationGatewayPrivateEndpointConnection"]: """Updates the specified private endpoint connection on application gateway. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param application_gateway_name: The name of the application gateway. :type application_gateway_name: str :param connection_name: The name of the application gateway private endpoint connection. :type connection_name: str :param parameters: Parameters supplied to update application gateway private endpoint connection operation. :type parameters: ~azure.mgmt.network.v2020_08_01.models.ApplicationGatewayPrivateEndpointConnection :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: By default, your polling method will be AsyncARMPolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy. :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: An instance of AsyncLROPoller that returns either ApplicationGatewayPrivateEndpointConnection or the result of cls(response) :rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2020_08_01.models.ApplicationGatewayPrivateEndpointConnection] :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["_models.ApplicationGatewayPrivateEndpointConnection"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._update_initial( resource_group_name=resource_group_name, application_gateway_name=application_gateway_name, connection_name=connection_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} # type: ignore async def get( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> "_models.ApplicationGatewayPrivateEndpointConnection": """Gets the specified private endpoint connection on application gateway. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param application_gateway_name: The name of the application gateway. :type application_gateway_name: str :param connection_name: The name of the application gateway private endpoint connection. :type connection_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ApplicationGatewayPrivateEndpointConnection, or the result of cls(response) :rtype: ~azure.mgmt.network.v2020_08_01.models.ApplicationGatewayPrivateEndpointConnection :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ApplicationGatewayPrivateEndpointConnection"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} # type: ignore def list( self, resource_group_name: str, application_gateway_name: str, **kwargs: Any ) -> AsyncIterable["_models.ApplicationGatewayPrivateEndpointConnectionListResult"]: """Lists all private endpoint connections on an application gateway. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param application_gateway_name: The name of the application gateway. :type application_gateway_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either ApplicationGatewayPrivateEndpointConnectionListResult or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2020_08_01.models.ApplicationGatewayPrivateEndpointConnectionListResult] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ApplicationGatewayPrivateEndpointConnectionListResult"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" def prepare_request(next_link=None): # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: # Construct URL url = self.list.metadata['url'] # type: ignore path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} # type: Dict[str, Any] request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnectionListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections'} # type: ignore

53.509302

241

0.690556

from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models as _models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ApplicationGatewayPrivateEndpointConnectionsOperations: models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _delete_initial( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> None: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" url = self._delete_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} async def begin_delete( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> AsyncLROPoller[None]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, application_gateway_name=application_gateway_name, connection_name=connection_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'location'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} async def _update_initial( self, resource_group_name: str, application_gateway_name: str, connection_name: str, parameters: "_models.ApplicationGatewayPrivateEndpointConnection", **kwargs: Any ) -> Optional["_models.ApplicationGatewayPrivateEndpointConnection"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self._update_initial.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') body_content_kwargs = {} body_content = self._serialize.body(parameters, 'ApplicationGatewayPrivateEndpointConnection') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} async def begin_update( self, resource_group_name: str, application_gateway_name: str, connection_name: str, parameters: "_models.ApplicationGatewayPrivateEndpointConnection", **kwargs: Any ) -> AsyncLROPoller["_models.ApplicationGatewayPrivateEndpointConnection"]: polling = kwargs.pop('polling', True) cls = kwargs.pop('cls', None) lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) if cont_token is None: raw_result = await self._update_initial( resource_group_name=resource_group_name, application_gateway_name=application_gateway_name, connection_name=connection_name, parameters=parameters, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } if polling is True: polling_method = AsyncARMPolling(lro_delay, lro_options={'final-state-via': 'azure-async-operation'}, path_format_arguments=path_format_arguments, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} async def get( self, resource_group_name: str, application_gateway_name: str, connection_name: str, **kwargs: Any ) -> "_models.ApplicationGatewayPrivateEndpointConnection": cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" url = self.get.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'connectionName': self._serialize.url("connection_name", connection_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnection', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections/{connectionName}'} def list( self, resource_group_name: str, application_gateway_name: str, **kwargs: Any ) -> AsyncIterable["_models.ApplicationGatewayPrivateEndpointConnectionListResult"]: cls = kwargs.pop('cls', None) error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) api_version = "2020-08-01" accept = "application/json" def prepare_request(next_link=None): header_parameters = {} header_parameters['Accept'] = self._serialize.header("accept", accept, 'str') if not next_link: url = self.list.metadata['url'] path_format_arguments = { 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'applicationGatewayName': self._serialize.url("application_gateway_name", application_gateway_name, 'str'), 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), } url = self._client.format_url(url, **path_format_arguments) query_parameters = {} query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') request = self._client.get(url, query_parameters, header_parameters) else: url = next_link query_parameters = {} request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('ApplicationGatewayPrivateEndpointConnectionListResult', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/applicationGateways/{applicationGatewayName}/privateEndpointConnections'}

true

f7200c78df71a4b408145caba6e03ce542b4d9df

3,753

py

Python

tests/system/action/motion_comment_section/test_sort.py

MJJojo97/openslides-backend

af0d1edb0070e352d46f285a1ba0bbe3702d49ae

[ "MIT" ]

null

tests/system/action/motion_comment_section/test_sort.py

MJJojo97/openslides-backend

af0d1edb0070e352d46f285a1ba0bbe3702d49ae

[ "MIT" ]

19

2021-11-22T16:25:54.000Z

2021-11-25T13:38:13.000Z

tests/system/action/motion_comment_section/test_sort.py

MJJojo97/openslides-backend

af0d1edb0070e352d46f285a1ba0bbe3702d49ae

[ "MIT" ]

null

from openslides_backend.permissions.permissions import Permissions from tests.system.action.base import BaseActionTestCase class MotionCommentSectionSortActionTest(BaseActionTestCase): def setUp(self) -> None: super().setUp() self.permission_test_model = { "motion_comment_section/31": { "meeting_id": 1, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 1, "name": "name_blanumop", }, } def test_sort_correct_1(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 222, "name": "name_blanumop", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 200) model_31 = self.get_model("motion_comment_section/31") assert model_31.get("weight") == 2 model_32 = self.get_model("motion_comment_section/32") assert model_32.get("weight") == 1 def test_sort_missing_model(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 400) assert "Id 32 not in db_instances." in response.json["message"] def test_sort_another_section_db(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 222, "name": "name_blanumop", }, "motion_comment_section/33": { "meeting_id": 222, "name": "name_polusiem", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 400) assert "Additional db_instances found." in response.json["message"] def test_sort_no_permissions(self) -> None: self.base_permission_test( self.permission_test_model, "motion_comment_section.sort", {"meeting_id": 1, "motion_comment_section_ids": [32, 31]}, ) def test_sort_permissions(self) -> None: self.base_permission_test( self.permission_test_model, "motion_comment_section.sort", {"meeting_id": 1, "motion_comment_section_ids": [32, 31]}, Permissions.Motion.CAN_MANAGE, )

34.75

75

0.501998

from openslides_backend.permissions.permissions import Permissions from tests.system.action.base import BaseActionTestCase class MotionCommentSectionSortActionTest(BaseActionTestCase): def setUp(self) -> None: super().setUp() self.permission_test_model = { "motion_comment_section/31": { "meeting_id": 1, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 1, "name": "name_blanumop", }, } def test_sort_correct_1(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 222, "name": "name_blanumop", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 200) model_31 = self.get_model("motion_comment_section/31") assert model_31.get("weight") == 2 model_32 = self.get_model("motion_comment_section/32") assert model_32.get("weight") == 1 def test_sort_missing_model(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 400) assert "Id 32 not in db_instances." in response.json["message"] def test_sort_another_section_db(self) -> None: self.set_models( { "meeting/222": { "name": "name_SNLGsvIV", "is_active_in_organization_id": 1, }, "motion_comment_section/31": { "meeting_id": 222, "name": "name_loisueb", }, "motion_comment_section/32": { "meeting_id": 222, "name": "name_blanumop", }, "motion_comment_section/33": { "meeting_id": 222, "name": "name_polusiem", }, } ) response = self.request( "motion_comment_section.sort", {"meeting_id": 222, "motion_comment_section_ids": [32, 31]}, ) self.assert_status_code(response, 400) assert "Additional db_instances found." in response.json["message"] def test_sort_no_permissions(self) -> None: self.base_permission_test( self.permission_test_model, "motion_comment_section.sort", {"meeting_id": 1, "motion_comment_section_ids": [32, 31]}, ) def test_sort_permissions(self) -> None: self.base_permission_test( self.permission_test_model, "motion_comment_section.sort", {"meeting_id": 1, "motion_comment_section_ids": [32, 31]}, Permissions.Motion.CAN_MANAGE, )

true

f7200c97efca195813bca2dc2b1dffe77ce84bea

4,104

py

Python

test/functional/feature_includeconf.py

kaboela/litecoinz

b793b04a717416726a7b1013b21b07fb35dbc4a2

[ "MIT" ]

8

2020-06-05T16:30:36.000Z

2021-09-28T08:39:52.000Z

test/functional/feature_includeconf.py

kaboela/litecoinz

b793b04a717416726a7b1013b21b07fb35dbc4a2

[ "MIT" ]

8

2020-04-04T11:24:26.000Z

2021-05-09T18:53:53.000Z

test/functional/feature_includeconf.py

kaboela/litecoinz

b793b04a717416726a7b1013b21b07fb35dbc4a2

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Tests the includeconf argument Verify that: 1. adding includeconf to the configuration file causes the includeconf file to be loaded in the correct order. 2. includeconf cannot be used as a command line argument. 3. includeconf cannot be used recursively (ie includeconf can only be used from the base config file). 4. multiple includeconf arguments can be specified in the main config file. """ import os from test_framework.test_framework import BitcoinTestFramework class IncludeConfTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = False self.num_nodes = 1 def setup_chain(self): super().setup_chain() # Create additional config files # - tmpdir/node0/relative.conf with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "w", encoding="utf8") as f: f.write("uacomment=relative\n") # - tmpdir/node0/relative2.conf with open(os.path.join(self.options.tmpdir, "node0", "relative2.conf"), "w", encoding="utf8") as f: f.write("uacomment=relative2\n") with open(os.path.join(self.options.tmpdir, "node0", "litecoinz.conf"), "a", encoding='utf8') as f: f.write("uacomment=main\nincludeconf=relative.conf\n") def run_test(self): self.log.info("-includeconf works from config file. subversion should end with 'main; relative)/'") subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative)/") self.log.info("-includeconf cannot be used as command-line arg") self.stop_node(0) self.nodes[0].assert_start_raises_init_error(extra_args=["-includeconf=relative2.conf"], expected_msg="Error: Error parsing command line arguments: -includeconf cannot be used from commandline; -includeconf=relative2.conf") self.log.info("-includeconf cannot be used recursively. subversion should end with 'main; relative)/'") with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "a", encoding="utf8") as f: f.write("includeconf=relative2.conf\n") self.start_node(0) subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative)/") self.stop_node(0, expected_stderr="warning: -includeconf cannot be used from included files; ignoring -includeconf=relative2.conf") self.log.info("-includeconf cannot contain invalid arg") # Commented out as long as we ignore invalid arguments in configuration files #with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "w", encoding="utf8") as f: # f.write("foo=bar\n") #self.nodes[0].assert_start_raises_init_error(expected_msg="Error: Error reading configuration file: Invalid configuration value foo") self.log.info("-includeconf cannot be invalid path") os.remove(os.path.join(self.options.tmpdir, "node0", "relative.conf")) self.nodes[0].assert_start_raises_init_error(expected_msg="Error: Error reading configuration file: Failed to include configuration file relative.conf") self.log.info("multiple -includeconf args can be used from the base config file. subversion should end with 'main; relative; relative2)/'") with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "w", encoding="utf8") as f: # Restore initial file contents f.write("uacomment=relative\n") with open(os.path.join(self.options.tmpdir, "node0", "litecoinz.conf"), "a", encoding='utf8') as f: f.write("includeconf=relative2.conf\n") self.start_node(0) subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative; relative2)/") if __name__ == '__main__': IncludeConfTest().main()

49.445783

231

0.694932

import os from test_framework.test_framework import BitcoinTestFramework class IncludeConfTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = False self.num_nodes = 1 def setup_chain(self): super().setup_chain() with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "w", encoding="utf8") as f: f.write("uacomment=relative\n") with open(os.path.join(self.options.tmpdir, "node0", "relative2.conf"), "w", encoding="utf8") as f: f.write("uacomment=relative2\n") with open(os.path.join(self.options.tmpdir, "node0", "litecoinz.conf"), "a", encoding='utf8') as f: f.write("uacomment=main\nincludeconf=relative.conf\n") def run_test(self): self.log.info("-includeconf works from config file. subversion should end with 'main; relative)/'") subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative)/") self.log.info("-includeconf cannot be used as command-line arg") self.stop_node(0) self.nodes[0].assert_start_raises_init_error(extra_args=["-includeconf=relative2.conf"], expected_msg="Error: Error parsing command line arguments: -includeconf cannot be used from commandline; -includeconf=relative2.conf") self.log.info("-includeconf cannot be used recursively. subversion should end with 'main; relative)/'") with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "a", encoding="utf8") as f: f.write("includeconf=relative2.conf\n") self.start_node(0) subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative)/") self.stop_node(0, expected_stderr="warning: -includeconf cannot be used from included files; ignoring -includeconf=relative2.conf") self.log.info("-includeconf cannot contain invalid arg") self.log.info("-includeconf cannot be invalid path") os.remove(os.path.join(self.options.tmpdir, "node0", "relative.conf")) self.nodes[0].assert_start_raises_init_error(expected_msg="Error: Error reading configuration file: Failed to include configuration file relative.conf") self.log.info("multiple -includeconf args can be used from the base config file. subversion should end with 'main; relative; relative2)/'") with open(os.path.join(self.options.tmpdir, "node0", "relative.conf"), "w", encoding="utf8") as f: f.write("uacomment=relative\n") with open(os.path.join(self.options.tmpdir, "node0", "litecoinz.conf"), "a", encoding='utf8') as f: f.write("includeconf=relative2.conf\n") self.start_node(0) subversion = self.nodes[0].getnetworkinfo()["subversion"] assert subversion.endswith("main; relative; relative2)/") if __name__ == '__main__': IncludeConfTest().main()

true

f72010304316b8649123167ea9d4b94f50b6f1f1

25,588

py

Python

plaidcloud/utilities/tests/test_remote_dimension.py

PlaidCloud/public-utilities

663e94f2657a02a4249177945e0880bb968c3439

[ "Apache-2.0" ]

null

plaidcloud/utilities/tests/test_remote_dimension.py

PlaidCloud/public-utilities

663e94f2657a02a4249177945e0880bb968c3439

[ "Apache-2.0" ]

48

2020-10-30T10:15:39.000Z

2022-03-25T17:23:57.000Z

plaidcloud/utilities/tests/test_remote_dimension.py

PlaidCloud/plaid-utilities

1031cb87580bbe110f56455925e483a0ae177fe1

[ "Apache-2.0" ]

null

#!/usr/bin/env python # coding=utf-8 from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import filecmp import os import unittest from unittest import TestCase import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from plaidcloud.utilities.connect import create_connection from plaidcloud.utilities.remote.dimension import Dimensions from plaidcloud.utilities.remote.dimension import MAIN from plaidcloud.utilities.remote.dimension import ROOT __author__ = 'Dave Parsons' __copyright__ = 'Copyright 2010-2020, Tartan Solutions, Inc' __credits__ = ['Dave Parsons'] __license__ = 'Proprietary' __maintainer__ = 'Dave Parsons' __email__ = 'dave.parsons@tartansolutions.com' # Folders for comparison BASELINE = './dim_baseline/' FOLDER = './dim_current/' conn = create_connection(verify_ssl=False) class TestDimension(TestCase): """Test Redis Dimension code""" def assertFileEqual(self, file1, file2, **kwargs): return self.assertTrue(filecmp.cmp(file1, file2, shallow=False)) def assertFrameEqual(self, df1, df2, **kwargs): return assert_frame_equal(df1, df2, check_names=True, check_like=True, **kwargs) def setUp(self): if not os.path.exists(BASELINE): os.makedirs(BASELINE) self.periods = 'periods_rpc_test' self.dims = Dimensions(conn=conn) self.dim = self.dims.get_dimension(name=self.periods, replace=False) return def test_001_load_hierarchy_main(self): df_main = pd.DataFrame( [ [ROOT, 'Year'], ['Year', 'Q1'], ['Year', 'Q2'], ['Year', 'Q3'], ['Year', 'Q4'], ['Q1', 'January'], ['Q1', 'February'], ['Q1', 'March'], ['Q2', 'April'], ['Q2', 'May'], ['Q2', 'June'], ['Q3', 'July'], ['Q3', 'August'], ['Q3', 'September'], ['Q4', 'October'], ['Q4', 'November'], ['Q4', 'December'], ], columns=['ParentName', 'ChildName'] ) # Clear down the dimension and reload self.dim.clear() # main hierarchy df_results = self.dim.load_hierarchy_from_dataframe(df_main, 'ParentName', 'ChildName') df_results.to_csv(f'{FOLDER}df_main_load.csv', index=False) # Create a backup file to allow reloading in tests data = self.dims.backup(self.periods) with open(f'{FOLDER}periods.yaml', 'w') as file: file.write(data) self.assertFileEqual(f'{FOLDER}df_main_load.csv', f'{BASELINE}df_main_load.csv') return def test_002_save_hierarchy_main(self): # main hierarchy df = self.dim.save_hierarchy_to_dataframe(MAIN) df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_main_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_main_hierarchy.csv', f'{BASELINE}df_main_hierarchy.csv') return def test_003_load_hierarchy_halves(self): df_halves = pd.DataFrame( [ [ROOT, 'H1', '~', 'halves'], [ROOT, 'H2', '~', 'halves'], ['H1', 'Q1', '+', 'halves'], ['H1', 'Q2', '+', 'halves'], ['H2', 'Q3', '+', 'halves'], ['H2', 'Q4', '+', 'halves'], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) # halves hierarchy df_results = self.dim.load_hierarchy_from_dataframe(df_halves, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_halves_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_halves_load.csv', f'{BASELINE}df_halves_load.csv') return def test_004_save_hierarchy_halves(self): # halves hierarchy df = self.dim.save_hierarchy_to_dataframe('halves') df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_halves_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_halves_hierarchy.csv', f'{BASELINE}df_halves_hierarchy.csv') return def test_005_load_hierarchy_financial(self): df_financial = pd.DataFrame( [ [ROOT, 'YTD', '+', 'financial'], [ROOT, 'YTG', '+', 'financial'], ['YTD', 'January', '+', 'financial'], ['YTD', 'February', '+', 'financial'], ['YTD', 'March', '+', 'financial'], ['YTD', 'April', '+', 'financial'], ['YTG', 'May', '-', 'financial'], ['YTG', 'June', '-', 'financial'], ['YTG', 'July', '-', 'financial'], ['YTG', 'August', '-', 'financial'], ['YTG', 'September', '-', 'financial'], ['YTG', 'October', '-', 'financial'], ['YTG', 'November', '-', 'financial'], ['YTG', 'December', '-', 'financial'], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) # financial hierarchy df_results = self.dim.load_hierarchy_from_dataframe(df_financial, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_financial_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_financial_load.csv', f'{BASELINE}df_financial_load.csv') return def test_006_save_hierarchy_financial(self): # financial hierarchy df = self.dim.save_hierarchy_to_dataframe('financial') df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_financial_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_financial_hierarchy.csv', f'{BASELINE}df_financial_hierarchy.csv') return def test_007_load_hierarchy_errors(self): # This dataframe includes specific errors so check out the results dataframe df_test = pd.DataFrame( [ ['', '', '+', 'main'], [' ', ' ', '+', 'main'], ['Q5', '', '+', 'main'], [np.NaN, np.NaN, '+', 'main'], [None, None, '+', 'main'], ['None', 'None', '+', 'main'], ['Q5', 'Q5', '+', 'main'], ['Q5', ROOT, '+', 'main'], ['Q5', 'Donk:tober', '+', 'main'], ['Donk:tober', 'Janusday', '+', 'main'], ['Year', 'Q5', '+', 'main'], ['Year', 'Q5', '+', 'main'], ['Q4', 'Badtober', '+', 'halves'], ['Q6', 'Craptober', '+', ''], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) df_results = self.dim.load_hierarchy_from_dataframe(df_test, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_complex_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_complex_load.csv', f'{BASELINE}df_complex_load.csv') return def test_008_load_save_aliases(self): df_aliases = pd.DataFrame( [ ['Trimestre 1', 'French', 'Q1'], ['Trimestre 2', 'French', 'Q2'], ['Trimestre 3', 'French', 'Q3'], ['Trimestre 4', 'French', 'Q4'], ['Janvier', 'French', 'January'], ['Fevier', 'French', 'February'], ['Mars', 'French', 'March'], ['Avril', 'French', 'April'], ['Mai', 'French', 'May'], ['Juin', 'French', 'June'], ['Julliet', 'French', 'July'], ['Aout', 'French', 'August'], ['Septembre', 'French', 'September'], ['Octobre', 'French', 'October'], ['Novembre', 'French', 'November'], ['Decembre', 'French', 'December'], ['Haneri 1', 'Welsh', 'H1'], ['Haneri 2', 'Welsh', 'H2'], ['Ionawr', 'Welsh', 'January'], ['Chwefror', 'Welsh', 'February'], ['Mawrth', 'Welsh', 'March'], ['Ebrill', 'Welsh', 'April'], ['Mai', 'Welsh', 'May'], ['Mehefin', 'Welsh', 'June'], ['Gorffennaf', 'Welsh', 'July'], ['Awst', 'Welsh', 'August'], ['Medi', 'Welsh', 'September'], ['Hydref', 'Welsh', 'October'], ['Tachwedd', 'Welsh', 'November'], ['Rhagfyr', 'Welsh', 'December'], ['Январь', 'Russian', 'January'], ['Февраль', 'Russian', 'February'], ['Март', 'Russian', 'March'], ['Апрель', 'Russian', 'April'], ['Май', 'Russian', 'May'], ['Июнь', 'Russian', 'June'], ['Июль', 'Russian', 'July'], ['Август', 'Russian', 'August'], ['Сентябрь', 'Russian', 'September'], ['Октябрь', 'Russian', 'October'], ['Ноябрь', 'Russian', 'November'], ['Декабрь', 'Russian', 'December'], ['일월', 'Korean', 'January'], ['이월', 'Korean', 'February'], ['삼월', 'Korean', 'March'], ['사월', 'Korean', 'April'], ['오월', 'Korean', 'May'], ['유월', 'Korean', 'June'], ['칠월', 'Korean', 'July'], ['팔월', 'Korean', 'August'], ['구월', 'Korean', 'September'], ['시월', 'Korean', 'October'], ['십일월', 'Korean', 'November'], ['십이월', 'Korean', 'December'], ['☃️', 'Emoji', 'January'], ['💘', 'Emoji', 'February'], ['☘️', 'Emoji', 'March'], ['☔', 'Emoji', 'April'], ['🌺', 'Emoji', 'May'], ['🌞', 'Emoji', 'June'], ['🍦', 'Emoji', 'July'], ['🏖️', 'Emoji', 'August'], ['🍎', 'Emoji', 'September'], ['🎃', 'Emoji', 'October'], ['🍂', 'Emoji', 'November'], ['🎅', 'Emoji', 'December'], ], columns=['AliasValue', 'AliasName', 'NodeName'] ) # Aliases self.dim.load_aliases_from_dataframe(df_aliases, 'NodeName', 'AliasName', 'AliasValue') df = self.dim.save_aliases_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_aliases.csv', f'{BASELINE}df_aliases.csv') return def test_009_load_save_properties(self): df_properties = pd.DataFrame( [ ['Magenta', 'Colour', ROOT], ['Purple', 'Colour', 'Year'], ['Red', 'Colour', 'Q1'], ['Orange', 'Colour', 'Q2'], ['Green', 'Colour', 'April'], ['Green', 'Colour', 'May'], ['Blue', 'Colour', 'July'], ['Blue', 'Colour', 'August'], ['Blue', 'Colour', 'September'], ['White', 'Colour', 'Q4'], ['Red', 'Colour', 'October'], ['Green', 'Colour', 'November'], ['Red', 'Colour', 'December'], ['Winter', 'Season', 'Q1'], ['Spring', 'Season', 'Q2'], ['Summer', 'Season', 'Q3'], ['Autumn', 'Season', 'Q4'], ], columns=['PropertyValue', 'PropertyName', 'NodeName'] ) # Properties self.dim.load_properties_from_dataframe(df_properties, 'NodeName', 'PropertyName', 'PropertyValue') df = self.dim.save_properties_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_properties.csv', index=False) self.assertFileEqual(f'{FOLDER}df_properties.csv', f'{BASELINE}df_properties.csv') return def test_010_load_save_values(self): df_values = pd.DataFrame( [ [-10.0, 'Costs', 'January'], [-100.0, 'Costs', 'February'], [-1000.0, 'Costs', 'March'], [-20.0, 'Costs', 'April'], [-200.0, 'Costs', 'May'], [-2000.0, 'Costs', 'June'], [-30.0, 'Costs', 'July'], [-300.0, 'Costs', 'August'], [-3000.0, 'Costs', 'September'], [-40.0, 'Costs', 'October'], [-400.0, 'Costs', 'November'], [-4000.0, 'Costs', 'December'], [10.0, 'Profit', 'January'], [100.0, 'Profit', 'February'], [1000.0, 'Profit', 'March'], [20.0, 'Profit', 'April'], [200.0, 'Profit', 'May'], [2000.0, 'Profit', 'June'], [30.0, 'Profit', 'July'], [300.0, 'Profit', 'August'], [3000.0, 'Profit', 'September'], [40.0, 'Profit', 'October'], [400.0, 'Profit', 'November'], [4000.0, 'Profit', 'December'], ], columns=['Value', 'ValueName', 'NodeName'] ) # Values self.dim.load_values_from_dataframe(df_values, 'NodeName', 'ValueName', 'Value') df = self.dim.save_values_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_values.csv', index=False) self.assertFileEqual(f'{FOLDER}df_values.csv', f'{BASELINE}df_values.csv') return def test_011_get_hierarchy_dataframe(self): df = self.dim.get_hierarchy_dataframe(hierarchy=MAIN) df = df.reindex(columns=sorted(df.columns)) df.to_csv(f'{FOLDER}df_get_hierarchy_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_hierarchy_main.csv', f'{BASELINE}df_get_hierarchy_main.csv') return def test_012_get_aliases_dataframe(self): df = self.dim.get_aliases_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_aliases.csv', f'{BASELINE}df_get_aliases.csv') return def test_013_get_attributes_dataframe(self): df = self.dim.get_attributes_dataframe() df.drop(labels='index', axis=1, inplace=True) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_attributes.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_attributes.csv', f'{BASELINE}df_get_attributes.csv') return def test_014_get_consolidation_dataframe(self): df = self.dim.get_consolidation_dataframe('Costs', hierarchy=MAIN) df.to_csv(f'{FOLDER}df_get_consolidation_costs_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_consolidation_costs_main.csv', f'{BASELINE}df_get_consolidation_costs_main.csv') return def test_015_get_properties_dataframe(self): df = self.dim.get_properties_dataframe() df.drop(labels='index', axis=1, inplace=True) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_properties.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_properties.csv', f'{BASELINE}df_get_properties.csv') return def test_016_get_values_dataframe(self): df = self.dim.get_values_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_values.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_values.csv', f'{BASELINE}df_get_values.csv') return def test_017_get_hierarchy_table(self): df = self.dim.hierarchy_table(hierarchy=MAIN) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_hierarchy_table_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_hierarchy_table_main.csv', f'{BASELINE}df_get_hierarchy_table_main.csv') return def test_018_get_all_leaves(self): expected = ['April', 'August', 'December', 'February', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'September'] nodes = sorted(self.dim.get_all_leaves(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_019_get_all_nodes(self): expected = ['!!root!!', 'April', 'August', 'December', 'Donk-tober', 'February', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'September', 'Year'] nodes = sorted(self.dim.get_all_nodes(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_020_get_all_parents(self): expected = ['!!root!!', 'Donk-tober', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Year'] nodes = sorted(self.dim.get_all_parents(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_021_get_ancestors(self): expected = [[0, 'February'], [1, 'Q1'], [2, 'Year'], [3, '!!root!!']] nodes = self.dim.get_ancestors('February', hierarchy=MAIN) return self.assertListEqual(expected, nodes) def test_022_get_ancestor_at_generation(self): expected = 'Year' node = self.dim.get_ancestor_at_generation('February', 1, hierarchy=MAIN) return self.assertEqual(expected, node) def test_023_get_ancestor_at_level(self): expected = 'Year' node = self.dim.get_ancestor_at_level('February', 2, hierarchy=MAIN) return self.assertEqual(expected, node) def test_024_get_bottom(self): expected = 'March' node = self.dim.get_bottom('Q1', hierarchy=MAIN) return self.assertEqual(expected, node) def test_025_get_top(self): expected = 'January' node = self.dim.get_top('Q1', hierarchy=MAIN) return self.assertEqual(expected, node) def test_026_get_down(self): expected = 'March' node = self.dim.get_down('Q1', 'February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_027_get_up(self): expected = 'January' node = self.dim.get_up('Q1', 'February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_028_get_children(self): expected = ['January', 'February', 'March'] nodes = self.dim.get_children('Q1', hierarchy=MAIN) return self.assertListEqual(expected, nodes) def test_029_get_children_count(self): expected = 3 count = self.dim.get_children_count('Q1', hierarchy=MAIN) return self.assertEqual(expected, count) def test_030_get_generation(self): expected = 2 count = self.dim.get_generation('Q1', hierarchy=MAIN) return self.assertEqual(expected, count) def test_031_get_grandparent(self): expected = 'Year' node = self.dim.get_grandparent('February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_032_get_leaves(self): expected = [[2, 'January'], [2, 'February'], [2, 'March'], [2, 'April'], [2, 'May'], [2, 'June'], [2, 'July'], [2, 'August'], [2, 'September'], [2, 'October'], [2, 'November'], [2, 'December'], [3, 'Janusday']] nodes = self.dim.get_leaves('Year', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_033_get_leaves_at_generation(self): expected = [[2, 'January'], [2, 'February'], [2, 'March'], [2, 'April'], [2, 'May'], [2, 'June'], [2, 'July'], [2, 'August'], [2, 'September'], [2, 'October'], [2, 'November'], [2, 'December']] nodes = self.dim.get_leaves_at_generation('Year', 2, hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_034_get_leaves_at_level(self): expected = [[3, 'January'], [3, 'February'], [3, 'March'], [3, 'April'], [3, 'May'], [3, 'June'], [3, 'July'], [3, 'August'], [3, 'September'], [3, 'October'], [3, 'November'], [3, 'December']] nodes = self.dim.get_leaves_at_level('February', 0, hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_035_get_parent(self): expected = 'Q1' nodes = self.dim.get_parent('February', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_036_get_parents(self): expected = [['financial', 'halves', 'main'], ['YTD', 'Q1', 'Q1']] nodes = self.dim.get_parents('February') return self.assertEqual(expected, nodes) def test_037_get_siblings(self): expected = ['January', 'February', 'March'] nodes = self.dim.get_siblings('February', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_038_get_difference(self): expected = sorted(['Janusday', 'Year', 'Q5', 'Donk-tober']) nodes = sorted(self.dim.get_difference(['halves'])) return self.assertEqual(expected, nodes) def test_039_get_intersection(self): expected = sorted(['!!root!!', 'April', 'August', 'December', 'February', 'January', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'September']) nodes = sorted(self.dim.get_intersection(['halves'])) return self.assertEqual(expected, nodes) def test_040_get_union(self): expected = sorted(['!!root!!', 'April', 'August', 'December', 'Donk-tober', 'February', 'H1', 'H2', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'September', 'Year']) nodes = sorted(self.dim.get_union(['halves'])) return self.assertEqual(expected, nodes) def test_041_add_node_to_alt(self): expected = 'H2' self.dim.add_node('H2', 'Q5', '+', hierarchy='halves', after='Q4') node = self.dim.get_parent('Q5', hierarchy='halves') return self.assertEqual(expected, node) def test_042_move_node_in_alt(self): expected = 'H1' self.dim.move_node('Q5', 'H1', hierarchy='halves', before='Q2') node = self.dim.get_parent('Q5', hierarchy='halves') return self.assertEqual(expected, node) def test_043_rename_node(self): expected = 'Q5' self.dim.rename_node('Donk-tober', 'Davetober') node = self.dim.get_parent('Davetober', hierarchy=MAIN) return self.assertEqual(expected, node) def test_044_delete_node(self): self.dim.delete_node('Year', 'Q5', hierarchy=MAIN) node = self.dim.node_exists('Q5') return self.assertFalse(node) def test_045_default_alias_dataframe(self): self.dim.set_default_aliases(primary='Welsh', secondary='French') df = self.dim.get_aliases_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_default_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_default_aliases.csv', f'{BASELINE}df_get_default_aliases.csv') pass def tearDown(self): self.dim = None self.dims = None

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from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import filecmp import os import unittest from unittest import TestCase import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from plaidcloud.utilities.connect import create_connection from plaidcloud.utilities.remote.dimension import Dimensions from plaidcloud.utilities.remote.dimension import MAIN from plaidcloud.utilities.remote.dimension import ROOT __author__ = 'Dave Parsons' __copyright__ = 'Copyright 2010-2020, Tartan Solutions, Inc' __credits__ = ['Dave Parsons'] __license__ = 'Proprietary' __maintainer__ = 'Dave Parsons' __email__ = 'dave.parsons@tartansolutions.com' BASELINE = './dim_baseline/' FOLDER = './dim_current/' conn = create_connection(verify_ssl=False) class TestDimension(TestCase): def assertFileEqual(self, file1, file2, **kwargs): return self.assertTrue(filecmp.cmp(file1, file2, shallow=False)) def assertFrameEqual(self, df1, df2, **kwargs): return assert_frame_equal(df1, df2, check_names=True, check_like=True, **kwargs) def setUp(self): if not os.path.exists(BASELINE): os.makedirs(BASELINE) self.periods = 'periods_rpc_test' self.dims = Dimensions(conn=conn) self.dim = self.dims.get_dimension(name=self.periods, replace=False) return def test_001_load_hierarchy_main(self): df_main = pd.DataFrame( [ [ROOT, 'Year'], ['Year', 'Q1'], ['Year', 'Q2'], ['Year', 'Q3'], ['Year', 'Q4'], ['Q1', 'January'], ['Q1', 'February'], ['Q1', 'March'], ['Q2', 'April'], ['Q2', 'May'], ['Q2', 'June'], ['Q3', 'July'], ['Q3', 'August'], ['Q3', 'September'], ['Q4', 'October'], ['Q4', 'November'], ['Q4', 'December'], ], columns=['ParentName', 'ChildName'] ) self.dim.clear() df_results = self.dim.load_hierarchy_from_dataframe(df_main, 'ParentName', 'ChildName') df_results.to_csv(f'{FOLDER}df_main_load.csv', index=False) data = self.dims.backup(self.periods) with open(f'{FOLDER}periods.yaml', 'w') as file: file.write(data) self.assertFileEqual(f'{FOLDER}df_main_load.csv', f'{BASELINE}df_main_load.csv') return def test_002_save_hierarchy_main(self): df = self.dim.save_hierarchy_to_dataframe(MAIN) df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_main_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_main_hierarchy.csv', f'{BASELINE}df_main_hierarchy.csv') return def test_003_load_hierarchy_halves(self): df_halves = pd.DataFrame( [ [ROOT, 'H1', '~', 'halves'], [ROOT, 'H2', '~', 'halves'], ['H1', 'Q1', '+', 'halves'], ['H1', 'Q2', '+', 'halves'], ['H2', 'Q3', '+', 'halves'], ['H2', 'Q4', '+', 'halves'], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) df_results = self.dim.load_hierarchy_from_dataframe(df_halves, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_halves_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_halves_load.csv', f'{BASELINE}df_halves_load.csv') return def test_004_save_hierarchy_halves(self): df = self.dim.save_hierarchy_to_dataframe('halves') df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_halves_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_halves_hierarchy.csv', f'{BASELINE}df_halves_hierarchy.csv') return def test_005_load_hierarchy_financial(self): df_financial = pd.DataFrame( [ [ROOT, 'YTD', '+', 'financial'], [ROOT, 'YTG', '+', 'financial'], ['YTD', 'January', '+', 'financial'], ['YTD', 'February', '+', 'financial'], ['YTD', 'March', '+', 'financial'], ['YTD', 'April', '+', 'financial'], ['YTG', 'May', '-', 'financial'], ['YTG', 'June', '-', 'financial'], ['YTG', 'July', '-', 'financial'], ['YTG', 'August', '-', 'financial'], ['YTG', 'September', '-', 'financial'], ['YTG', 'October', '-', 'financial'], ['YTG', 'November', '-', 'financial'], ['YTG', 'December', '-', 'financial'], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) df_results = self.dim.load_hierarchy_from_dataframe(df_financial, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_financial_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_financial_load.csv', f'{BASELINE}df_financial_load.csv') return def test_006_save_hierarchy_financial(self): df = self.dim.save_hierarchy_to_dataframe('financial') df.drop(labels='index', axis=1, inplace=True) df.to_csv(f'{FOLDER}df_financial_hierarchy.csv', index=False) self.assertFileEqual(f'{FOLDER}df_financial_hierarchy.csv', f'{BASELINE}df_financial_hierarchy.csv') return def test_007_load_hierarchy_errors(self): df_test = pd.DataFrame( [ ['', '', '+', 'main'], [' ', ' ', '+', 'main'], ['Q5', '', '+', 'main'], [np.NaN, np.NaN, '+', 'main'], [None, None, '+', 'main'], ['None', 'None', '+', 'main'], ['Q5', 'Q5', '+', 'main'], ['Q5', ROOT, '+', 'main'], ['Q5', 'Donk:tober', '+', 'main'], ['Donk:tober', 'Janusday', '+', 'main'], ['Year', 'Q5', '+', 'main'], ['Year', 'Q5', '+', 'main'], ['Q4', 'Badtober', '+', 'halves'], ['Q6', 'Craptober', '+', ''], ], columns=['ParentName', 'ChildName', 'ConsolidationType', 'Hierarchy'] ) df_results = self.dim.load_hierarchy_from_dataframe(df_test, 'ParentName', 'ChildName', 'ConsolidationType', hierarchy='Hierarchy') df_results.to_csv(f'{FOLDER}df_complex_load.csv', index=False) self.assertFileEqual(f'{FOLDER}df_complex_load.csv', f'{BASELINE}df_complex_load.csv') return def test_008_load_save_aliases(self): df_aliases = pd.DataFrame( [ ['Trimestre 1', 'French', 'Q1'], ['Trimestre 2', 'French', 'Q2'], ['Trimestre 3', 'French', 'Q3'], ['Trimestre 4', 'French', 'Q4'], ['Janvier', 'French', 'January'], ['Fevier', 'French', 'February'], ['Mars', 'French', 'March'], ['Avril', 'French', 'April'], ['Mai', 'French', 'May'], ['Juin', 'French', 'June'], ['Julliet', 'French', 'July'], ['Aout', 'French', 'August'], ['Septembre', 'French', 'September'], ['Octobre', 'French', 'October'], ['Novembre', 'French', 'November'], ['Decembre', 'French', 'December'], ['Haneri 1', 'Welsh', 'H1'], ['Haneri 2', 'Welsh', 'H2'], ['Ionawr', 'Welsh', 'January'], ['Chwefror', 'Welsh', 'February'], ['Mawrth', 'Welsh', 'March'], ['Ebrill', 'Welsh', 'April'], ['Mai', 'Welsh', 'May'], ['Mehefin', 'Welsh', 'June'], ['Gorffennaf', 'Welsh', 'July'], ['Awst', 'Welsh', 'August'], ['Medi', 'Welsh', 'September'], ['Hydref', 'Welsh', 'October'], ['Tachwedd', 'Welsh', 'November'], ['Rhagfyr', 'Welsh', 'December'], ['Январь', 'Russian', 'January'], ['Февраль', 'Russian', 'February'], ['Март', 'Russian', 'March'], ['Апрель', 'Russian', 'April'], ['Май', 'Russian', 'May'], ['Июнь', 'Russian', 'June'], ['Июль', 'Russian', 'July'], ['Август', 'Russian', 'August'], ['Сентябрь', 'Russian', 'September'], ['Октябрь', 'Russian', 'October'], ['Ноябрь', 'Russian', 'November'], ['Декабрь', 'Russian', 'December'], ['일월', 'Korean', 'January'], ['이월', 'Korean', 'February'], ['삼월', 'Korean', 'March'], ['사월', 'Korean', 'April'], ['오월', 'Korean', 'May'], ['유월', 'Korean', 'June'], ['칠월', 'Korean', 'July'], ['팔월', 'Korean', 'August'], ['구월', 'Korean', 'September'], ['시월', 'Korean', 'October'], ['십일월', 'Korean', 'November'], ['십이월', 'Korean', 'December'], ['☃️', 'Emoji', 'January'], ['💘', 'Emoji', 'February'], ['☘️', 'Emoji', 'March'], ['☔', 'Emoji', 'April'], ['🌺', 'Emoji', 'May'], ['🌞', 'Emoji', 'June'], ['🍦', 'Emoji', 'July'], ['🏖️', 'Emoji', 'August'], ['🍎', 'Emoji', 'September'], ['🎃', 'Emoji', 'October'], ['🍂', 'Emoji', 'November'], ['🎅', 'Emoji', 'December'], ], columns=['AliasValue', 'AliasName', 'NodeName'] ) self.dim.load_aliases_from_dataframe(df_aliases, 'NodeName', 'AliasName', 'AliasValue') df = self.dim.save_aliases_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_aliases.csv', f'{BASELINE}df_aliases.csv') return def test_009_load_save_properties(self): df_properties = pd.DataFrame( [ ['Magenta', 'Colour', ROOT], ['Purple', 'Colour', 'Year'], ['Red', 'Colour', 'Q1'], ['Orange', 'Colour', 'Q2'], ['Green', 'Colour', 'April'], ['Green', 'Colour', 'May'], ['Blue', 'Colour', 'July'], ['Blue', 'Colour', 'August'], ['Blue', 'Colour', 'September'], ['White', 'Colour', 'Q4'], ['Red', 'Colour', 'October'], ['Green', 'Colour', 'November'], ['Red', 'Colour', 'December'], ['Winter', 'Season', 'Q1'], ['Spring', 'Season', 'Q2'], ['Summer', 'Season', 'Q3'], ['Autumn', 'Season', 'Q4'], ], columns=['PropertyValue', 'PropertyName', 'NodeName'] ) self.dim.load_properties_from_dataframe(df_properties, 'NodeName', 'PropertyName', 'PropertyValue') df = self.dim.save_properties_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_properties.csv', index=False) self.assertFileEqual(f'{FOLDER}df_properties.csv', f'{BASELINE}df_properties.csv') return def test_010_load_save_values(self): df_values = pd.DataFrame( [ [-10.0, 'Costs', 'January'], [-100.0, 'Costs', 'February'], [-1000.0, 'Costs', 'March'], [-20.0, 'Costs', 'April'], [-200.0, 'Costs', 'May'], [-2000.0, 'Costs', 'June'], [-30.0, 'Costs', 'July'], [-300.0, 'Costs', 'August'], [-3000.0, 'Costs', 'September'], [-40.0, 'Costs', 'October'], [-400.0, 'Costs', 'November'], [-4000.0, 'Costs', 'December'], [10.0, 'Profit', 'January'], [100.0, 'Profit', 'February'], [1000.0, 'Profit', 'March'], [20.0, 'Profit', 'April'], [200.0, 'Profit', 'May'], [2000.0, 'Profit', 'June'], [30.0, 'Profit', 'July'], [300.0, 'Profit', 'August'], [3000.0, 'Profit', 'September'], [40.0, 'Profit', 'October'], [400.0, 'Profit', 'November'], [4000.0, 'Profit', 'December'], ], columns=['Value', 'ValueName', 'NodeName'] ) self.dim.load_values_from_dataframe(df_values, 'NodeName', 'ValueName', 'Value') df = self.dim.save_values_to_dataframe(None) df.drop(labels='index', axis=1, inplace=True) df.sort_values(by=['name', 'node', 'value'], axis=0, inplace=True) df.to_csv(f'{FOLDER}df_values.csv', index=False) self.assertFileEqual(f'{FOLDER}df_values.csv', f'{BASELINE}df_values.csv') return def test_011_get_hierarchy_dataframe(self): df = self.dim.get_hierarchy_dataframe(hierarchy=MAIN) df = df.reindex(columns=sorted(df.columns)) df.to_csv(f'{FOLDER}df_get_hierarchy_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_hierarchy_main.csv', f'{BASELINE}df_get_hierarchy_main.csv') return def test_012_get_aliases_dataframe(self): df = self.dim.get_aliases_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_aliases.csv', f'{BASELINE}df_get_aliases.csv') return def test_013_get_attributes_dataframe(self): df = self.dim.get_attributes_dataframe() df.drop(labels='index', axis=1, inplace=True) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_attributes.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_attributes.csv', f'{BASELINE}df_get_attributes.csv') return def test_014_get_consolidation_dataframe(self): df = self.dim.get_consolidation_dataframe('Costs', hierarchy=MAIN) df.to_csv(f'{FOLDER}df_get_consolidation_costs_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_consolidation_costs_main.csv', f'{BASELINE}df_get_consolidation_costs_main.csv') return def test_015_get_properties_dataframe(self): df = self.dim.get_properties_dataframe() df.drop(labels='index', axis=1, inplace=True) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_properties.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_properties.csv', f'{BASELINE}df_get_properties.csv') return def test_016_get_values_dataframe(self): df = self.dim.get_values_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_values.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_values.csv', f'{BASELINE}df_get_values.csv') return def test_017_get_hierarchy_table(self): df = self.dim.hierarchy_table(hierarchy=MAIN) df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_hierarchy_table_main.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_hierarchy_table_main.csv', f'{BASELINE}df_get_hierarchy_table_main.csv') return def test_018_get_all_leaves(self): expected = ['April', 'August', 'December', 'February', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'September'] nodes = sorted(self.dim.get_all_leaves(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_019_get_all_nodes(self): expected = ['!!root!!', 'April', 'August', 'December', 'Donk-tober', 'February', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'September', 'Year'] nodes = sorted(self.dim.get_all_nodes(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_020_get_all_parents(self): expected = ['!!root!!', 'Donk-tober', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Year'] nodes = sorted(self.dim.get_all_parents(hierarchy=MAIN)) return self.assertListEqual(expected, nodes) def test_021_get_ancestors(self): expected = [[0, 'February'], [1, 'Q1'], [2, 'Year'], [3, '!!root!!']] nodes = self.dim.get_ancestors('February', hierarchy=MAIN) return self.assertListEqual(expected, nodes) def test_022_get_ancestor_at_generation(self): expected = 'Year' node = self.dim.get_ancestor_at_generation('February', 1, hierarchy=MAIN) return self.assertEqual(expected, node) def test_023_get_ancestor_at_level(self): expected = 'Year' node = self.dim.get_ancestor_at_level('February', 2, hierarchy=MAIN) return self.assertEqual(expected, node) def test_024_get_bottom(self): expected = 'March' node = self.dim.get_bottom('Q1', hierarchy=MAIN) return self.assertEqual(expected, node) def test_025_get_top(self): expected = 'January' node = self.dim.get_top('Q1', hierarchy=MAIN) return self.assertEqual(expected, node) def test_026_get_down(self): expected = 'March' node = self.dim.get_down('Q1', 'February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_027_get_up(self): expected = 'January' node = self.dim.get_up('Q1', 'February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_028_get_children(self): expected = ['January', 'February', 'March'] nodes = self.dim.get_children('Q1', hierarchy=MAIN) return self.assertListEqual(expected, nodes) def test_029_get_children_count(self): expected = 3 count = self.dim.get_children_count('Q1', hierarchy=MAIN) return self.assertEqual(expected, count) def test_030_get_generation(self): expected = 2 count = self.dim.get_generation('Q1', hierarchy=MAIN) return self.assertEqual(expected, count) def test_031_get_grandparent(self): expected = 'Year' node = self.dim.get_grandparent('February', hierarchy=MAIN) return self.assertEqual(expected, node) def test_032_get_leaves(self): expected = [[2, 'January'], [2, 'February'], [2, 'March'], [2, 'April'], [2, 'May'], [2, 'June'], [2, 'July'], [2, 'August'], [2, 'September'], [2, 'October'], [2, 'November'], [2, 'December'], [3, 'Janusday']] nodes = self.dim.get_leaves('Year', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_033_get_leaves_at_generation(self): expected = [[2, 'January'], [2, 'February'], [2, 'March'], [2, 'April'], [2, 'May'], [2, 'June'], [2, 'July'], [2, 'August'], [2, 'September'], [2, 'October'], [2, 'November'], [2, 'December']] nodes = self.dim.get_leaves_at_generation('Year', 2, hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_034_get_leaves_at_level(self): expected = [[3, 'January'], [3, 'February'], [3, 'March'], [3, 'April'], [3, 'May'], [3, 'June'], [3, 'July'], [3, 'August'], [3, 'September'], [3, 'October'], [3, 'November'], [3, 'December']] nodes = self.dim.get_leaves_at_level('February', 0, hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_035_get_parent(self): expected = 'Q1' nodes = self.dim.get_parent('February', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_036_get_parents(self): expected = [['financial', 'halves', 'main'], ['YTD', 'Q1', 'Q1']] nodes = self.dim.get_parents('February') return self.assertEqual(expected, nodes) def test_037_get_siblings(self): expected = ['January', 'February', 'March'] nodes = self.dim.get_siblings('February', hierarchy=MAIN) return self.assertEqual(expected, nodes) def test_038_get_difference(self): expected = sorted(['Janusday', 'Year', 'Q5', 'Donk-tober']) nodes = sorted(self.dim.get_difference(['halves'])) return self.assertEqual(expected, nodes) def test_039_get_intersection(self): expected = sorted(['!!root!!', 'April', 'August', 'December', 'February', 'January', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'September']) nodes = sorted(self.dim.get_intersection(['halves'])) return self.assertEqual(expected, nodes) def test_040_get_union(self): expected = sorted(['!!root!!', 'April', 'August', 'December', 'Donk-tober', 'February', 'H1', 'H2', 'January', 'Janusday', 'July', 'June', 'March', 'May', 'November', 'October', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'September', 'Year']) nodes = sorted(self.dim.get_union(['halves'])) return self.assertEqual(expected, nodes) def test_041_add_node_to_alt(self): expected = 'H2' self.dim.add_node('H2', 'Q5', '+', hierarchy='halves', after='Q4') node = self.dim.get_parent('Q5', hierarchy='halves') return self.assertEqual(expected, node) def test_042_move_node_in_alt(self): expected = 'H1' self.dim.move_node('Q5', 'H1', hierarchy='halves', before='Q2') node = self.dim.get_parent('Q5', hierarchy='halves') return self.assertEqual(expected, node) def test_043_rename_node(self): expected = 'Q5' self.dim.rename_node('Donk-tober', 'Davetober') node = self.dim.get_parent('Davetober', hierarchy=MAIN) return self.assertEqual(expected, node) def test_044_delete_node(self): self.dim.delete_node('Year', 'Q5', hierarchy=MAIN) node = self.dim.node_exists('Q5') return self.assertFalse(node) def test_045_default_alias_dataframe(self): self.dim.set_default_aliases(primary='Welsh', secondary='French') df = self.dim.get_aliases_dataframe() df = df.reindex(columns=sorted(df.columns)) df.sort_values(by=list(df.columns), axis=0, inplace=True) df.to_csv(f'{FOLDER}df_get_default_aliases.csv', index=False) self.assertFileEqual(f'{FOLDER}df_get_default_aliases.csv', f'{BASELINE}df_get_default_aliases.csv') pass def tearDown(self): self.dim = None self.dims = None

true

f7201125ce532819474be57b6c62cb7fcba4cd59

33,749

py

Python

sdks/python/apache_beam/dataframe/pandas_doctests_test.py

psobot/beam

d9da8a4dc818b01a86d2dce2e78c0d78b47038bb

[ "Apache-2.0", "BSD-3-Clause" ]

5

2019-07-27T11:54:33.000Z

2021-06-06T11:53:36.000Z

sdks/python/apache_beam/dataframe/pandas_doctests_test.py

psobot/beam

d9da8a4dc818b01a86d2dce2e78c0d78b47038bb

[ "Apache-2.0", "BSD-3-Clause" ]

12

2019-04-15T15:27:23.000Z

2019-07-01T18:13:10.000Z

sdks/python/apache_beam/dataframe/pandas_doctests_test.py

psobot/beam

d9da8a4dc818b01a86d2dce2e78c0d78b47038bb

[ "Apache-2.0", "BSD-3-Clause" ]

1

2021-06-03T19:54:48.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import sys import unittest import pandas as pd from apache_beam.dataframe import doctests from apache_beam.dataframe.pandas_top_level_functions import _is_top_level_function @unittest.skipIf(sys.platform == 'win32', '[BEAM-10626]') class DoctestTest(unittest.TestCase): def test_ndframe_tests(self): # IO methods are tested in io_test.py skip_writes = { f'pandas.core.generic.NDFrame.{name}': ['*'] for name in dir(pd.core.generic.NDFrame) if name.startswith('to_') } result = doctests.testmod( pd.core.generic, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.generic.NDFrame.first': ['*'], 'pandas.core.generic.NDFrame.head': ['*'], 'pandas.core.generic.NDFrame.last': ['*'], 'pandas.core.generic.NDFrame.shift': ['*'], 'pandas.core.generic.NDFrame.tail': ['*'], 'pandas.core.generic.NDFrame.take': ['*'], 'pandas.core.generic.NDFrame.values': ['*'], 'pandas.core.generic.NDFrame.tz_localize': [ "s.tz_localize('CET', ambiguous='infer')", # np.array is not a deferred object. This use-case is possible # with a deferred Series though, which is tested in # frames_test.py "s.tz_localize('CET', ambiguous=np.array([True, True, False]))", ], 'pandas.core.generic.NDFrame.truncate': [ # These inputs rely on tail (wont implement, order # sensitive) for verification "df.tail()", "df.loc['2016-01-05':'2016-01-10', :].tail()", ], 'pandas.core.generic.NDFrame.replace': [ "s.replace([1, 2], method='bfill')", # Relies on method='pad' "s.replace('a', None)", ], 'pandas.core.generic.NDFrame.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.generic.NDFrame.sort_values': ['*'], 'pandas.core.generic.NDFrame.mask': [ 'df.where(m, -df) == np.where(m, df, -df)' ], 'pandas.core.generic.NDFrame.where': [ 'df.where(m, -df) == np.where(m, df, -df)' ], 'pandas.core.generic.NDFrame.interpolate': ['*'], }, not_implemented_ok={ 'pandas.core.generic.NDFrame.asof': ['*'], 'pandas.core.generic.NDFrame.at_time': ['*'], 'pandas.core.generic.NDFrame.between_time': ['*'], 'pandas.core.generic.NDFrame.describe': ['*'], 'pandas.core.generic.NDFrame.ewm': ['*'], 'pandas.core.generic.NDFrame.expanding': ['*'], 'pandas.core.generic.NDFrame.flags': ['*'], 'pandas.core.generic.NDFrame.pct_change': ['*'], 'pandas.core.generic.NDFrame.rank': ['*'], 'pandas.core.generic.NDFrame.reindex': ['*'], 'pandas.core.generic.NDFrame.reindex_like': ['*'], 'pandas.core.generic.NDFrame.replace': ['*'], 'pandas.core.generic.NDFrame.resample': ['*'], 'pandas.core.generic.NDFrame.rolling': ['*'], 'pandas.core.generic.NDFrame.sample': ['*'], 'pandas.core.generic.NDFrame.set_flags': ['*'], 'pandas.core.generic.NDFrame.squeeze': ['*'], 'pandas.core.generic.NDFrame.transform': ['*'], 'pandas.core.generic.NDFrame.truncate': ['*'], 'pandas.core.generic.NDFrame.xs': ['*'], # argsort unimplemented 'pandas.core.generic.NDFrame.abs': [ 'df.loc[(df.c - 43).abs().argsort()]', ], }, skip={ # Internal test 'pandas.core.generic.NDFrame._set_axis_name': ['*'], # Fails to construct test series. asfreq is not implemented anyway. 'pandas.core.generic.NDFrame.asfreq': ['*'], 'pandas.core.generic.NDFrame.astype': ['*'], 'pandas.core.generic.NDFrame.convert_dtypes': ['*'], 'pandas.core.generic.NDFrame.copy': ['*'], 'pandas.core.generic.NDFrame.droplevel': ['*'], 'pandas.core.generic.NDFrame.infer_objects': ['*'], 'pandas.core.generic.NDFrame.rank': [ # Modified dataframe 'df' ], 'pandas.core.generic.NDFrame.rename': [ # Seems to be an upstream bug. The actual error has a different # message: # TypeError: Index(...) must be called with a collection of # some kind, 2 was passed # pandas doctests only verify the type of exception 'df.rename(2)' ], # Tests rely on setting index 'pandas.core.generic.NDFrame.rename_axis': ['*'], # Raises right exception, but testing framework has matching issues. 'pandas.core.generic.NDFrame.replace': [ "df.replace({'a string': 'new value', True: False}) # raises" ], 'pandas.core.generic.NDFrame.squeeze': ['*'], # NameError 'pandas.core.generic.NDFrame.resample': ['df'], # Skipped so we don't need to install natsort 'pandas.core.generic.NDFrame.sort_values': [ 'from natsort import index_natsorted', 'df.sort_values(\n' ' by="time",\n' ' key=lambda x: np.argsort(index_natsorted(df["time"]))\n' ')' ], **skip_writes }) self.assertEqual(result.failed, 0) def test_dataframe_tests(self): result = doctests.testmod( pd.core.frame, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.frame.DataFrame.T': ['*'], 'pandas.core.frame.DataFrame.cummax': ['*'], 'pandas.core.frame.DataFrame.cummin': ['*'], 'pandas.core.frame.DataFrame.cumsum': ['*'], 'pandas.core.frame.DataFrame.cumprod': ['*'], 'pandas.core.frame.DataFrame.diff': ['*'], 'pandas.core.frame.DataFrame.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.frame.DataFrame.items': ['*'], 'pandas.core.frame.DataFrame.itertuples': ['*'], 'pandas.core.frame.DataFrame.iterrows': ['*'], 'pandas.core.frame.DataFrame.iteritems': ['*'], # default keep is 'first' 'pandas.core.frame.DataFrame.nlargest': [ "df.nlargest(3, 'population')", "df.nlargest(3, ['population', 'GDP'])", "df.nlargest(3, 'population', keep='last')" ], 'pandas.core.frame.DataFrame.nsmallest': [ "df.nsmallest(3, 'population')", "df.nsmallest(3, ['population', 'GDP'])", "df.nsmallest(3, 'population', keep='last')", ], 'pandas.core.frame.DataFrame.replace': [ "s.replace([1, 2], method='bfill')", # Relies on method='pad' "s.replace('a', None)", ], 'pandas.core.frame.DataFrame.to_records': ['*'], 'pandas.core.frame.DataFrame.to_dict': ['*'], 'pandas.core.frame.DataFrame.to_numpy': ['*'], 'pandas.core.frame.DataFrame.to_string': ['*'], 'pandas.core.frame.DataFrame.transpose': ['*'], 'pandas.core.frame.DataFrame.shape': ['*'], 'pandas.core.frame.DataFrame.shift': [ 'df.shift(periods=3, freq="D")', 'df.shift(periods=3, freq="infer")' ], 'pandas.core.frame.DataFrame.unstack': ['*'], 'pandas.core.frame.DataFrame.memory_usage': ['*'], 'pandas.core.frame.DataFrame.info': ['*'], # Not equal to df.agg('mode', axis='columns', numeric_only=True) # because there can be multiple columns if a row has more than one # mode 'pandas.core.frame.DataFrame.mode': [ "df.mode(axis='columns', numeric_only=True)" ], 'pandas.core.frame.DataFrame.append': [ 'df.append(df2, ignore_index=True)', "for i in range(5):\n" + " df = df.append({'A': i}, ignore_index=True)", ], 'pandas.core.frame.DataFrame.sort_index': ['*'], 'pandas.core.frame.DataFrame.sort_values': ['*'], 'pandas.core.frame.DataFrame.melt': [ "df.melt(id_vars=['A'], value_vars=['B'])", "df.melt(id_vars=['A'], value_vars=['B', 'C'])", "df.melt(col_level=0, id_vars=['A'], value_vars=['B'])", "df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])", "df.melt(id_vars=['A'], value_vars=['B'],\n" + " var_name='myVarname', value_name='myValname')" ] }, not_implemented_ok={ 'pandas.core.frame.DataFrame.transform': ['*'], 'pandas.core.frame.DataFrame.reindex': ['*'], 'pandas.core.frame.DataFrame.reindex_axis': ['*'], 'pandas.core.frame.DataFrame.round': [ 'df.round(decimals)', ], # We should be able to support pivot and pivot_table for categorical # columns 'pandas.core.frame.DataFrame.pivot': ['*'], # We can implement this as a zipping operator, but it won't have the # same capability. The doctest includes an example that branches on # a deferred result. 'pandas.core.frame.DataFrame.combine': ['*'], # Can be implemented as a zipping operator 'pandas.core.frame.DataFrame.combine_first': ['*'], # Difficult to parallelize but should be possible? 'pandas.core.frame.DataFrame.dot': [ # reindex not supported 's2 = s.reindex([1, 0, 2, 3])', 'df.dot(s2)', ], # Trivially elementwise for axis=columns. Relies on global indexing # for axis=rows. # Difficult to determine proxy, need to inspect function 'pandas.core.frame.DataFrame.apply': ['*'], # Cross-join not implemented 'pandas.core.frame.DataFrame.merge': [ "df1.merge(df2, how='cross')" ], # TODO(BEAM-11711) 'pandas.core.frame.DataFrame.set_index': [ "df.set_index([s, s**2])", ], }, skip={ # Throws NotImplementedError when modifying df 'pandas.core.frame.DataFrame.transform': ['df'], 'pandas.core.frame.DataFrame.axes': [ # Returns deferred index. 'df.axes', ], 'pandas.core.frame.DataFrame.compare': ['*'], 'pandas.core.frame.DataFrame.cov': [ # Relies on setting entries ahead of time. "df.loc[df.index[:5], 'a'] = np.nan", "df.loc[df.index[5:10], 'b'] = np.nan", 'df.cov(min_periods=12)', ], 'pandas.core.frame.DataFrame.drop_duplicates': ['*'], 'pandas.core.frame.DataFrame.duplicated': ['*'], 'pandas.core.frame.DataFrame.idxmax': ['*'], 'pandas.core.frame.DataFrame.idxmin': ['*'], 'pandas.core.frame.DataFrame.rename': [ # Returns deferred index. 'df.index', 'df.rename(index=str).index', ], 'pandas.core.frame.DataFrame.set_index': [ # TODO(BEAM-11711): This could pass in the index as # a DeferredIndex, and we should fail it as order-sensitive. "df.set_index([pd.Index([1, 2, 3, 4]), 'year'])", ], 'pandas.core.frame.DataFrame.set_axis': ['*'], 'pandas.core.frame.DataFrame.to_markdown': ['*'], 'pandas.core.frame.DataFrame.to_parquet': ['*'], 'pandas.core.frame.DataFrame.value_counts': ['*'], 'pandas.core.frame.DataFrame.to_records': [ 'df.index = df.index.rename("I")', 'index_dtypes = f"<S{df.index.str.len().max()}"', # 1.x 'index_dtypes = "<S{}".format(df.index.str.len().max())', #0.x 'df.to_records(index_dtypes=index_dtypes)', ], # These tests use the static method pd.pivot_table, which doesn't # actually raise NotImplementedError 'pandas.core.frame.DataFrame.pivot_table': ['*'], # Expected to raise a ValueError, but we raise NotImplementedError 'pandas.core.frame.DataFrame.pivot': [ "df.pivot(index='foo', columns='bar', values='baz')" ], 'pandas.core.frame.DataFrame.append': [ 'df', # pylint: disable=line-too-long "pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n" " ignore_index=True)" ], 'pandas.core.frame.DataFrame.eval': ['df'], 'pandas.core.frame.DataFrame.melt': [ "df.columns = [list('ABC'), list('DEF')]", "df" ], 'pandas.core.frame.DataFrame.merge': [ # Order-sensitive index, checked in frames_test.py. "df1.merge(df2, left_on='lkey', right_on='rkey')", "df1.merge(df2, left_on='lkey', right_on='rkey',\n" " suffixes=('_left', '_right'))", "df1.merge(df2, how='left', on='a')", ], # Raises right exception, but testing framework has matching issues. 'pandas.core.frame.DataFrame.replace': [ "df.replace({'a string': 'new value', True: False}) # raises" ], 'pandas.core.frame.DataFrame.to_sparse': ['type(df)'], # Skipped because "seen_wont_implement" is reset before getting to # these calls, so the NameError they raise is not ignored. 'pandas.core.frame.DataFrame.T': [ 'df1_transposed.dtypes', 'df2_transposed.dtypes' ], 'pandas.core.frame.DataFrame.transpose': [ 'df1_transposed.dtypes', 'df2_transposed.dtypes' ], # Skipped because the relies on iloc to set a cell to NA. Test is # replicated in frames_test::DeferredFrameTest::test_applymap. 'pandas.core.frame.DataFrame.applymap': [ 'df_copy.iloc[0, 0] = pd.NA', "df_copy.applymap(lambda x: len(str(x)), na_action='ignore')", ], # Skipped so we don't need to install natsort 'pandas.core.frame.DataFrame.sort_values': [ 'from natsort import index_natsorted', 'df.sort_values(\n' ' by="time",\n' ' key=lambda x: np.argsort(index_natsorted(df["time"]))\n' ')' ], # Mode that we don't yet support, documentation added in pandas # 1.2.0 (https://github.com/pandas-dev/pandas/issues/35912) 'pandas.core.frame.DataFrame.aggregate': [ "df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))" ], }) self.assertEqual(result.failed, 0) def test_series_tests(self): result = doctests.testmod( pd.core.series, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.series.Series.__array__': ['*'], 'pandas.core.series.Series.array': ['*'], 'pandas.core.series.Series.cummax': ['*'], 'pandas.core.series.Series.cummin': ['*'], 'pandas.core.series.Series.cumsum': ['*'], 'pandas.core.series.Series.cumprod': ['*'], 'pandas.core.series.Series.diff': ['*'], 'pandas.core.series.Series.dot': [ 's.dot(arr)', # non-deferred result ], 'pandas.core.series.Series.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.series.Series.items': ['*'], 'pandas.core.series.Series.iteritems': ['*'], # default keep is 'first' 'pandas.core.series.Series.nlargest': [ "s.nlargest()", "s.nlargest(3)", "s.nlargest(3, keep='last')", ], 'pandas.core.series.Series.memory_usage': ['*'], 'pandas.core.series.Series.nsmallest': [ "s.nsmallest()", "s.nsmallest(3)", "s.nsmallest(3, keep='last')", ], 'pandas.core.series.Series.pop': ['*'], 'pandas.core.series.Series.searchsorted': ['*'], 'pandas.core.series.Series.shift': ['*'], 'pandas.core.series.Series.take': ['*'], 'pandas.core.series.Series.to_dict': ['*'], 'pandas.core.series.Series.unique': ['*'], 'pandas.core.series.Series.unstack': ['*'], 'pandas.core.series.Series.values': ['*'], 'pandas.core.series.Series.view': ['*'], 'pandas.core.series.Series.append': [ 's1.append(s2, ignore_index=True)', ], 'pandas.core.series.Series.sort_index': ['*'], 'pandas.core.series.Series.sort_values': ['*'], 'pandas.core.series.Series.argmax': ['*'], 'pandas.core.series.Series.argmin': ['*'], }, not_implemented_ok={ 'pandas.core.series.Series.transform': ['*'], 'pandas.core.series.Series.groupby': [ 'ser.groupby(["a", "b", "a", "b"]).mean()', 'ser.groupby(["a", "b", "a", np.nan]).mean()', 'ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()', # Grouping by a series is not supported 'ser.groupby(ser > 100).mean()', ], 'pandas.core.series.Series.reindex': ['*'], }, skip={ # error formatting 'pandas.core.series.Series.append': [ 's1.append(s2, verify_integrity=True)', ], # Throws NotImplementedError when modifying df 'pandas.core.series.Series.transform': ['df'], 'pandas.core.series.Series.autocorr': ['*'], 'pandas.core.series.Series.combine': ['*'], 'pandas.core.series.Series.combine_first': ['*'], 'pandas.core.series.Series.compare': ['*'], 'pandas.core.series.Series.cov': [ # Differs in LSB on jenkins. "s1.cov(s2)", ], 'pandas.core.series.Series.drop_duplicates': ['*'], 'pandas.core.series.Series.duplicated': ['*'], 'pandas.core.series.Series.explode': ['*'], 'pandas.core.series.Series.idxmax': ['*'], 'pandas.core.series.Series.idxmin': ['*'], 'pandas.core.series.Series.nonzero': ['*'], 'pandas.core.series.Series.quantile': ['*'], 'pandas.core.series.Series.pop': ['ser'], # testing side effect 'pandas.core.series.Series.repeat': ['*'], 'pandas.core.series.Series.replace': ['*'], 'pandas.core.series.Series.reset_index': ['*'], 'pandas.core.series.Series.searchsorted': [ # This doctest seems to be incorrectly parsed. "x = pd.Categorical(['apple', 'bread', 'bread'," ], 'pandas.core.series.Series.set_axis': ['*'], 'pandas.core.series.Series.to_csv': ['*'], 'pandas.core.series.Series.to_markdown': ['*'], 'pandas.core.series.Series.update': ['*'], 'pandas.core.series.Series.view': [ # Inspection after modification. 's' ], }) self.assertEqual(result.failed, 0) def test_string_tests(self): PD_VERSION = tuple(int(v) for v in pd.__version__.split('.')) if PD_VERSION < (1, 2, 0): module = pd.core.strings else: # Definitions were moved to accessor in pandas 1.2.0 module = pd.core.strings.accessor module_name = module.__name__ result = doctests.testmod( module, use_beam=False, wont_implement_ok={ # These methods can accept deferred series objects, but not lists f'{module_name}.StringMethods.cat': [ "s.str.cat(['A', 'B', 'C', 'D'], sep=',')", "s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')", "s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')" ], f'{module_name}.StringMethods.repeat': [ 's.str.repeat(repeats=[1, 2, 3])' ], f'{module_name}.str_repeat': ['s.str.repeat(repeats=[1, 2, 3])'], f'{module_name}.StringMethods.get_dummies': ['*'], f'{module_name}.str_get_dummies': ['*'], }, skip={ # count() on Series with a NaN produces mismatched type if we # have a NaN-only partition. f'{module_name}.StringMethods.count': ["s.str.count('a')"], f'{module_name}.str_count': ["s.str.count('a')"], # Produce None instead of NaN, see # frames_test.py::DeferredFrameTest::test_str_split f'{module_name}.StringMethods.rsplit': [ 's.str.split(expand=True)', 's.str.rsplit("/", n=1, expand=True)', ], f'{module_name}.StringMethods.split': [ 's.str.split(expand=True)', 's.str.rsplit("/", n=1, expand=True)', ], # Bad test strings in pandas 1.1.x f'{module_name}.str_replace': [ "pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)" ], f'{module_name}.StringMethods.replace': [ "pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)" ], # output has incorrect formatting in 1.2.x f'{module_name}.StringMethods.extractall': ['*'] }) self.assertEqual(result.failed, 0) def test_datetime_tests(self): # TODO(BEAM-10721) datetimelike_result = doctests.testmod( pd.core.arrays.datetimelike, use_beam=False, skip={ 'pandas.core.arrays.datetimelike.AttributesMixin._unbox_scalar': [ '*' ], 'pandas.core.arrays.datetimelike.TimelikeOps.ceil': ['*'], 'pandas.core.arrays.datetimelike.TimelikeOps.floor': ['*'], 'pandas.core.arrays.datetimelike.TimelikeOps.round': ['*'], }) datetime_result = doctests.testmod( pd.core.arrays.datetimes, use_beam=False, skip={ 'pandas.core.arrays.datetimes.DatetimeArray.day': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.hour': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.microsecond': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.minute': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.month': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.nanosecond': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.second': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.year': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_leap_year': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_month_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_month_start': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_start': [ '*' ], 'pandas.core.arrays.datetimes.DatetimeArray.is_year_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_year_start': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.to_period': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.tz_localize': ['*'], }) self.assertEqual(datetimelike_result.failed, 0) self.assertEqual(datetime_result.failed, 0) def test_indexing_tests(self): result = doctests.testmod( pd.core.indexing, use_beam=False, skip={ 'pandas.core.indexing._IndexSlice': ['*'], 'pandas.core.indexing.IndexingMixin.at': ['*'], 'pandas.core.indexing.IndexingMixin.iat': ['*'], 'pandas.core.indexing.IndexingMixin.iloc': ['*'], 'pandas.core.indexing.IndexingMixin.loc': ['*'], 'pandas.core.indexing._AtIndexer': ['*'], 'pandas.core.indexing._LocIndexer': ['*'], 'pandas.core.indexing._iAtIndexer': ['*'], 'pandas.core.indexing._iLocIndexer': ['*'], }) self.assertEqual(result.failed, 0) def test_groupby_tests(self): result = doctests.testmod( pd.core.groupby.groupby, use_beam=False, wont_implement_ok={ 'pandas.core.groupby.groupby.GroupBy.head': ['*'], 'pandas.core.groupby.groupby.GroupBy.tail': ['*'], 'pandas.core.groupby.groupby.GroupBy.nth': ['*'], 'pandas.core.groupby.groupby.GroupBy.cumcount': ['*'], }, not_implemented_ok={ 'pandas.core.groupby.groupby.GroupBy.describe': ['*'], 'pandas.core.groupby.groupby.GroupBy.ngroup': ['*'], 'pandas.core.groupby.groupby.GroupBy.resample': ['*'], 'pandas.core.groupby.groupby.GroupBy.sample': ['*'], 'pandas.core.groupby.groupby.GroupBy.quantile': ['*'], 'pandas.core.groupby.groupby.BaseGroupBy.pipe': ['*'], # pipe tests are in a different location in pandas 1.1.x 'pandas.core.groupby.groupby._GroupBy.pipe': ['*'], 'pandas.core.groupby.groupby.GroupBy.nth': [ "df.groupby('A', as_index=False).nth(1)", ], }, skip={ # Uses iloc to mutate a DataFrame 'pandas.core.groupby.groupby.GroupBy.resample': [ 'df.iloc[2, 0] = 5', 'df', ], # TODO: Raise wont implement for list passed as a grouping column # Currently raises unhashable type: list 'pandas.core.groupby.groupby.GroupBy.ngroup': [ 'df.groupby(["A", [1,1,2,3,2,1]]).ngroup()' ], }) self.assertEqual(result.failed, 0) result = doctests.testmod( pd.core.groupby.generic, use_beam=False, wont_implement_ok={ # Returns an array by default, not a Series. WontImplement # (non-deferred) 'pandas.core.groupby.generic.SeriesGroupBy.unique': ['*'], # TODO: Is take actually deprecated? 'pandas.core.groupby.generic.DataFrameGroupBy.take': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.take': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.nsmallest': [ "s.nsmallest(3, keep='last')", "s.nsmallest(3)", "s.nsmallest()", ], 'pandas.core.groupby.generic.SeriesGroupBy.nlargest': [ "s.nlargest(3, keep='last')", "s.nlargest(3)", "s.nlargest()", ], 'pandas.core.groupby.generic.DataFrameGroupBy.diff': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.diff': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.hist': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.groupby.generic.SeriesGroupBy.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], }, not_implemented_ok={ 'pandas.core.groupby.generic.DataFrameGroupBy.transform': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.idxmax': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.idxmin': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.filter': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.nunique': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.transform': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.idxmax': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.idxmin': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.filter': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.describe': ['*'], }, skip={ 'pandas.core.groupby.generic.SeriesGroupBy.cov': [ # Floating point comparison fails 's1.cov(s2)', ], 'pandas.core.groupby.generic.DataFrameGroupBy.cov': [ # Mutates input DataFrame with loc # TODO: Replicate in frames_test.py "df.loc[df.index[:5], 'a'] = np.nan", "df.loc[df.index[5:10], 'b'] = np.nan", "df.cov(min_periods=12)", ], # These examples rely on grouping by a list 'pandas.core.groupby.generic.SeriesGroupBy.aggregate': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.aggregate': ['*'], }) self.assertEqual(result.failed, 0) def test_top_level(self): tests = { name: func.__doc__ for (name, func) in pd.__dict__.items() if _is_top_level_function(func) and getattr(func, '__doc__', None) } # IO methods are tested in io_test.py skip_reads = {name: ['*'] for name in dir(pd) if name.startswith('read_')} result = doctests.teststrings( tests, use_beam=False, report=True, not_implemented_ok={ 'concat': ['pd.concat([s1, s2], ignore_index=True)'], 'crosstab': ['*'], 'cut': ['*'], 'eval': ['*'], 'factorize': ['*'], 'get_dummies': ['*'], 'infer_freq': ['*'], 'lreshape': ['*'], 'melt': ['*'], 'merge': ["df1.merge(df2, how='cross')"], 'merge_asof': ['*'], 'pivot': ['*'], 'pivot_table': ['*'], 'qcut': ['*'], 'reset_option': ['*'], 'set_eng_float_format': ['*'], 'set_option': ['*'], 'to_numeric': ['*'], 'to_timedelta': ['*'], 'unique': ['*'], 'value_counts': ['*'], 'wide_to_long': ['*'], }, wont_implement_ok={ 'to_datetime': ['s.head()'], 'to_pickle': ['*'], 'melt': [ "pd.melt(df, id_vars=['A'], value_vars=['B'])", "pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])", "pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B'])", "pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])", "pd.melt(df, id_vars=['A'], value_vars=['B'],\n" + " var_name='myVarname', value_name='myValname')" ], }, skip={ # error formatting 'concat': ['pd.concat([df5, df6], verify_integrity=True)'], # doctest DeprecationWarning 'melt': ['df'], # Order-sensitive re-indexing. 'merge': [ "df1.merge(df2, left_on='lkey', right_on='rkey')", "df1.merge(df2, left_on='lkey', right_on='rkey',\n" " suffixes=('_left', '_right'))", "df1.merge(df2, how='left', on='a')", ], # Not an actual test. 'option_context': ['*'], 'factorize': ['codes', 'uniques'], # Bad top-level use of un-imported function. 'merge_ordered': [ 'merge_ordered(df1, df2, fill_method="ffill", left_by="group")' ], # Expected error. 'pivot': ["df.pivot(index='foo', columns='bar', values='baz')"], # Never written. 'to_pickle': ['os.remove("./dummy.pkl")'], **skip_reads }) self.assertEqual(result.failed, 0) if __name__ == '__main__': unittest.main()

44.406579

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0.518919

import sys import unittest import pandas as pd from apache_beam.dataframe import doctests from apache_beam.dataframe.pandas_top_level_functions import _is_top_level_function @unittest.skipIf(sys.platform == 'win32', '[BEAM-10626]') class DoctestTest(unittest.TestCase): def test_ndframe_tests(self): skip_writes = { f'pandas.core.generic.NDFrame.{name}': ['*'] for name in dir(pd.core.generic.NDFrame) if name.startswith('to_') } result = doctests.testmod( pd.core.generic, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.generic.NDFrame.first': ['*'], 'pandas.core.generic.NDFrame.head': ['*'], 'pandas.core.generic.NDFrame.last': ['*'], 'pandas.core.generic.NDFrame.shift': ['*'], 'pandas.core.generic.NDFrame.tail': ['*'], 'pandas.core.generic.NDFrame.take': ['*'], 'pandas.core.generic.NDFrame.values': ['*'], 'pandas.core.generic.NDFrame.tz_localize': [ "s.tz_localize('CET', ambiguous='infer')", "s.tz_localize('CET', ambiguous=np.array([True, True, False]))", ], 'pandas.core.generic.NDFrame.truncate': [ "df.tail()", "df.loc['2016-01-05':'2016-01-10', :].tail()", ], 'pandas.core.generic.NDFrame.replace': [ "s.replace([1, 2], method='bfill')", "s.replace('a', None)", ], 'pandas.core.generic.NDFrame.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.generic.NDFrame.sort_values': ['*'], 'pandas.core.generic.NDFrame.mask': [ 'df.where(m, -df) == np.where(m, df, -df)' ], 'pandas.core.generic.NDFrame.where': [ 'df.where(m, -df) == np.where(m, df, -df)' ], 'pandas.core.generic.NDFrame.interpolate': ['*'], }, not_implemented_ok={ 'pandas.core.generic.NDFrame.asof': ['*'], 'pandas.core.generic.NDFrame.at_time': ['*'], 'pandas.core.generic.NDFrame.between_time': ['*'], 'pandas.core.generic.NDFrame.describe': ['*'], 'pandas.core.generic.NDFrame.ewm': ['*'], 'pandas.core.generic.NDFrame.expanding': ['*'], 'pandas.core.generic.NDFrame.flags': ['*'], 'pandas.core.generic.NDFrame.pct_change': ['*'], 'pandas.core.generic.NDFrame.rank': ['*'], 'pandas.core.generic.NDFrame.reindex': ['*'], 'pandas.core.generic.NDFrame.reindex_like': ['*'], 'pandas.core.generic.NDFrame.replace': ['*'], 'pandas.core.generic.NDFrame.resample': ['*'], 'pandas.core.generic.NDFrame.rolling': ['*'], 'pandas.core.generic.NDFrame.sample': ['*'], 'pandas.core.generic.NDFrame.set_flags': ['*'], 'pandas.core.generic.NDFrame.squeeze': ['*'], 'pandas.core.generic.NDFrame.transform': ['*'], 'pandas.core.generic.NDFrame.truncate': ['*'], 'pandas.core.generic.NDFrame.xs': ['*'], 'pandas.core.generic.NDFrame.abs': [ 'df.loc[(df.c - 43).abs().argsort()]', ], }, skip={ 'pandas.core.generic.NDFrame._set_axis_name': ['*'], 'pandas.core.generic.NDFrame.asfreq': ['*'], 'pandas.core.generic.NDFrame.astype': ['*'], 'pandas.core.generic.NDFrame.convert_dtypes': ['*'], 'pandas.core.generic.NDFrame.copy': ['*'], 'pandas.core.generic.NDFrame.droplevel': ['*'], 'pandas.core.generic.NDFrame.infer_objects': ['*'], 'pandas.core.generic.NDFrame.rank': [ 'df' ], 'pandas.core.generic.NDFrame.rename': [ 'df.rename(2)' ], 'pandas.core.generic.NDFrame.rename_axis': ['*'], 'pandas.core.generic.NDFrame.replace': [ "df.replace({'a string': 'new value', True: False}) # raises" ], 'pandas.core.generic.NDFrame.squeeze': ['*'], 'pandas.core.generic.NDFrame.resample': ['df'], 'pandas.core.generic.NDFrame.sort_values': [ 'from natsort import index_natsorted', 'df.sort_values(\n' ' by="time",\n' ' key=lambda x: np.argsort(index_natsorted(df["time"]))\n' ')' ], **skip_writes }) self.assertEqual(result.failed, 0) def test_dataframe_tests(self): result = doctests.testmod( pd.core.frame, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.frame.DataFrame.T': ['*'], 'pandas.core.frame.DataFrame.cummax': ['*'], 'pandas.core.frame.DataFrame.cummin': ['*'], 'pandas.core.frame.DataFrame.cumsum': ['*'], 'pandas.core.frame.DataFrame.cumprod': ['*'], 'pandas.core.frame.DataFrame.diff': ['*'], 'pandas.core.frame.DataFrame.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.frame.DataFrame.items': ['*'], 'pandas.core.frame.DataFrame.itertuples': ['*'], 'pandas.core.frame.DataFrame.iterrows': ['*'], 'pandas.core.frame.DataFrame.iteritems': ['*'], # default keep is 'first' 'pandas.core.frame.DataFrame.nlargest': [ "df.nlargest(3, 'population')", "df.nlargest(3, ['population', 'GDP'])", "df.nlargest(3, 'population', keep='last')" ], 'pandas.core.frame.DataFrame.nsmallest': [ "df.nsmallest(3, 'population')", "df.nsmallest(3, ['population', 'GDP'])", "df.nsmallest(3, 'population', keep='last')", ], 'pandas.core.frame.DataFrame.replace': [ "s.replace([1, 2], method='bfill')", # Relies on method='pad' "s.replace('a', None)", ], 'pandas.core.frame.DataFrame.to_records': ['*'], 'pandas.core.frame.DataFrame.to_dict': ['*'], 'pandas.core.frame.DataFrame.to_numpy': ['*'], 'pandas.core.frame.DataFrame.to_string': ['*'], 'pandas.core.frame.DataFrame.transpose': ['*'], 'pandas.core.frame.DataFrame.shape': ['*'], 'pandas.core.frame.DataFrame.shift': [ 'df.shift(periods=3, freq="D")', 'df.shift(periods=3, freq="infer")' ], 'pandas.core.frame.DataFrame.unstack': ['*'], 'pandas.core.frame.DataFrame.memory_usage': ['*'], 'pandas.core.frame.DataFrame.info': ['*'], # Not equal to df.agg('mode', axis='columns', numeric_only=True) # because there can be multiple columns if a row has more than one # mode 'pandas.core.frame.DataFrame.mode': [ "df.mode(axis='columns', numeric_only=True)" ], 'pandas.core.frame.DataFrame.append': [ 'df.append(df2, ignore_index=True)', "for i in range(5):\n" + " df = df.append({'A': i}, ignore_index=True)", ], 'pandas.core.frame.DataFrame.sort_index': ['*'], 'pandas.core.frame.DataFrame.sort_values': ['*'], 'pandas.core.frame.DataFrame.melt': [ "df.melt(id_vars=['A'], value_vars=['B'])", "df.melt(id_vars=['A'], value_vars=['B', 'C'])", "df.melt(col_level=0, id_vars=['A'], value_vars=['B'])", "df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])", "df.melt(id_vars=['A'], value_vars=['B'],\n" + " var_name='myVarname', value_name='myValname')" ] }, not_implemented_ok={ 'pandas.core.frame.DataFrame.transform': ['*'], 'pandas.core.frame.DataFrame.reindex': ['*'], 'pandas.core.frame.DataFrame.reindex_axis': ['*'], 'pandas.core.frame.DataFrame.round': [ 'df.round(decimals)', ], # We should be able to support pivot and pivot_table for categorical # columns 'pandas.core.frame.DataFrame.pivot': ['*'], # We can implement this as a zipping operator, but it won't have the 'pandas.core.frame.DataFrame.combine': ['*'], 'pandas.core.frame.DataFrame.combine_first': ['*'], 'pandas.core.frame.DataFrame.dot': [ 's2 = s.reindex([1, 0, 2, 3])', 'df.dot(s2)', ], 'pandas.core.frame.DataFrame.apply': ['*'], 'pandas.core.frame.DataFrame.merge': [ "df1.merge(df2, how='cross')" ], 'pandas.core.frame.DataFrame.set_index': [ "df.set_index([s, s**2])", ], }, skip={ 'pandas.core.frame.DataFrame.transform': ['df'], 'pandas.core.frame.DataFrame.axes': [ 'df.axes', ], 'pandas.core.frame.DataFrame.compare': ['*'], 'pandas.core.frame.DataFrame.cov': [ "df.loc[df.index[:5], 'a'] = np.nan", "df.loc[df.index[5:10], 'b'] = np.nan", 'df.cov(min_periods=12)', ], 'pandas.core.frame.DataFrame.drop_duplicates': ['*'], 'pandas.core.frame.DataFrame.duplicated': ['*'], 'pandas.core.frame.DataFrame.idxmax': ['*'], 'pandas.core.frame.DataFrame.idxmin': ['*'], 'pandas.core.frame.DataFrame.rename': [ 'df.index', 'df.rename(index=str).index', ], 'pandas.core.frame.DataFrame.set_index': [ "df.set_index([pd.Index([1, 2, 3, 4]), 'year'])", ], 'pandas.core.frame.DataFrame.set_axis': ['*'], 'pandas.core.frame.DataFrame.to_markdown': ['*'], 'pandas.core.frame.DataFrame.to_parquet': ['*'], 'pandas.core.frame.DataFrame.value_counts': ['*'], 'pandas.core.frame.DataFrame.to_records': [ 'df.index = df.index.rename("I")', 'index_dtypes = f"<S{df.index.str.len().max()}"', 'index_dtypes = "<S{}".format(df.index.str.len().max())', 'df.to_records(index_dtypes=index_dtypes)', ], # actually raise NotImplementedError 'pandas.core.frame.DataFrame.pivot_table': ['*'], # Expected to raise a ValueError, but we raise NotImplementedError 'pandas.core.frame.DataFrame.pivot': [ "df.pivot(index='foo', columns='bar', values='baz')" ], 'pandas.core.frame.DataFrame.append': [ 'df', # pylint: disable=line-too-long "pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],\n" " ignore_index=True)" ], 'pandas.core.frame.DataFrame.eval': ['df'], 'pandas.core.frame.DataFrame.melt': [ "df.columns = [list('ABC'), list('DEF')]", "df" ], 'pandas.core.frame.DataFrame.merge': [ # Order-sensitive index, checked in frames_test.py. "df1.merge(df2, left_on='lkey', right_on='rkey')", "df1.merge(df2, left_on='lkey', right_on='rkey',\n" " suffixes=('_left', '_right'))", "df1.merge(df2, how='left', on='a')", ], # Raises right exception, but testing framework has matching issues. 'pandas.core.frame.DataFrame.replace': [ "df.replace({'a string': 'new value', True: False}) # raises" ], 'pandas.core.frame.DataFrame.to_sparse': ['type(df)'], # Skipped because "seen_wont_implement" is reset before getting to # these calls, so the NameError they raise is not ignored. 'pandas.core.frame.DataFrame.T': [ 'df1_transposed.dtypes', 'df2_transposed.dtypes' ], 'pandas.core.frame.DataFrame.transpose': [ 'df1_transposed.dtypes', 'df2_transposed.dtypes' ], # Skipped because the relies on iloc to set a cell to NA. Test is # replicated in frames_test::DeferredFrameTest::test_applymap. 'pandas.core.frame.DataFrame.applymap': [ 'df_copy.iloc[0, 0] = pd.NA', "df_copy.applymap(lambda x: len(str(x)), na_action='ignore')", ], # Skipped so we don't need to install natsort 'pandas.core.frame.DataFrame.sort_values': [ 'from natsort import index_natsorted', 'df.sort_values(\n' ' by="time",\n' ' key=lambda x: np.argsort(index_natsorted(df["time"]))\n' ')' ], # 1.2.0 (https://github.com/pandas-dev/pandas/issues/35912) 'pandas.core.frame.DataFrame.aggregate': [ "df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))" ], }) self.assertEqual(result.failed, 0) def test_series_tests(self): result = doctests.testmod( pd.core.series, use_beam=False, report=True, wont_implement_ok={ 'pandas.core.series.Series.__array__': ['*'], 'pandas.core.series.Series.array': ['*'], 'pandas.core.series.Series.cummax': ['*'], 'pandas.core.series.Series.cummin': ['*'], 'pandas.core.series.Series.cumsum': ['*'], 'pandas.core.series.Series.cumprod': ['*'], 'pandas.core.series.Series.diff': ['*'], 'pandas.core.series.Series.dot': [ 's.dot(arr)', # non-deferred result ], 'pandas.core.series.Series.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.series.Series.items': ['*'], 'pandas.core.series.Series.iteritems': ['*'], # default keep is 'first' 'pandas.core.series.Series.nlargest': [ "s.nlargest()", "s.nlargest(3)", "s.nlargest(3, keep='last')", ], 'pandas.core.series.Series.memory_usage': ['*'], 'pandas.core.series.Series.nsmallest': [ "s.nsmallest()", "s.nsmallest(3)", "s.nsmallest(3, keep='last')", ], 'pandas.core.series.Series.pop': ['*'], 'pandas.core.series.Series.searchsorted': ['*'], 'pandas.core.series.Series.shift': ['*'], 'pandas.core.series.Series.take': ['*'], 'pandas.core.series.Series.to_dict': ['*'], 'pandas.core.series.Series.unique': ['*'], 'pandas.core.series.Series.unstack': ['*'], 'pandas.core.series.Series.values': ['*'], 'pandas.core.series.Series.view': ['*'], 'pandas.core.series.Series.append': [ 's1.append(s2, ignore_index=True)', ], 'pandas.core.series.Series.sort_index': ['*'], 'pandas.core.series.Series.sort_values': ['*'], 'pandas.core.series.Series.argmax': ['*'], 'pandas.core.series.Series.argmin': ['*'], }, not_implemented_ok={ 'pandas.core.series.Series.transform': ['*'], 'pandas.core.series.Series.groupby': [ 'ser.groupby(["a", "b", "a", "b"]).mean()', 'ser.groupby(["a", "b", "a", np.nan]).mean()', 'ser.groupby(["a", "b", "a", np.nan], dropna=False).mean()', # Grouping by a series is not supported 'ser.groupby(ser > 100).mean()', ], 'pandas.core.series.Series.reindex': ['*'], }, skip={ # error formatting 'pandas.core.series.Series.append': [ 's1.append(s2, verify_integrity=True)', ], # Throws NotImplementedError when modifying df 'pandas.core.series.Series.transform': ['df'], 'pandas.core.series.Series.autocorr': ['*'], 'pandas.core.series.Series.combine': ['*'], 'pandas.core.series.Series.combine_first': ['*'], 'pandas.core.series.Series.compare': ['*'], 'pandas.core.series.Series.cov': [ # Differs in LSB on jenkins. "s1.cov(s2)", ], 'pandas.core.series.Series.drop_duplicates': ['*'], 'pandas.core.series.Series.duplicated': ['*'], 'pandas.core.series.Series.explode': ['*'], 'pandas.core.series.Series.idxmax': ['*'], 'pandas.core.series.Series.idxmin': ['*'], 'pandas.core.series.Series.nonzero': ['*'], 'pandas.core.series.Series.quantile': ['*'], 'pandas.core.series.Series.pop': ['ser'], # testing side effect 'pandas.core.series.Series.repeat': ['*'], 'pandas.core.series.Series.replace': ['*'], 'pandas.core.series.Series.reset_index': ['*'], 'pandas.core.series.Series.searchsorted': [ # This doctest seems to be incorrectly parsed. "x = pd.Categorical(['apple', 'bread', 'bread'," ], 'pandas.core.series.Series.set_axis': ['*'], 'pandas.core.series.Series.to_csv': ['*'], 'pandas.core.series.Series.to_markdown': ['*'], 'pandas.core.series.Series.update': ['*'], 'pandas.core.series.Series.view': [ # Inspection after modification. 's' ], }) self.assertEqual(result.failed, 0) def test_string_tests(self): PD_VERSION = tuple(int(v) for v in pd.__version__.split('.')) if PD_VERSION < (1, 2, 0): module = pd.core.strings else: # Definitions were moved to accessor in pandas 1.2.0 module = pd.core.strings.accessor module_name = module.__name__ result = doctests.testmod( module, use_beam=False, wont_implement_ok={ # These methods can accept deferred series objects, but not lists f'{module_name}.StringMethods.cat': [ "s.str.cat(['A', 'B', 'C', 'D'], sep=',')", "s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')", "s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')" ], f'{module_name}.StringMethods.repeat': [ 's.str.repeat(repeats=[1, 2, 3])' ], f'{module_name}.str_repeat': ['s.str.repeat(repeats=[1, 2, 3])'], f'{module_name}.StringMethods.get_dummies': ['*'], f'{module_name}.str_get_dummies': ['*'], }, skip={ # count() on Series with a NaN produces mismatched type if we # have a NaN-only partition. f'{module_name}.StringMethods.count': ["s.str.count('a')"], f'{module_name}.str_count': ["s.str.count('a')"], # Produce None instead of NaN, see # frames_test.py::DeferredFrameTest::test_str_split f'{module_name}.StringMethods.rsplit': [ 's.str.split(expand=True)', 's.str.rsplit("/", n=1, expand=True)', ], f'{module_name}.StringMethods.split': [ 's.str.split(expand=True)', 's.str.rsplit("/", n=1, expand=True)', ], # Bad test strings in pandas 1.1.x f'{module_name}.str_replace': [ "pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)" ], f'{module_name}.StringMethods.replace': [ "pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)" ], # output has incorrect formatting in 1.2.x f'{module_name}.StringMethods.extractall': ['*'] }) self.assertEqual(result.failed, 0) def test_datetime_tests(self): # TODO(BEAM-10721) datetimelike_result = doctests.testmod( pd.core.arrays.datetimelike, use_beam=False, skip={ 'pandas.core.arrays.datetimelike.AttributesMixin._unbox_scalar': [ '*' ], 'pandas.core.arrays.datetimelike.TimelikeOps.ceil': ['*'], 'pandas.core.arrays.datetimelike.TimelikeOps.floor': ['*'], 'pandas.core.arrays.datetimelike.TimelikeOps.round': ['*'], }) datetime_result = doctests.testmod( pd.core.arrays.datetimes, use_beam=False, skip={ 'pandas.core.arrays.datetimes.DatetimeArray.day': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.hour': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.microsecond': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.minute': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.month': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.nanosecond': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.second': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.year': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_leap_year': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_month_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_month_start': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_quarter_start': [ '*' ], 'pandas.core.arrays.datetimes.DatetimeArray.is_year_end': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.is_year_start': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.to_period': ['*'], 'pandas.core.arrays.datetimes.DatetimeArray.tz_localize': ['*'], }) self.assertEqual(datetimelike_result.failed, 0) self.assertEqual(datetime_result.failed, 0) def test_indexing_tests(self): result = doctests.testmod( pd.core.indexing, use_beam=False, skip={ 'pandas.core.indexing._IndexSlice': ['*'], 'pandas.core.indexing.IndexingMixin.at': ['*'], 'pandas.core.indexing.IndexingMixin.iat': ['*'], 'pandas.core.indexing.IndexingMixin.iloc': ['*'], 'pandas.core.indexing.IndexingMixin.loc': ['*'], 'pandas.core.indexing._AtIndexer': ['*'], 'pandas.core.indexing._LocIndexer': ['*'], 'pandas.core.indexing._iAtIndexer': ['*'], 'pandas.core.indexing._iLocIndexer': ['*'], }) self.assertEqual(result.failed, 0) def test_groupby_tests(self): result = doctests.testmod( pd.core.groupby.groupby, use_beam=False, wont_implement_ok={ 'pandas.core.groupby.groupby.GroupBy.head': ['*'], 'pandas.core.groupby.groupby.GroupBy.tail': ['*'], 'pandas.core.groupby.groupby.GroupBy.nth': ['*'], 'pandas.core.groupby.groupby.GroupBy.cumcount': ['*'], }, not_implemented_ok={ 'pandas.core.groupby.groupby.GroupBy.describe': ['*'], 'pandas.core.groupby.groupby.GroupBy.ngroup': ['*'], 'pandas.core.groupby.groupby.GroupBy.resample': ['*'], 'pandas.core.groupby.groupby.GroupBy.sample': ['*'], 'pandas.core.groupby.groupby.GroupBy.quantile': ['*'], 'pandas.core.groupby.groupby.BaseGroupBy.pipe': ['*'], # pipe tests are in a different location in pandas 1.1.x 'pandas.core.groupby.groupby._GroupBy.pipe': ['*'], 'pandas.core.groupby.groupby.GroupBy.nth': [ "df.groupby('A', as_index=False).nth(1)", ], }, skip={ # Uses iloc to mutate a DataFrame 'pandas.core.groupby.groupby.GroupBy.resample': [ 'df.iloc[2, 0] = 5', 'df', ], # TODO: Raise wont implement for list passed as a grouping column # Currently raises unhashable type: list 'pandas.core.groupby.groupby.GroupBy.ngroup': [ 'df.groupby(["A", [1,1,2,3,2,1]]).ngroup()' ], }) self.assertEqual(result.failed, 0) result = doctests.testmod( pd.core.groupby.generic, use_beam=False, wont_implement_ok={ # Returns an array by default, not a Series. WontImplement # (non-deferred) 'pandas.core.groupby.generic.SeriesGroupBy.unique': ['*'], # TODO: Is take actually deprecated? 'pandas.core.groupby.generic.DataFrameGroupBy.take': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.take': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.nsmallest': [ "s.nsmallest(3, keep='last')", "s.nsmallest(3)", "s.nsmallest()", ], 'pandas.core.groupby.generic.SeriesGroupBy.nlargest': [ "s.nlargest(3, keep='last')", "s.nlargest(3)", "s.nlargest()", ], 'pandas.core.groupby.generic.DataFrameGroupBy.diff': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.diff': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.hist': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], 'pandas.core.groupby.generic.SeriesGroupBy.fillna': [ "df.fillna(method='ffill')", 'df.fillna(value=values, limit=1)', ], }, not_implemented_ok={ 'pandas.core.groupby.generic.DataFrameGroupBy.transform': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.idxmax': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.idxmin': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.filter': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.nunique': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.transform': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.idxmax': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.idxmin': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.filter': ['*'], 'pandas.core.groupby.generic.SeriesGroupBy.describe': ['*'], }, skip={ 'pandas.core.groupby.generic.SeriesGroupBy.cov': [ # Floating point comparison fails 's1.cov(s2)', ], 'pandas.core.groupby.generic.DataFrameGroupBy.cov': [ # Mutates input DataFrame with loc # TODO: Replicate in frames_test.py "df.loc[df.index[:5], 'a'] = np.nan", "df.loc[df.index[5:10], 'b'] = np.nan", "df.cov(min_periods=12)", ], # These examples rely on grouping by a list 'pandas.core.groupby.generic.SeriesGroupBy.aggregate': ['*'], 'pandas.core.groupby.generic.DataFrameGroupBy.aggregate': ['*'], }) self.assertEqual(result.failed, 0) def test_top_level(self): tests = { name: func.__doc__ for (name, func) in pd.__dict__.items() if _is_top_level_function(func) and getattr(func, '__doc__', None) } # IO methods are tested in io_test.py skip_reads = {name: ['*'] for name in dir(pd) if name.startswith('read_')} result = doctests.teststrings( tests, use_beam=False, report=True, not_implemented_ok={ 'concat': ['pd.concat([s1, s2], ignore_index=True)'], 'crosstab': ['*'], 'cut': ['*'], 'eval': ['*'], 'factorize': ['*'], 'get_dummies': ['*'], 'infer_freq': ['*'], 'lreshape': ['*'], 'melt': ['*'], 'merge': ["df1.merge(df2, how='cross')"], 'merge_asof': ['*'], 'pivot': ['*'], 'pivot_table': ['*'], 'qcut': ['*'], 'reset_option': ['*'], 'set_eng_float_format': ['*'], 'set_option': ['*'], 'to_numeric': ['*'], 'to_timedelta': ['*'], 'unique': ['*'], 'value_counts': ['*'], 'wide_to_long': ['*'], }, wont_implement_ok={ 'to_datetime': ['s.head()'], 'to_pickle': ['*'], 'melt': [ "pd.melt(df, id_vars=['A'], value_vars=['B'])", "pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])", "pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B'])", "pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')])", "pd.melt(df, id_vars=['A'], value_vars=['B'],\n" + " var_name='myVarname', value_name='myValname')" ], }, skip={ # error formatting 'concat': ['pd.concat([df5, df6], verify_integrity=True)'], # doctest DeprecationWarning 'melt': ['df'], # Order-sensitive re-indexing. 'merge': [ "df1.merge(df2, left_on='lkey', right_on='rkey')", "df1.merge(df2, left_on='lkey', right_on='rkey',\n" " suffixes=('_left', '_right'))", "df1.merge(df2, how='left', on='a')", ], # Not an actual test. 'option_context': ['*'], 'factorize': ['codes', 'uniques'], # Bad top-level use of un-imported function. 'merge_ordered': [ 'merge_ordered(df1, df2, fill_method="ffill", left_by="group")' ], # Expected error. 'pivot': ["df.pivot(index='foo', columns='bar', values='baz')"], # Never written. 'to_pickle': ['os.remove("./dummy.pkl")'], **skip_reads }) self.assertEqual(result.failed, 0) if __name__ == '__main__': unittest.main()

true

f720120c884a1396999a7662659cbb6bb8cb01bb

811

py

Python

manage.py

oguzhanunlu/validate_json

79cda734934195bd59055d7f04288a7b538f9542

[ "Apache-2.0" ]

null

manage.py

oguzhanunlu/validate_json

79cda734934195bd59055d7f04288a7b538f9542

[ "Apache-2.0" ]

null

manage.py

oguzhanunlu/validate_json

79cda734934195bd59055d7f04288a7b538f9542

[ "Apache-2.0" ]

null

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "validate_json.settings") try: from django.core.management import execute_from_command_line except ImportError: # The above import may fail for some other reason. Ensure that the # issue is really that Django is missing to avoid masking other # exceptions on Python 2. try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

35.26087

77

0.644883

import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "validate_json.settings") try: from django.core.management import execute_from_command_line except ImportError: try: import django except ImportError: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) raise execute_from_command_line(sys.argv)

true

f72012960cef127dbd4634d4f311534488584e40

27,649

py

Python

salt/modules/snapper.py

amaclean199/salt

8aaac011b4616e3c9e74a1daafb4a2146a5a430f

[ "Apache-2.0" ]

12

2015-01-21T00:18:25.000Z

2021-07-11T07:35:26.000Z

salt/modules/snapper.py

amaclean199/salt

8aaac011b4616e3c9e74a1daafb4a2146a5a430f

[ "Apache-2.0" ]

1

2015-10-05T22:03:10.000Z

salt/modules/snapper.py

amaclean199/salt

8aaac011b4616e3c9e74a1daafb4a2146a5a430f

[ "Apache-2.0" ]

12

2015-01-05T09:50:42.000Z

2019-08-19T01:43:40.000Z

# -*- coding: utf-8 -*- ''' Module to manage filesystem snapshots with snapper .. versionadded:: 2016.11.0 :codeauthor: Duncan Mac-Vicar P. <dmacvicar@suse.de> :codeauthor: Pablo Suárez Hernández <psuarezhernandez@suse.de> :depends: ``dbus`` Python module. :depends: ``snapper`` http://snapper.io, available in most distros :maturity: new :platform: Linux ''' from __future__ import absolute_import, unicode_literals, print_function import logging import os import time import difflib try: from pwd import getpwuid HAS_PWD = True except ImportError: HAS_PWD = False from salt.exceptions import CommandExecutionError import salt.utils.files # import 3rd party libs from salt.ext import six try: import dbus # pylint: disable=wrong-import-order HAS_DBUS = True except ImportError: HAS_DBUS = False DBUS_STATUS_MAP = { 1: "created", 2: "deleted", 4: "type changed", 8: "modified", 16: "permission changed", 32: "owner changed", 64: "group changed", 128: "extended attributes changed", 256: "ACL info changed", } SNAPPER_DBUS_OBJECT = 'org.opensuse.Snapper' SNAPPER_DBUS_PATH = '/org/opensuse/Snapper' SNAPPER_DBUS_INTERFACE = 'org.opensuse.Snapper' # pylint: disable=invalid-name log = logging.getLogger(__name__) bus = None system_bus_error = None snapper = None snapper_error = None if HAS_DBUS: try: bus = dbus.SystemBus() except dbus.DBusException as exc: log.warning(exc) system_bus_error = exc else: if SNAPPER_DBUS_OBJECT in bus.list_activatable_names(): try: snapper = dbus.Interface(bus.get_object(SNAPPER_DBUS_OBJECT, SNAPPER_DBUS_PATH), dbus_interface=SNAPPER_DBUS_INTERFACE) except (dbus.DBusException, ValueError) as exc: log.warning(exc) snapper_error = exc else: snapper_error = 'snapper is missing' # pylint: enable=invalid-name def __virtual__(): error_msg = 'The snapper module cannot be loaded: {0}' if not HAS_DBUS: return False, error_msg.format('missing python dbus module') elif not snapper: return False, error_msg.format(snapper_error) elif not bus: return False, error_msg.format(system_bus_error) elif not HAS_PWD: return False, error_msg.format('pwd module not available') return 'snapper' def _snapshot_to_data(snapshot): ''' Returns snapshot data from a D-Bus response. A snapshot D-Bus response is a dbus.Struct containing the information related to a snapshot: [id, type, pre_snapshot, timestamp, user, description, cleanup_algorithm, userdata] id: dbus.UInt32 type: dbus.UInt16 pre_snapshot: dbus.UInt32 timestamp: dbus.Int64 user: dbus.UInt32 description: dbus.String cleaup_algorithm: dbus.String userdata: dbus.Dictionary ''' data = {} data['id'] = snapshot[0] data['type'] = ['single', 'pre', 'post'][snapshot[1]] if data['type'] == 'post': data['pre'] = snapshot[2] if snapshot[3] != -1: data['timestamp'] = snapshot[3] else: data['timestamp'] = int(time.time()) data['user'] = getpwuid(snapshot[4])[0] data['description'] = snapshot[5] data['cleanup'] = snapshot[6] data['userdata'] = {} for key, value in snapshot[7].items(): data['userdata'][key] = value return data def _dbus_exception_to_reason(exc, args): ''' Returns a error message from a snapper DBusException ''' error = exc.get_dbus_name() if error == 'error.unknown_config': return "Unknown configuration '{0}'".format(args['config']) elif error == 'error.illegal_snapshot': return 'Invalid snapshot' else: return exc.get_dbus_name() def list_snapshots(config='root'): ''' List available snapshots CLI example: .. code-block:: bash salt '*' snapper.list_snapshots config=myconfig ''' try: snapshots = snapper.ListSnapshots(config) return [_snapshot_to_data(s) for s in snapshots] except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing snapshots: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def get_snapshot(number=0, config='root'): ''' Get detailed information about a given snapshot CLI example: .. code-block:: bash salt '*' snapper.get_snapshot 1 ''' try: snapshot = snapper.GetSnapshot(config, int(number)) return _snapshot_to_data(snapshot) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while retrieving snapshot: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def list_configs(): ''' List all available configs CLI example: .. code-block:: bash salt '*' snapper.list_configs ''' try: configs = snapper.ListConfigs() return dict((config[0], config[2]) for config in configs) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing configurations: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def _config_filter(value): if isinstance(value, bool): return 'yes' if value else 'no' return value def set_config(name='root', **kwargs): ''' Set configuration values CLI example: .. code-block:: bash salt '*' snapper.set_config SYNC_ACL=True Keys are case insensitive as they will be always uppercased to snapper convention. The above example is equivalent to: .. code-block:: bash salt '*' snapper.set_config sync_acl=True ''' try: data = dict((k.upper(), _config_filter(v)) for k, v in kwargs.items() if not k.startswith('__')) snapper.SetConfig(name, data) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while setting configuration {0}: {1}' .format(name, _dbus_exception_to_reason(exc, locals())) ) return True def _get_last_snapshot(config='root'): ''' Returns the last existing created snapshot ''' snapshot_list = sorted(list_snapshots(config), key=lambda x: x['id']) return snapshot_list[-1] def status_to_string(dbus_status): ''' Converts a numeric dbus snapper status into a string CLI Example: .. code-block:: bash salt '*' snapper.status_to_string <dbus_status> ''' status_tuple = ( dbus_status & 0b000000001, dbus_status & 0b000000010, dbus_status & 0b000000100, dbus_status & 0b000001000, dbus_status & 0b000010000, dbus_status & 0b000100000, dbus_status & 0b001000000, dbus_status & 0b010000000, dbus_status & 0b100000000 ) return [DBUS_STATUS_MAP[status] for status in status_tuple if status] def get_config(name='root'): ''' Retrieves all values from a given configuration CLI example: .. code-block:: bash salt '*' snapper.get_config ''' try: config = snapper.GetConfig(name) return config except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while retrieving configuration: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def create_config(name=None, subvolume=None, fstype=None, template=None, extra_opts=None): ''' Creates a new Snapper configuration name Name of the new Snapper configuration. subvolume Path to the related subvolume. fstype Filesystem type of the subvolume. template Configuration template to use. (Default: default) extra_opts Extra Snapper configuration opts dictionary. It will override the values provided by the given template (if any). CLI example: .. code-block:: bash salt '*' snapper.create_config name=myconfig subvolume=/foo/bar/ fstype=btrfs salt '*' snapper.create_config name=myconfig subvolume=/foo/bar/ fstype=btrfs template="default" salt '*' snapper.create_config name=myconfig subvolume=/foo/bar/ fstype=btrfs extra_opts='{"NUMBER_CLEANUP": False}' ''' def raise_arg_error(argname): raise CommandExecutionError( 'You must provide a "{0}" for the new configuration'.format(argname) ) if not name: raise_arg_error("name") if not subvolume: raise_arg_error("subvolume") if not fstype: raise_arg_error("fstype") if not template: template = "" try: snapper.CreateConfig(name, subvolume, fstype, template) if extra_opts: set_config(name, **extra_opts) return get_config(name) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while creating the new configuration: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def create_snapshot(config='root', snapshot_type='single', pre_number=None, description=None, cleanup_algorithm='number', userdata=None, **kwargs): ''' Creates an snapshot config Configuration name. snapshot_type Specifies the type of the new snapshot. Possible values are single, pre and post. pre_number For post snapshots the number of the pre snapshot must be provided. description Description for the snapshot. If not given, the salt job will be used. cleanup_algorithm Set the cleanup algorithm for the snapshot. number Deletes old snapshots when a certain number of snapshots is reached. timeline Deletes old snapshots but keeps a number of hourly, daily, weekly, monthly and yearly snapshots. empty-pre-post Deletes pre/post snapshot pairs with empty diffs. userdata Set userdata for the snapshot (key-value pairs). Returns the number of the created snapshot. CLI example: .. code-block:: bash salt '*' snapper.create_snapshot ''' if not userdata: userdata = {} jid = kwargs.get('__pub_jid') if description is None and jid is not None: description = 'salt job {0}'.format(jid) if jid is not None: userdata['salt_jid'] = jid new_nr = None try: if snapshot_type == 'single': new_nr = snapper.CreateSingleSnapshot(config, description, cleanup_algorithm, userdata) elif snapshot_type == 'pre': new_nr = snapper.CreatePreSnapshot(config, description, cleanup_algorithm, userdata) elif snapshot_type == 'post': if pre_number is None: raise CommandExecutionError( "pre snapshot number 'pre_number' needs to be" "specified for snapshots of the 'post' type") new_nr = snapper.CreatePostSnapshot(config, pre_number, description, cleanup_algorithm, userdata) else: raise CommandExecutionError( "Invalid snapshot type '{0}'", format(snapshot_type)) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing changed files: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) return new_nr def delete_snapshot(snapshots_ids=None, config="root"): ''' Deletes an snapshot config Configuration name. (Default: root) snapshots_ids List of the snapshots IDs to be deleted. CLI example: .. code-block:: bash salt '*' snapper.delete_snapshot 54 salt '*' snapper.delete_snapshot config=root 54 salt '*' snapper.delete_snapshot config=root snapshots_ids=[54,55,56] ''' if not snapshots_ids: raise CommandExecutionError('Error: No snapshot ID has been provided') try: current_snapshots_ids = [x['id'] for x in list_snapshots(config)] if not isinstance(snapshots_ids, list): snapshots_ids = [snapshots_ids] if not set(snapshots_ids).issubset(set(current_snapshots_ids)): raise CommandExecutionError( "Error: Snapshots '{0}' not found".format(", ".join( [six.text_type(x) for x in set(snapshots_ids).difference( set(current_snapshots_ids))])) ) snapper.DeleteSnapshots(config, snapshots_ids) return {config: {"ids": snapshots_ids, "status": "deleted"}} except dbus.DBusException as exc: raise CommandExecutionError(_dbus_exception_to_reason(exc, locals())) def modify_snapshot(snapshot_id=None, description=None, userdata=None, cleanup=None, config="root"): ''' Modify attributes of an existing snapshot. config Configuration name. (Default: root) snapshot_id ID of the snapshot to be modified. cleanup Change the cleanup method of the snapshot. (str) description Change the description of the snapshot. (str) userdata Change the userdata dictionary of the snapshot. (dict) CLI example: .. code-block:: bash salt '*' snapper.modify_snapshot 54 description="my snapshot description" salt '*' snapper.modify_snapshot 54 description="my snapshot description" salt '*' snapper.modify_snapshot 54 userdata='{"foo": "bar"}' salt '*' snapper.modify_snapshot snapshot_id=54 cleanup="number" ''' if not snapshot_id: raise CommandExecutionError('Error: No snapshot ID has been provided') snapshot = get_snapshot(config=config, number=snapshot_id) try: # Updating only the explicitly provided attributes by the user updated_opts = { 'description': description if description is not None else snapshot['description'], 'cleanup': cleanup if cleanup is not None else snapshot['cleanup'], 'userdata': userdata if userdata is not None else snapshot['userdata'], } snapper.SetSnapshot(config, snapshot_id, updated_opts['description'], updated_opts['cleanup'], updated_opts['userdata']) return get_snapshot(config=config, number=snapshot_id) except dbus.DBusException as exc: raise CommandExecutionError(_dbus_exception_to_reason(exc, locals())) def _get_num_interval(config, num_pre, num_post): ''' Returns numerical interval based on optionals num_pre, num_post values ''' post = int(num_post) if num_post else 0 pre = int(num_pre) if num_pre is not None else _get_last_snapshot(config)['id'] return pre, post def _is_text_file(filename): ''' Checks if a file is a text file ''' type_of_file = os.popen('file -bi {0}'.format(filename), 'r').read() return type_of_file.startswith('text') def run(function, *args, **kwargs): ''' Runs a function from an execution module creating pre and post snapshots and associating the salt job id with those snapshots for easy undo and cleanup. function Salt function to call. config Configuration name. (default: "root") description A description for the snapshots. (default: None) userdata Data to include in the snapshot metadata. (default: None) cleanup_algorithm Snapper cleanup algorithm. (default: "number") `*args` args for the function to call. (default: None) `**kwargs` kwargs for the function to call (default: None) This would run append text to /etc/motd using the file.append module, and will create two snapshots, pre and post with the associated metadata. The jid will be available as salt_jid in the userdata of the snapshot. You can immediately see the changes CLI Example: .. code-block:: bash salt '*' snapper.run file.append args='["/etc/motd", "some text"]' ''' config = kwargs.pop("config", "root") description = kwargs.pop("description", "snapper.run[{0}]".format(function)) cleanup_algorithm = kwargs.pop("cleanup_algorithm", "number") userdata = kwargs.pop("userdata", {}) func_kwargs = dict((k, v) for k, v in kwargs.items() if not k.startswith('__')) kwargs = dict((k, v) for k, v in kwargs.items() if k.startswith('__')) pre_nr = __salt__['snapper.create_snapshot']( config=config, snapshot_type='pre', description=description, cleanup_algorithm=cleanup_algorithm, userdata=userdata, **kwargs) if function not in __salt__: raise CommandExecutionError( 'function "{0}" does not exist'.format(function) ) try: ret = __salt__[function](*args, **func_kwargs) except CommandExecutionError as exc: ret = "\n".join([six.text_type(exc), __salt__[function].__doc__]) __salt__['snapper.create_snapshot']( config=config, snapshot_type='post', pre_number=pre_nr, description=description, cleanup_algorithm=cleanup_algorithm, userdata=userdata, **kwargs) return ret def status(config='root', num_pre=None, num_post=None): ''' Returns a comparison between two snapshots config Configuration name. num_pre first snapshot ID to compare. Default is last snapshot num_post last snapshot ID to compare. Default is 0 (current state) CLI example: .. code-block:: bash salt '*' snapper.status salt '*' snapper.status num_pre=19 num_post=20 ''' try: pre, post = _get_num_interval(config, num_pre, num_post) snapper.CreateComparison(config, int(pre), int(post)) files = snapper.GetFiles(config, int(pre), int(post)) status_ret = {} SUBVOLUME = list_configs()[config]['SUBVOLUME'] for file in files: # In case of SUBVOLUME is included in filepath we remove it # to prevent from filepath starting with double '/' _filepath = file[0][len(SUBVOLUME):] if file[0].startswith(SUBVOLUME) else file[0] status_ret[os.path.normpath(SUBVOLUME + _filepath)] = {'status': status_to_string(file[1])} return status_ret except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing changed files: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def changed_files(config='root', num_pre=None, num_post=None): ''' Returns the files changed between two snapshots config Configuration name. num_pre first snapshot ID to compare. Default is last snapshot num_post last snapshot ID to compare. Default is 0 (current state) CLI example: .. code-block:: bash salt '*' snapper.changed_files salt '*' snapper.changed_files num_pre=19 num_post=20 ''' return status(config, num_pre, num_post).keys() def undo(config='root', files=None, num_pre=None, num_post=None): ''' Undo all file changes that happened between num_pre and num_post, leaving the files into the state of num_pre. .. warning:: If one of the files has changes after num_post, they will be overwritten The snapshots are used to determine the file list, but the current version of the files will be overwritten by the versions in num_pre. You to undo changes between num_pre and the current version of the files use num_post=0. CLI Example: .. code-block:: bash salt '*' snapper.undo ''' pre, post = _get_num_interval(config, num_pre, num_post) changes = status(config, pre, post) changed = set(changes.keys()) requested = set(files or changed) if not requested.issubset(changed): raise CommandExecutionError( 'Given file list contains files that are not present' 'in the changed filelist: {0}'.format(changed - requested)) cmdret = __salt__['cmd.run']('snapper -c {0} undochange {1}..{2} {3}'.format( config, pre, post, ' '.join(requested))) try: components = cmdret.split(' ') ret = {} for comp in components: key, val = comp.split(':') ret[key] = val return ret except ValueError as exc: raise CommandExecutionError( 'Error while processing Snapper response: {0}'.format(cmdret)) def _get_jid_snapshots(jid, config='root'): ''' Returns pre/post snapshots made by a given Salt jid Looks for 'salt_jid' entries into snapshots userdata which are created when 'snapper.run' is executed. ''' jid_snapshots = [x for x in list_snapshots(config) if x['userdata'].get("salt_jid") == jid] pre_snapshot = [x for x in jid_snapshots if x['type'] == "pre"] post_snapshot = [x for x in jid_snapshots if x['type'] == "post"] if not pre_snapshot or not post_snapshot: raise CommandExecutionError("Jid '{0}' snapshots not found".format(jid)) return ( pre_snapshot[0]['id'], post_snapshot[0]['id'] ) def undo_jid(jid, config='root'): ''' Undo the changes applied by a salt job jid The job id to lookup config Configuration name. CLI Example: .. code-block:: bash salt '*' snapper.undo_jid jid=20160607130930720112 ''' pre_snapshot, post_snapshot = _get_jid_snapshots(jid, config=config) return undo(config, num_pre=pre_snapshot, num_post=post_snapshot) def diff(config='root', filename=None, num_pre=None, num_post=None): ''' Returns the differences between two snapshots config Configuration name. filename if not provided the showing differences between snapshots for all "text" files num_pre first snapshot ID to compare. Default is last snapshot num_post last snapshot ID to compare. Default is 0 (current state) CLI Example: .. code-block:: bash salt '*' snapper.diff salt '*' snapper.diff filename=/var/log/snapper.log num_pre=19 num_post=20 ''' try: pre, post = _get_num_interval(config, num_pre, num_post) files = changed_files(config, pre, post) if filename: files = [filename] if filename in files else [] SUBVOLUME = list_configs()[config]['SUBVOLUME'] pre_mount = snapper.MountSnapshot(config, pre, False) if pre else SUBVOLUME post_mount = snapper.MountSnapshot(config, post, False) if post else SUBVOLUME files_diff = dict() for filepath in [filepath for filepath in files if not os.path.isdir(filepath)]: _filepath = filepath if filepath.startswith(SUBVOLUME): _filepath = filepath[len(SUBVOLUME):] # Just in case, removing possible double '/' from the final file paths pre_file = os.path.normpath(pre_mount + "/" + _filepath).replace("//", "/") post_file = os.path.normpath(post_mount + "/" + _filepath).replace("//", "/") if os.path.isfile(pre_file): pre_file_exists = True with salt.utils.files.fopen(pre_file) as rfh: pre_file_content = [salt.utils.stringutils.to_unicode(_l) for _l in rfh.readlines()] else: pre_file_content = [] pre_file_exists = False if os.path.isfile(post_file): post_file_exists = True with salt.utils.files.fopen(post_file) as rfh: post_file_content = [salt.utils.stringutils.to_unicode(_l) for _l in rfh.readlines()] else: post_file_content = [] post_file_exists = False if _is_text_file(pre_file) or _is_text_file(post_file): files_diff[filepath] = { 'comment': "text file changed", 'diff': ''.join(difflib.unified_diff(pre_file_content, post_file_content, fromfile=pre_file, tofile=post_file))} if pre_file_exists and not post_file_exists: files_diff[filepath]['comment'] = "text file deleted" if not pre_file_exists and post_file_exists: files_diff[filepath]['comment'] = "text file created" elif not _is_text_file(pre_file) and not _is_text_file(post_file): # This is a binary file files_diff[filepath] = {'comment': "binary file changed"} if pre_file_exists: files_diff[filepath]['old_sha256_digest'] = __salt__['hashutil.sha256_digest'](''.join(pre_file_content)) if post_file_exists: files_diff[filepath]['new_sha256_digest'] = __salt__['hashutil.sha256_digest'](''.join(post_file_content)) if post_file_exists and not pre_file_exists: files_diff[filepath]['comment'] = "binary file created" if pre_file_exists and not post_file_exists: files_diff[filepath]['comment'] = "binary file deleted" if pre: snapper.UmountSnapshot(config, pre, False) if post: snapper.UmountSnapshot(config, post, False) return files_diff except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while showing differences between snapshots: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def diff_jid(jid, config='root'): ''' Returns the changes applied by a `jid` jid The job id to lookup config Configuration name. CLI Example: .. code-block:: bash salt '*' snapper.diff_jid jid=20160607130930720112 ''' pre_snapshot, post_snapshot = _get_jid_snapshots(jid, config=config) return diff(config, num_pre=pre_snapshot, num_post=post_snapshot) def create_baseline(tag="baseline", config='root'): ''' Creates a snapshot marked as baseline tag Tag name for the baseline config Configuration name. CLI Example: .. code-block:: bash salt '*' snapper.create_baseline salt '*' snapper.create_baseline my_custom_baseline ''' return __salt__['snapper.create_snapshot'](config=config, snapshot_type='single', description="baseline snapshot", cleanup_algorithm="number", userdata={"baseline_tag": tag})

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from __future__ import absolute_import, unicode_literals, print_function import logging import os import time import difflib try: from pwd import getpwuid HAS_PWD = True except ImportError: HAS_PWD = False from salt.exceptions import CommandExecutionError import salt.utils.files from salt.ext import six try: import dbus HAS_DBUS = True except ImportError: HAS_DBUS = False DBUS_STATUS_MAP = { 1: "created", 2: "deleted", 4: "type changed", 8: "modified", 16: "permission changed", 32: "owner changed", 64: "group changed", 128: "extended attributes changed", 256: "ACL info changed", } SNAPPER_DBUS_OBJECT = 'org.opensuse.Snapper' SNAPPER_DBUS_PATH = '/org/opensuse/Snapper' SNAPPER_DBUS_INTERFACE = 'org.opensuse.Snapper' log = logging.getLogger(__name__) bus = None system_bus_error = None snapper = None snapper_error = None if HAS_DBUS: try: bus = dbus.SystemBus() except dbus.DBusException as exc: log.warning(exc) system_bus_error = exc else: if SNAPPER_DBUS_OBJECT in bus.list_activatable_names(): try: snapper = dbus.Interface(bus.get_object(SNAPPER_DBUS_OBJECT, SNAPPER_DBUS_PATH), dbus_interface=SNAPPER_DBUS_INTERFACE) except (dbus.DBusException, ValueError) as exc: log.warning(exc) snapper_error = exc else: snapper_error = 'snapper is missing' def __virtual__(): error_msg = 'The snapper module cannot be loaded: {0}' if not HAS_DBUS: return False, error_msg.format('missing python dbus module') elif not snapper: return False, error_msg.format(snapper_error) elif not bus: return False, error_msg.format(system_bus_error) elif not HAS_PWD: return False, error_msg.format('pwd module not available') return 'snapper' def _snapshot_to_data(snapshot): data = {} data['id'] = snapshot[0] data['type'] = ['single', 'pre', 'post'][snapshot[1]] if data['type'] == 'post': data['pre'] = snapshot[2] if snapshot[3] != -1: data['timestamp'] = snapshot[3] else: data['timestamp'] = int(time.time()) data['user'] = getpwuid(snapshot[4])[0] data['description'] = snapshot[5] data['cleanup'] = snapshot[6] data['userdata'] = {} for key, value in snapshot[7].items(): data['userdata'][key] = value return data def _dbus_exception_to_reason(exc, args): error = exc.get_dbus_name() if error == 'error.unknown_config': return "Unknown configuration '{0}'".format(args['config']) elif error == 'error.illegal_snapshot': return 'Invalid snapshot' else: return exc.get_dbus_name() def list_snapshots(config='root'): try: snapshots = snapper.ListSnapshots(config) return [_snapshot_to_data(s) for s in snapshots] except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing snapshots: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def get_snapshot(number=0, config='root'): try: snapshot = snapper.GetSnapshot(config, int(number)) return _snapshot_to_data(snapshot) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while retrieving snapshot: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def list_configs(): try: configs = snapper.ListConfigs() return dict((config[0], config[2]) for config in configs) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing configurations: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def _config_filter(value): if isinstance(value, bool): return 'yes' if value else 'no' return value def set_config(name='root', **kwargs): try: data = dict((k.upper(), _config_filter(v)) for k, v in kwargs.items() if not k.startswith('__')) snapper.SetConfig(name, data) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while setting configuration {0}: {1}' .format(name, _dbus_exception_to_reason(exc, locals())) ) return True def _get_last_snapshot(config='root'): snapshot_list = sorted(list_snapshots(config), key=lambda x: x['id']) return snapshot_list[-1] def status_to_string(dbus_status): status_tuple = ( dbus_status & 0b000000001, dbus_status & 0b000000010, dbus_status & 0b000000100, dbus_status & 0b000001000, dbus_status & 0b000010000, dbus_status & 0b000100000, dbus_status & 0b001000000, dbus_status & 0b010000000, dbus_status & 0b100000000 ) return [DBUS_STATUS_MAP[status] for status in status_tuple if status] def get_config(name='root'): try: config = snapper.GetConfig(name) return config except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while retrieving configuration: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def create_config(name=None, subvolume=None, fstype=None, template=None, extra_opts=None): def raise_arg_error(argname): raise CommandExecutionError( 'You must provide a "{0}" for the new configuration'.format(argname) ) if not name: raise_arg_error("name") if not subvolume: raise_arg_error("subvolume") if not fstype: raise_arg_error("fstype") if not template: template = "" try: snapper.CreateConfig(name, subvolume, fstype, template) if extra_opts: set_config(name, **extra_opts) return get_config(name) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while creating the new configuration: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def create_snapshot(config='root', snapshot_type='single', pre_number=None, description=None, cleanup_algorithm='number', userdata=None, **kwargs): if not userdata: userdata = {} jid = kwargs.get('__pub_jid') if description is None and jid is not None: description = 'salt job {0}'.format(jid) if jid is not None: userdata['salt_jid'] = jid new_nr = None try: if snapshot_type == 'single': new_nr = snapper.CreateSingleSnapshot(config, description, cleanup_algorithm, userdata) elif snapshot_type == 'pre': new_nr = snapper.CreatePreSnapshot(config, description, cleanup_algorithm, userdata) elif snapshot_type == 'post': if pre_number is None: raise CommandExecutionError( "pre snapshot number 'pre_number' needs to be" "specified for snapshots of the 'post' type") new_nr = snapper.CreatePostSnapshot(config, pre_number, description, cleanup_algorithm, userdata) else: raise CommandExecutionError( "Invalid snapshot type '{0}'", format(snapshot_type)) except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing changed files: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) return new_nr def delete_snapshot(snapshots_ids=None, config="root"): if not snapshots_ids: raise CommandExecutionError('Error: No snapshot ID has been provided') try: current_snapshots_ids = [x['id'] for x in list_snapshots(config)] if not isinstance(snapshots_ids, list): snapshots_ids = [snapshots_ids] if not set(snapshots_ids).issubset(set(current_snapshots_ids)): raise CommandExecutionError( "Error: Snapshots '{0}' not found".format(", ".join( [six.text_type(x) for x in set(snapshots_ids).difference( set(current_snapshots_ids))])) ) snapper.DeleteSnapshots(config, snapshots_ids) return {config: {"ids": snapshots_ids, "status": "deleted"}} except dbus.DBusException as exc: raise CommandExecutionError(_dbus_exception_to_reason(exc, locals())) def modify_snapshot(snapshot_id=None, description=None, userdata=None, cleanup=None, config="root"): if not snapshot_id: raise CommandExecutionError('Error: No snapshot ID has been provided') snapshot = get_snapshot(config=config, number=snapshot_id) try: updated_opts = { 'description': description if description is not None else snapshot['description'], 'cleanup': cleanup if cleanup is not None else snapshot['cleanup'], 'userdata': userdata if userdata is not None else snapshot['userdata'], } snapper.SetSnapshot(config, snapshot_id, updated_opts['description'], updated_opts['cleanup'], updated_opts['userdata']) return get_snapshot(config=config, number=snapshot_id) except dbus.DBusException as exc: raise CommandExecutionError(_dbus_exception_to_reason(exc, locals())) def _get_num_interval(config, num_pre, num_post): post = int(num_post) if num_post else 0 pre = int(num_pre) if num_pre is not None else _get_last_snapshot(config)['id'] return pre, post def _is_text_file(filename): type_of_file = os.popen('file -bi {0}'.format(filename), 'r').read() return type_of_file.startswith('text') def run(function, *args, **kwargs): config = kwargs.pop("config", "root") description = kwargs.pop("description", "snapper.run[{0}]".format(function)) cleanup_algorithm = kwargs.pop("cleanup_algorithm", "number") userdata = kwargs.pop("userdata", {}) func_kwargs = dict((k, v) for k, v in kwargs.items() if not k.startswith('__')) kwargs = dict((k, v) for k, v in kwargs.items() if k.startswith('__')) pre_nr = __salt__['snapper.create_snapshot']( config=config, snapshot_type='pre', description=description, cleanup_algorithm=cleanup_algorithm, userdata=userdata, **kwargs) if function not in __salt__: raise CommandExecutionError( 'function "{0}" does not exist'.format(function) ) try: ret = __salt__[function](*args, **func_kwargs) except CommandExecutionError as exc: ret = "\n".join([six.text_type(exc), __salt__[function].__doc__]) __salt__['snapper.create_snapshot']( config=config, snapshot_type='post', pre_number=pre_nr, description=description, cleanup_algorithm=cleanup_algorithm, userdata=userdata, **kwargs) return ret def status(config='root', num_pre=None, num_post=None): try: pre, post = _get_num_interval(config, num_pre, num_post) snapper.CreateComparison(config, int(pre), int(post)) files = snapper.GetFiles(config, int(pre), int(post)) status_ret = {} SUBVOLUME = list_configs()[config]['SUBVOLUME'] for file in files: _filepath = file[0][len(SUBVOLUME):] if file[0].startswith(SUBVOLUME) else file[0] status_ret[os.path.normpath(SUBVOLUME + _filepath)] = {'status': status_to_string(file[1])} return status_ret except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while listing changed files: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def changed_files(config='root', num_pre=None, num_post=None): return status(config, num_pre, num_post).keys() def undo(config='root', files=None, num_pre=None, num_post=None): pre, post = _get_num_interval(config, num_pre, num_post) changes = status(config, pre, post) changed = set(changes.keys()) requested = set(files or changed) if not requested.issubset(changed): raise CommandExecutionError( 'Given file list contains files that are not present' 'in the changed filelist: {0}'.format(changed - requested)) cmdret = __salt__['cmd.run']('snapper -c {0} undochange {1}..{2} {3}'.format( config, pre, post, ' '.join(requested))) try: components = cmdret.split(' ') ret = {} for comp in components: key, val = comp.split(':') ret[key] = val return ret except ValueError as exc: raise CommandExecutionError( 'Error while processing Snapper response: {0}'.format(cmdret)) def _get_jid_snapshots(jid, config='root'): jid_snapshots = [x for x in list_snapshots(config) if x['userdata'].get("salt_jid") == jid] pre_snapshot = [x for x in jid_snapshots if x['type'] == "pre"] post_snapshot = [x for x in jid_snapshots if x['type'] == "post"] if not pre_snapshot or not post_snapshot: raise CommandExecutionError("Jid '{0}' snapshots not found".format(jid)) return ( pre_snapshot[0]['id'], post_snapshot[0]['id'] ) def undo_jid(jid, config='root'): pre_snapshot, post_snapshot = _get_jid_snapshots(jid, config=config) return undo(config, num_pre=pre_snapshot, num_post=post_snapshot) def diff(config='root', filename=None, num_pre=None, num_post=None): try: pre, post = _get_num_interval(config, num_pre, num_post) files = changed_files(config, pre, post) if filename: files = [filename] if filename in files else [] SUBVOLUME = list_configs()[config]['SUBVOLUME'] pre_mount = snapper.MountSnapshot(config, pre, False) if pre else SUBVOLUME post_mount = snapper.MountSnapshot(config, post, False) if post else SUBVOLUME files_diff = dict() for filepath in [filepath for filepath in files if not os.path.isdir(filepath)]: _filepath = filepath if filepath.startswith(SUBVOLUME): _filepath = filepath[len(SUBVOLUME):] pre_file = os.path.normpath(pre_mount + "/" + _filepath).replace("//", "/") post_file = os.path.normpath(post_mount + "/" + _filepath).replace("//", "/") if os.path.isfile(pre_file): pre_file_exists = True with salt.utils.files.fopen(pre_file) as rfh: pre_file_content = [salt.utils.stringutils.to_unicode(_l) for _l in rfh.readlines()] else: pre_file_content = [] pre_file_exists = False if os.path.isfile(post_file): post_file_exists = True with salt.utils.files.fopen(post_file) as rfh: post_file_content = [salt.utils.stringutils.to_unicode(_l) for _l in rfh.readlines()] else: post_file_content = [] post_file_exists = False if _is_text_file(pre_file) or _is_text_file(post_file): files_diff[filepath] = { 'comment': "text file changed", 'diff': ''.join(difflib.unified_diff(pre_file_content, post_file_content, fromfile=pre_file, tofile=post_file))} if pre_file_exists and not post_file_exists: files_diff[filepath]['comment'] = "text file deleted" if not pre_file_exists and post_file_exists: files_diff[filepath]['comment'] = "text file created" elif not _is_text_file(pre_file) and not _is_text_file(post_file): files_diff[filepath] = {'comment': "binary file changed"} if pre_file_exists: files_diff[filepath]['old_sha256_digest'] = __salt__['hashutil.sha256_digest'](''.join(pre_file_content)) if post_file_exists: files_diff[filepath]['new_sha256_digest'] = __salt__['hashutil.sha256_digest'](''.join(post_file_content)) if post_file_exists and not pre_file_exists: files_diff[filepath]['comment'] = "binary file created" if pre_file_exists and not post_file_exists: files_diff[filepath]['comment'] = "binary file deleted" if pre: snapper.UmountSnapshot(config, pre, False) if post: snapper.UmountSnapshot(config, post, False) return files_diff except dbus.DBusException as exc: raise CommandExecutionError( 'Error encountered while showing differences between snapshots: {0}' .format(_dbus_exception_to_reason(exc, locals())) ) def diff_jid(jid, config='root'): pre_snapshot, post_snapshot = _get_jid_snapshots(jid, config=config) return diff(config, num_pre=pre_snapshot, num_post=post_snapshot) def create_baseline(tag="baseline", config='root'): return __salt__['snapper.create_snapshot'](config=config, snapshot_type='single', description="baseline snapshot", cleanup_algorithm="number", userdata={"baseline_tag": tag})

true

f72013a2dc273fdddd592328a01ea75807d6c262

1,098

py

Python

cli/tests/test_managers/test_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

cli/tests/test_managers/test_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

cli/tests/test_managers/test_run.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2019 Polyaxon, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # coding: utf-8 from __future__ import absolute_import, division, print_function from unittest import TestCase import pytest from polyaxon_sdk import V1Run from polyaxon.managers.run import RunManager @pytest.mark.managers_mark class TestRunManager(TestCase): def test_default_props(self): assert RunManager.IS_GLOBAL is False assert RunManager.IS_POLYAXON_DIR is True assert RunManager.CONFIG_FILE_NAME == ".polyaxonrun" assert RunManager.CONFIG == V1Run

30.5

74

0.762295

from __future__ import absolute_import, division, print_function from unittest import TestCase import pytest from polyaxon_sdk import V1Run from polyaxon.managers.run import RunManager @pytest.mark.managers_mark class TestRunManager(TestCase): def test_default_props(self): assert RunManager.IS_GLOBAL is False assert RunManager.IS_POLYAXON_DIR is True assert RunManager.CONFIG_FILE_NAME == ".polyaxonrun" assert RunManager.CONFIG == V1Run

true

f720140e054f1279701550bef9197a63ff8e51bf

88,490

py

Python

src/neo4j_loader/load_csv_data.py

cebriggs7135/ontology-api

4d2512dcec532cfdfdcd5ff88216e402afbbe2af

[ "MIT" ]

null

src/neo4j_loader/load_csv_data.py

cebriggs7135/ontology-api

4d2512dcec532cfdfdcd5ff88216e402afbbe2af

[ "MIT" ]

null

src/neo4j_loader/load_csv_data.py

cebriggs7135/ontology-api

4d2512dcec532cfdfdcd5ff88216e402afbbe2af

[ "MIT" ]

null

''' Created on Oct 20, 2020 @author: chb69 ''' import sys import os import types import mysql.connector from mysql.connector import errorcode import csv import argparse """ this list includes the prefixes for several informatics resources found in the PheKnowLator mapping data. This might be useful in the future: bto: is BRENDA Tissue ontology (human brain related) fbbt: Flybase caro: Common Anatomy Reference Ontology xao: Frog zfa: Zebrafish ma: mouse anatomy wbbt: Wormbase fao: fungal anatomy ontology https://ncit.nci.nih.gov/ncitbrowser/conceptreport.jsp?dictionary=nci_thesaurus&code=<code> resolves to NCI terms might be able to map these** vsao: Vertebrate Skeletal Anatomy Ontology kupo: Kidney and Urinary Pathway Ontology mp: mouse phenotype emapa: Mouse Developmental Anatomy Ontology caloha:ts- an ontology of human anatomy and human cell types pmid: PubMed mat: Minimal Anatomy Terminology miaa: ??? efo: ??? (found Experimental Factor Ontology but that doesn't look right) ehdaa: Human Developmental Anatomy Ontology vhog: Vertebrate Homologous Ontology Group pba: ??? bams: BAMS Neuroanatomical Ontology mba: ??? ev: eVOC (Expressed Sequence Annotation for Humans) dhba: ??? http://www.snomedbrowser.com/codes/details/<code> resolves to SNOMED terms. We might be able to import these nlxanat: a NIF ontology http://uri.neuinfo.org/nif/nifstd/nlx_anat aao: Amphibian Gross Anatomy Ontology tao: Teleost Anatomy Ontology tgma: Mosquito Gross Anatomy Ontology hao: Hymenoptera Anatomy Ontology """ config = {} def load_config(root_path, filename): '''This method was heavily borrowed from the flask config.py file's from_pyfile method. It reads a file containing python constants and loads it into a dictionary. :param root_path: the path leading to the config file :param filename: the filename of the config relative to the root path. ''' filename = os.path.join(root_path, filename) d = types.ModuleType("config") d.__file__ = filename return_dict = {} try: with open(filename, mode="rb") as config_file: exec(compile(config_file.read(), filename, "exec"), d.__dict__) for config_key in d.__dict__: if str(config_key).startswith('__') == False: return_dict[config_key] = d.__dict__[config_key] except OSError as e: e.strerror = f"Unable to load configuration file ({e.strerror})" raise return return_dict def create_database(config): ''' Create the initial database. This method uses the SQL script found in the TABLE_CREATE_SQL_FILEPATH of the config file to build the database. :param dict config: The configuration settings ''' connection = None try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD']) cursor = connection.cursor() with open(config['TABLE_CREATE_SQL_FILEPATH'], encoding="utf-8") as f: commands = f.read().split(';') for command in commands: if str(command).strip() != "": print('Executing: ' + command) cursor.execute(command) print ("Done creating database tables.") except mysql.connector.Error as err: if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) finally: if connection != None: connection.close() def create_indices(config): ''' Create the indices in the mysql database to improve performance in the transform step. There is a set of default indices that need to be created. These are found in the config parameter INDEX_CREATE_SQL_FILEPATH. After these are created, a series of custom indices need to be added to the various tables created from the other config parameters. :param dict config: The configuration settings ''' connection = None try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME']) cursor = connection.cursor() """ with open(config['INDEX_CREATE_SQL_FILEPATH'], encoding="utf-8") as f: # this code creates the "default" indices commands = f.read().split(';') for command in commands: if str(command).strip() != "": print('Executing: ' + command) cursor.execute(command) """ # the code below creates the indices for the tables created from entries in the # app.cfg file for table_info in config['NODE_METADATA_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_node_label_idx (node_label(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_codeid_idx (codeid(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['EDGE_LIST_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_subject_idx (subject(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_predicate_idx (predicate(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_object_idx (object(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['DBXREF_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD FULLTEXT INDEX {table_name}_dbxrefs_idx (dbxrefs(700))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['RELATIONS_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_relation_id_idx (relation_id(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_relation_label_idx (relation_label(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_inverse_relation_label_idx (inverse_relation_label(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['SYNONYM_LIST_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_synonym_idx (synonym(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) print ("Done creating database indices.") except mysql.connector.Error as err: if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) finally: if connection != None: connection.close() def load_edge_list(config): ''' Load all of the edge_list CSV files into a series of mysql tables. param dict config: the configuration data for this application ''' edge_list_list = config['EDGE_LIST_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in edge_list_list: # walk through the list of edge_list files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, subject VARCHAR(2048) NOT NULL, predicate VARCHAR(2048) NOT NULL, object VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the generic SQL to create the edge_list tables cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) # add the SAB for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_synonym_list(config): ''' Load all of the synonym CSV files into a series of mysql tables. param dict config: the configuration data for this application ''' if 'SYNONYM_LIST_FILE_TABLE_INFO' not in config: #don't run this code if the synonym file is missing return synonym_list = config['SYNONYM_LIST_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in synonym_list: # walk through the list of synonym files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048) NOT NULL, synonym VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the generic SQL to create a synonym table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) # add the SAB for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_relations(config): ''' Load all of the relations CSV files into a series of mysql tables. param dict config: the configuration data for this application ''' node_metadata_list = config['RELATIONS_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: # walk through the list of relations files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, relation_id VARCHAR(2048) NOT NULL, relation_label VARCHAR(2048) NOT NULL, inverse_relation_label VARCHAR(2048), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the generic create relations SQL statement cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def create_missing_codeids(config): node_metadata_list = config['NODE_METADATA_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: table_name = table_data['table_name'] sql = """UPDATE {table_name} SET codeid = REPLACE(REPLACE(ontology_uri, 'http://purl.obolibrary.org/obo/',''), '_', ' ') WHERE codeid IS NULL""".format(table_name=table_name) # add a codeid for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def fix_dbxrefs(config): connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) table_name = 'dbxrefs' sql = """UPDATE {table_name} SET xref = UPPER(xref)""".format(table_name=table_name) # uppercase all dbxrefs data in table cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'NCIT:', 'NCI:') WHERE xref LIKE 'NCIT:%'""".format(table_name=table_name) # convert all the NCI codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'HTTP://WWW.SNOMEDBROWSER.COM/CODES/DETAILS/', 'SNOMEDCT_US:') WHERE xref LIKE 'HTTP://WWW.SNOMEDBROWSER.COM/CODES/DETAILS/%'""".format(table_name=table_name) # convert all the SNOMED codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'MESH:', 'MSH:') WHERE xref LIKE 'MESH:%' AND instr(xref, 'MESH:D') > 0 AND instr(xref, 'MESH:D24') = 0""".format(table_name=table_name) # convert all the MeSH codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, ':', ' ')""".format(table_name=table_name) # replace all remaining colons with spaces dbxrefs data in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_node_metadata(config): ''' Load all of the node_metadata CSV files into a series of mysql tables. param dict config: the configuration data for this application ''' node_metadata_list = config['NODE_METADATA_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: # walk through the list of node_metadata files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048), node_label VARCHAR(2048) NOT NULL, node_definition VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this SQL creates the generic node_metadata table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) # add the SAB for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_dbxref(config): ''' Load all of the dbxref CSV files into a series of mysql tables. param dict config: the configuration data for this application ''' dbxref_list = config['DBXREF_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in dbxref_list: # walk through the list of dbxref files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, dbxrefs VARCHAR(5120) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the SQL to create a generic dbxref table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_umls_codes(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CODEs.csv') table_name = 'umls_codes' load_file(config, file_path, table_name) def load_umls_defs(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'DEFs.csv') table_name = 'umls_defs' load_file(config, file_path, table_name) def load_umls_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'SUIs.csv') table_name = 'umls_suis' load_file(config, file_path, table_name) def load_umls_cuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUIs.csv') table_name = 'umls_cuis' load_file(config, file_path, table_name) def load_umls_tuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'TUIs.csv') table_name = 'umls_tuis' load_file(config, file_path, table_name) def load_umls_code_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CODE-SUIs.csv') table_name = 'umls_code_suis' load_file(config, file_path, table_name) def load_umls_cui_codes(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-CODEs.csv') table_name = 'umls_cui_codes' load_file(config, file_path, table_name) def load_umls_cui_cuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-CUIs.csv') table_name = 'umls_cui_cuis' load_file(config, file_path, table_name) def load_umls_cui_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-SUIs.csv') table_name = 'umls_cui_suis' load_file(config, file_path, table_name) def load_umls_cui_tuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-TUIs.csv') table_name = 'umls_cui_tuis' load_file(config, file_path, table_name) def load_umls_def_rel(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'DEFrel.csv') table_name = 'umls_def_rel' load_file(config, file_path, table_name) def load_umls_tui_rel(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'TUIrel.csv') table_name = 'umls_tui_rel' load_file(config, file_path, table_name) def build_xref_table(config): ''' Build the dbxrefs table by reading the ontology_dbxref table. The ontology_dbxref table contains a column dbxrefs. This method takes dbxrefs, a pipe-delimited list of xrefs, and splits it into separate entries (ex: xref1|xref2|xref3). Each individual xref becomes a new row in the dbxrefs table. :param dict config: The configuration settings ''' dbxref_list = config['DBXREF_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS dbxrefs" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE dbxrefs ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, xref VARCHAR(2048) NOT NULL, PRIMARY KEY(id) );""" cursor.execute(create_table_sql) for table_data in dbxref_list: table_name = table_data['table_name'] sab = table_data['sab'] cursor.execute("SELECT ontology_uri, dbxrefs FROM {table_name}".format(table_name=table_name)) print("Loading {sab} data into table {table_name}".format(table_name="dbxrefs", sab=sab), end='', flush=True) result = cursor.fetchall() record_count = 0 for row in result: ontology_uri = row['ontology_uri'] all_xrefs = row['dbxrefs'] xref_list = all_xrefs.split('|') # For each row in the ontology_dbxref table, split the dbxrefs column into a list for ref in xref_list: # for each xref in the list, insert a new row into the dbxrefs table ref = ref.replace("'","''") sql = "INSERT INTO dbxrefs (ontology_uri, xref) VALUES ('{ontology_uri}','{ref}')".format(ontology_uri=ontology_uri, ref=ref) cursor.execute(sql) record_count = record_count + 1 #commit every 10,000 records if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() print('') # do this to disable the 'end' flag in prior print statements connection.commit() print ("Done loading the {table_name} table.".format(table_name="dbxrefs")) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_file(config, file_path, table_name): ''' Load a CSV or tab-delimited file into a mysql table. param dict config: the configuration data for this application param str file_path: the full path to the CSV or tab-delimited file that will be loaded param str table_name: the name of the table in the database that will contain the data from file_path ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor() record_count = 0 with open(file_path) as csvfile: myCSVReader = None if file_path.endswith('.txt'): # this code determines whether we are loading a CSV or tab-delimited file myCSVReader = csv.DictReader(csvfile, delimiter='\t') else: myCSVReader = csv.DictReader(csvfile) field_names = myCSVReader.fieldnames # the following statements remove some extra columns from the UMLS exported files if 'name_lc' in field_names: field_names.remove('name_lc') if 'REL' in field_names: field_names.remove('REL') if 'RELA' in field_names: field_names.remove('RELA') if (file_path.endswith('CUI-SUIs.csv') or file_path.endswith('CUI-TUIs.csv') or file_path.endswith('DEFrel.csv') or file_path.endswith('TUIrel.csv')): # add a field for type if the UMLS file contains relationship data field_names.append('type') field_list_str = '%s' % ', '.join(map(str, field_names)) # the next two lines "cleanup" the column names from the file into a SQL compliant column name field_list_str = field_list_str.replace(':ID', '') field_list_str = field_list_str.replace(':', '') value_list_str = '' for field in field_names: # Build a list of column names for the insert SQL statement value_list_str += '%({field})s, '.format(field=field) value_list_str = value_list_str[:-2] sql = """INSERT INTO {table_name}({field_list}) VALUE ({value_list})""".format(table_name=table_name, field_list=field_list_str, value_list=value_list_str) print("Loading data from {file_name} into table {table_name}".format(file_name=file_path, table_name=table_name), end='', flush=True) for row in myCSVReader: # for some of the files, specify the 'type' column if file_path.endswith('CUI-SUIs.csv'): row['type'] = 'PREF_TERM' if file_path.endswith('CUI-TUIs.csv'): row['type'] = 'STY' if file_path.endswith('DEFrel.csv'): row['type'] = 'DEF' if file_path.endswith('TUIrel.csv'): row['type'] = 'ISA_STY' # use row directly when csv headers match column names. # remove data from a row if the column header is None if table_name == 'suis': if None in row.keys(): row.pop(None) if None in row.keys(): row.pop(None) cursor.execute(sql, row) record_count = record_count + 1 #commit every 200,000 records if record_count % 200000 == 0: print('.', end='', flush=True) connection.commit() print('') # do this to disable the 'end' flag in prior print statements connection.commit() print ("Done loading the {table_name} table.".format(table_name=table_name)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def extract_non_umls(config): load_node_metadata(config) load_relations(config) load_dbxref(config) load_edge_list(config) load_synonym_list(config) # This code is temporary. It should be moved to a pre-processing step create_missing_codeids(config) # END This code is temporary. It should be moved to a pre-processing step def extract(config): ''' The extract method loads the CSV and tab-delimited files into mysql tables mirroring their file structure. param dict config: The configuration data for this application ''' create_database(config) load_node_metadata(config) load_relations(config) load_dbxref(config) load_edge_list(config) load_synonym_list(config) load_umls_codes(config) load_umls_defs(config) load_umls_suis(config) load_umls_cuis(config) load_umls_tuis(config) load_umls_cui_codes(config) load_umls_code_suis(config) load_umls_cui_cuis(config) load_umls_cui_suis(config) load_umls_cui_tuis(config) load_umls_def_rel(config) load_umls_tui_rel(config) # This code is temporary. It should be moved to a pre-processing step create_missing_codeids(config) # END This code is temporary. It should be moved to a pre-processing step create_indices(config) print("Done with extract process") def build_ambiguous_codes_table(config): ''' Construct a table called temp_ambiguous_codes (ontology_uri, codeid). This table contains a subset of the codes that map to more than one CUI. These codes are "ambiguous" because we cannot use them in our automated processing. Our code cannot decide which of the CUIs should be assigned the preferred term from the data we are loading. Also, these connections tend to conflate items (ex: left hand, right hand, and hand are all the same). We will use this table to "filter out" some of the data during the ETL process. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS temp_ambiguous_codes" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE temp_ambiguous_codes ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table temp_ambiguous_codes") sql = """INSERT INTO temp_ambiguous_codes (ontology_uri, codeid) SELECT DISTINCT ontology_uri, xref as codeid FROM dbxrefs, umls_cui_codes as rel WHERE xref = rel.end_id GROUP BY ontology_uri, xref HAVING COUNT(DISTINCT rel.start_id) > 1""" """This query builds the temp_ambiguous_codes table. It inserts codes with more than 1 CUI into the temp_ambiguous_codes table. """ cursor.execute(sql) connection.commit() print("Loaded codes into table temp_ambiguous_codes") except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def temp_build_ccf_code_cui_table(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS temp_ccf_cui_codes" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE temp_ccf_cui_codes ( id INT NOT NULL AUTO_INCREMENT, codeid VARCHAR(2048) NOT NULL, cui VARCHAR(2048), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table temp_ccf_cui_codes") sql = "ALTER TABLE temp_ccf_cui_codes ADD INDEX temp_ccf_cui_codes_codeid(codeid(50))" cursor.execute(sql) sql = "ALTER TABLE temp_ccf_cui_codes ADD INDEX temp_ccf_cui_codes_cui_idx(cui(50))" cursor.execute(sql) cursor = connection.cursor() record_count = 0 file_path = '/home/chb69/umls_data/ccf/CCF-CUI.csv' with open(file_path) as csvfile: myCSVReader = None if file_path.endswith('.txt'): # this code determines whether we are loading a CSV or tab-delimited file myCSVReader = csv.DictReader(csvfile, delimiter='\t') else: myCSVReader = csv.DictReader(csvfile) field_names = myCSVReader.fieldnames #a.name,b.CodeID,c.CUI,d.name print("Loading data from {file_name} into table {table_name}".format(file_name=file_path, table_name='temp_ccf_cui_codes'), end='', flush=True) for row in myCSVReader: sql = "INSERT INTO temp_ccf_cui_codes (codeid, cui) VALUES ('{codeid}','{cui}')".format(codeid=row['b.CodeID'],cui=row['c.CUI']) cursor.execute(sql) connection.commit() print ("Done loading the {table_name} table.".format(table_name="temp_ccf_cui_codes")) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def build_ontology_uri_to_umls_map_table(config): ''' Construct a table called ontology_uri_map (ontology_uri, cui, codeid, type, sab). This table is a mapping between the dbxref data and the UMLS data. The table is built from dbxrefs and cui_codes (ULMS) tables. The ontology_uri is the primary key within the dbxref data. The cui and codeid are the main keys within the UMLS data. Each record in ontology_uri_map allows one to move between both systems. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS ontology_uri_map" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE ontology_uri_map ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, cui VARCHAR(2048), codeid VARCHAR(2048), type VARCHAR(50), mapping_type VARCHAR(50), sab VARCHAR(50), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table ontology_uri_map") sql = """INSERT INTO ontology_uri_map (ontology_uri, cui) SELECT DISTINCT ontology_uri, substr(xref,6) as CUI FROM dbxrefs WHERE xref LIKE 'UMLS%'""" # This query loads all the ontology_uri's that map directly to a UMLS CUI according to the dbxrefs table # these records will have their codeid column set to NULL cursor.execute(sql) connection.commit() print("Loaded UMLS map into table ontology_uri_map") sql = """INSERT INTO ontology_uri_map (ontology_uri, codeid, cui, type, sab) SELECT DISTINCT ontology_uri, xref as codeid, rel.start_id as cui, 'PT' as type, substring_index(xref,' ', 1) as sab FROM dbxrefs, umls_cui_codes as rel WHERE xref = rel.end_id AND (ontology_uri, xref) NOT IN (SELECT ontology_uri,codeid FROM temp_ambiguous_codes)""" # This query loads all the ontology_uri's that map to a code according to the dbxrefs table cursor.execute(sql) connection.commit() print("Loaded map into table ontology_uri_map") # add indices after loading to speed up the load sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_ontology_uri_idx(ontology_uri(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_cui_idx(cui(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_codeid_idx(codeid(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_type_idx(type(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_mapping_type_idx(mapping_type(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_sab_idx(sab(50))" cursor.execute(sql) print("Built indices for table ontology_uri_map") sql = """UPDATE ontology_uri_map SET mapping_type = 'PRIMARY' where codeid is null AND ontology_uri IN ( SELECT ontology_uri from (SELECT ontology_uri FROM ontology_uri_map where codeid is null group by ontology_uri having count(distinct cui) = 1) as table_one)""" # This query sets all the PRIMARY CUIs cursor.execute(sql) connection.commit() print("Loaded PRIMARY CUI map data into table ontology_uri_map") except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def build_relations_table(config): ''' Create a new table called relations. This table will contains a superset of all relations loaded so far. After this table is loaded, UPDATE it to add the inverse relations (if necessary). param dict config: the configuration data for this application ''' relations_table_info = config['RELATIONS_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS relations" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE relations ( id INT NOT NULL AUTO_INCREMENT, relation_id VARCHAR(2048) NOT NULL, relation_label VARCHAR(2048) NOT NULL, inverse_relation_label VARCHAR(2048), sab VARCHAR(50), PRIMARY KEY(id));""" # step 1: create the new relations table cursor.execute(create_table_sql) print("Created table relations") for table_info in relations_table_info: # step 2: for each entry in the RELATIONS_FILE_TABLE_INFO config entry, # insert the data from the table referenced by RELATIONS_FILE_TABLE_INFO into the relations table table_name = table_info['table_name'] sab = table_info['sab'] sql = """INSERT INTO relations (relation_id, relation_label, inverse_relation_label, sab) SELECT relation_id, relation_label, inverse_relation_label, '{sab}' FROM {table_name}""".format(table_name=table_name, sab=sab) cursor.execute(sql) connection.commit() print("Loaded {sab} relations data into table relations".format(sab=sab)) sql = """UPDATE relations r1 LEFT JOIN relations r2 ON r1.relation_id = r2.relation_id SET r1.inverse_relation_label = CONCAT('inverse ', r2.relation_label) WHERE r2.inverse_relation_label IS NULL""" """After the 'normal' or 'forward' relations are loaded, find any records in the relations table that have inverse_relation_label set to NULL. For each record missing an inverse_relation_label, create an inverse_relation_label equal to 'inverse ' + relation_label """ cursor.execute(sql) connection.commit() print("Added inverse relations for {sab} data into table relations".format(sab=sab)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_cui_cui_relations(config): ''' Extract all relationships between two UMLS CUIs found in the PheKnowLator data. This method only inserts data into the cui_cuis table. It does not create new CUIs. It adds both the "regular" relations plus their inverse relations. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) edge_list_file_info = config['EDGE_LIST_FILE_TABLE_INFO'] for edge_list_info in edge_list_file_info: # walk through all the existing edge_list tables and load the data into the # umls_cui_cuis table sab = edge_list_info['sab'] table_name = edge_list_info['table_name'] sql = """DELETE FROM umls_cui_cuis WHERE sab = '{sab}'""".format(sab=sab) cursor.execute(sql) connection.commit() print('') print("Deleted {sab} map from table umls_cui_cuis".format(sab=sab)) sql = """INSERT INTO umls_cui_cuis (start_id, type, end_id, sab) SELECT DISTINCT subject_table.cui as start_id, lower(replace(rel.relation_label,' ','_')) as type, object_table.cui as end_id, 'UBERON' as sab FROM {table_name} el, relations rel, ontology_uri_map subject_table, ontology_uri_map object_table WHERE rel.relation_id = el.predicate AND subject_table.ontology_uri = el.subject AND subject_table.mapping_type = 'PRIMARY' AND object_table.ontology_uri = el.object AND object_table.mapping_type = 'PRIMARY' AND subject_table.cui != object_table.cui AND el.sab = '{sab}'""".format(table_name=table_name,sab=sab) """ This query needs some explanation. Basically, the edge_list table is the central table in the query. We use the edge_list table structure (subject, predicate, object) to find records where the edge_list contains relationships between the subject CUI and the object CUI. This record will become a new relationship between 2 CUIs. Lastly, we map from the edge_list relation_id to the "English" relation_label. We replace the spaces in the relation_label with underscores ('_'). This becomes the label for the relationship in the CUI to CUI relationship. """ cursor.execute(sql) connection.commit() print("Loaded {sab} map into table umls_cui_cuis".format(sab=sab)) sql = """INSERT INTO umls_cui_cuis (start_id, type, end_id, sab) SELECT DISTINCT object_table.cui as start_id, lower(replace(rel.inverse_relation_label,' ','_')) as type, subject_table.cui as end_id, 'UBERON' as sab FROM {table_name} el, relations rel, ontology_uri_map subject_table, ontology_uri_map object_table WHERE rel.relation_id = el.predicate AND subject_table.ontology_uri = el.subject AND subject_table.mapping_type = 'PRIMARY' AND object_table.ontology_uri = el.object AND object_table.mapping_type = 'PRIMARY' AND subject_table.cui != object_table.cui AND rel.inverse_relation_label IS NOT NULL AND el.sab = '{sab}'""".format(table_name=table_name,sab=sab) """ This query is basically the same as the initial query above, but there are two important differences: - the relationship used is the inverse_relation_label from the pkl_relations table. - the subject and object are swapped since we are creating the inverse relationship """ cursor.execute(sql) connection.commit() print("Loaded {sab} inverse relation map into table umls_cui_cuis".format(sab=sab)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_terms(config): ''' The method creates new labels (Term nodes) in the graph for each node_metadata table. Adding a Term node affects several tables: suis, code_suis, cui_suis, and new_sui_map. The new_sui_map does not represent data in the graph, it merely tracks minted SUIs between application runs to avoid changing the SUI and losing its connection to the UMLS codes. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE suis_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE code_suis_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE new_sui_map" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = """TRUNCATE cui_suis_updated""" cursor.execute(truncate_table_sql) connection.commit() print ("Copying cui_suis INTO cui_suis_updated") sql = """INSERT INTO cui_suis_updated SELECT * FROM umls_cui_suis""" cursor.execute(sql) connection.commit() sql = "DROP INDEX suis_updated_sui_idx ON suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX suis_updated_name_idx ON suis_updated" cursor.execute(sql) connection.commit() print ("Copying suis INTO suis_updated") sql = """INSERT INTO suis_updated SELECT * FROM umls_suis""" cursor.execute(sql) connection.commit() sql = "ALTER TABLE suis_updated ADD INDEX suis_updated_sui_idx (sui(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE suis_updated ADD INDEX suis_updated_name_idx (name(500))" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_start_id_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_end_id_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_type_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_cui_idx ON code_suis_updated" cursor.execute(sql) connection.commit() print ("Copying code_suis INTO code_suis_updated") sql = """INSERT INTO code_suis_updated SELECT * FROM umls_code_suis""" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_start_id_idx (start_id(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_end_id_idx (end_id(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_type_idx (type(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_cui_idx (cui(100))" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 # start SUI numbering at one for table_info in node_metadata_info: # for each entry in the NODE_METADATA_FILE_TABLE_INFO config entry, query the node_metadata # table and find all missing terms. Then add the missing terms to the appropriate database tables table_name = table_info['table_name'] sab = table_info['sab'] dict_new_suis = {} """ keep an in-memory list of the new SUIs generated The SQL includes a list of existing SUIs when it is initially executed. During execution, new SUIs are created but they are missing from the ones retrieved by the SQL (i.e. a "dirty read"). Therefore, the new SUIs are not found and will create duplicate SUIs with the same labels. This in-memory list provides lookup services to avoid recreating the labels.""" sql = """SELECT oum.ontology_uri as ontology_uri, oum.cui AS cui, IFNULL(oum.codeid,nm.codeid) AS codeid, nm.node_label AS label, '{sab}' as sab, su.sui AS sui, 'PT' AS term_type FROM {table_name} nm INNER JOIN ontology_uri_map oum ON nm.ontology_uri = oum.ontology_uri AND oum.mapping_type = 'PRIMARY' LEFT OUTER JOIN suis_updated su ON nm.node_label = su.name WHERE oum.codeid is null OR oum.codeid NOT IN (select start_id FROM code_suis_updated)""".format(table_name=table_name,sab=sab) """This query joins the ontology_uri_map data to the label from the node_metadata table. The query only returns records where the codeid is NULL or the codeid is missing from the code_suis_updated table. These represent records that need a new SUI minted.""" cursor.execute(sql) result = cursor.fetchall() print("") print ("Loading tables suis_updated, code_suis_updated, and new_sui_map for SAB: {sab}".format(sab=sab), end='', flush=True) for row in result: ontology_uri = row['ontology_uri'] cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] label = row['label'] term_type = row['term_type'] sui = row['sui'] if sui == None: if label in dict_new_suis.keys(): # if the label already exists, then use the existing SUI sui = dict_new_suis[label] else: # if the label does not exist, then mint a new SUI sui = 'HS' + str(record_count).zfill(6) # mint a new SUI prefixed with 'HS' sql = """INSERT INTO suis_updated (sui, name) VALUES ('{sui}',"{name}")""".format(sui=sui,name=label) cursor.execute(sql) sql = """INSERT INTO new_sui_map (codeid, sui, name) VALUES ('{codeid}','{sui}',"{name}")""".format(codeid=codeid,sui=sui,name=label) cursor.execute(sql) dict_new_suis[label] = sui # add the new SUI to the in memory list sql = """INSERT INTO code_suis_updated (start_id, end_id, type, cui) VALUES ('{codeid}','{sui}','{term_type}','{cui}')""".format(codeid=codeid,sui=sui,cui=cui,term_type=term_type) cursor.execute(sql) if 'HC' in cui and term_type == 'PT': #insert a new HCUI into the cui_suis_updated table since it does not exist in the table yet. sql = """INSERT INTO cui_suis_updated (start_id, end_id, type) VALUES ('{cui}','{sui}','PREF_TERM')""".format(cui=cui,sui=sui) cursor.execute(sql) record_count = record_count + 1 #commit every 10,000 records if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() print('') insert_new_synonyms(config, record_count) # after the for loop completes, add all the synonymous terms. This is done outside of the for loop # because there is not necessarily a 1 to 1 relationship between the node_metadata entries and the synoymous files. # However, there is a dependency because the insert_new_synonym method needs to continue the SUI numbering. # This method is executed after the commit because then we do not need to worry about a situation where some of the # terms have not yet been committed to the database. except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_synonyms(config, record_count): ''' The method creates new labels (Term nodes) in the graph for each synonym table. This method is basically identical to the insert_new_terms method. The only differences are the a) the config entry used (this uses SYNONYM_LIST_FILE_TABLE_INFO) and b) SQL used to find the synonyms Adding a Term node affects several tables: suis, code_suis, cui_suis, and new_sui_map. The new_sui_map does not represent data in the graph, it merely tracks minted SUIs between application runs to avoid changing the SUI and losing its connection to the UMLS codes. param dict config: The configuration data for this application. ''' if 'SYNONYM_LIST_FILE_TABLE_INFO' not in config: #skip this method if there are no synonym files defined return synonym_list = config['SYNONYM_LIST_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_info in synonym_list: sab = table_info['sab'] table_name = table_info['table_name'] dict_new_suis = {} """ keep an in-memory list of the new SUIs generated The SQL includes a list of existing SUIs when it is initially executed. During execution, new SUIs are created but they are missing from the ones retrieved by the SQL (i.e. a "dirty read"). Therefore, the new SUIs are not found and will create duplicate SUIs with the same labels. This in-memory list provides lookup services to avoid recreating the labels.""" sql = """SELECT DISTINCT oum.ontology_uri as ontology_uri, oum.cui AS cui,nm.codeid AS codeid, nm.synonym AS label, '{sab}' as sab, su.sui AS sui, 'SY' AS term_type FROM {table_name} nm INNER JOIN ontology_uri_map oum ON nm.ontology_uri = oum.ontology_uri LEFT OUTER JOIN suis_updated su ON nm.synonym = su.name""".format(table_name=table_name,sab=sab) """This query joins the ontology_uri_map data to the label from the node_metadata table. The query only returns records where the codeid is NULL or the codeid is missing from the code_suis_updated table. These represent records that need a new SUI minted.""" cursor.execute(sql) result = cursor.fetchall() print ("Loading tables suis_updated, code_suis_updated, and new_sui_map for SAB: {sab}".format(sab=sab), end='', flush=True) for row in result: ontology_uri = row['ontology_uri'] cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] label = row['label'] term_type = row['term_type'] sui = row['sui'] if sui == None: if label in dict_new_suis.keys(): # if the label already exists, then use the existing SUI sui = dict_new_suis[label] else: # if the label does not exist, then mint a new SUI sui = 'HS' + str(record_count).zfill(6) # mint a new SUI prefixed with 'HS' sql = """INSERT INTO suis_updated (sui, name) VALUES ('{sui}',"{name}")""".format(sui=sui,name=label) cursor.execute(sql) sql = """INSERT INTO new_sui_map (codeid, sui, name) VALUES ('{codeid}','{sui}',"{name}")""".format(codeid=codeid,sui=sui,name=label) cursor.execute(sql) dict_new_suis[label] = sui # add the new SUI to the in memory list sql = """INSERT INTO code_suis_updated (start_id, end_id, type, cui) VALUES ('{codeid}','{sui}','{term_type}','{cui}')""".format(codeid=codeid,sui=sui,cui=cui,term_type=term_type) cursor.execute(sql) record_count = record_count + 1 #commit every 10,000 records if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_cuis(config): ''' Find every entry in the node_metadata tables that is missing from the ontology_uri_map table. This indicates a record that was not mapped to any existing UMLS code. This means the record needs a new CUI minted for it. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE cuis_updated" cursor.execute(truncate_table_sql) connection.commit() print ("Truncating cui_codes_updated") sql = """TRUNCATE cui_codes_updated""" cursor.execute(sql) connection.commit() print ("Copying cuis INTO cuis_updated") sql = """INSERT INTO cuis_updated SELECT * FROM umls_cuis""" cursor.execute(sql) connection.commit() print ("Deleting HuBMAP CUIs") sql = """DELETE FROM ontology_uri_map WHERE cui LIKE 'HC%'""" cursor.execute(sql) connection.commit() print ("Copying cuis INTO cui_codes_updated") sql = """INSERT INTO cui_codes_updated SELECT * FROM umls_cui_codes""" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 # start HCUI numbering at one print ("Creating new HCUI's and codes") for table_info in node_metadata_info: sab = table_info['sab'] table_name = table_info['table_name'] print ("Deleting {sab} codes from umls_codes".format(sab=sab)) sql = """DELETE FROM umls_codes WHERE sab = '{sab}'""".format(sab=sab) # remove old records for the sab cursor.execute(sql) connection.commit() print("Loading node metadata for {sab}".format(sab=sab)) sql = """SELECT ontology_uri AS ontology_uri, codeid AS codeid, sab AS sab FROM {table_name} nm WHERE nm.ontology_uri NOT IN (SELECT ontology_uri FROM ontology_uri_map WHERE mapping_type = 'PRIMARY')""".format(table_name=table_name) """Find all the records in the current node_metadata table that were not mapped to an UMLS terms.""" cursor.execute(sql) result = cursor.fetchall() for row in result: ontology_uri = row['ontology_uri'] cui = 'HC' + str(record_count).zfill(6) # mint a new CUI using the HC prefix record_count = record_count + 1 current_sab = sab codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] sql = """INSERT INTO ontology_uri_map (ontology_uri,codeid,cui,sab,mapping_type) VALUES ('{ontology_uri}','{codeid}','{cui}','{sab}','PRIMARY')""".format(codeid=codeid,cui=cui,ontology_uri=ontology_uri,sab=current_sab) # add the new HCUI to the ontology_uri_map cursor.execute(sql) sql = """INSERT INTO cuis_updated (cui) VALUES ('{cui}')""".format(cui=cui) # add the new HCUI to the cuis_updated table cursor.execute(sql) connection.commit() sql = """INSERT INTO umls_codes (codeid, sab,code) VALUES ('{codeid}','{sab}','{code}')""".format(codeid=codeid,sab=current_sab,code=code) # add the new Code information to umls_codes cursor.execute(sql) connection.commit() sql = """INSERT INTO cui_codes_updated (start_id, end_id) VALUES ('{cui}','{codeid}')""".format(cui=cui,codeid=codeid) # connect the new HCUI to its new Code cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_codes(config): ''' Create the new codes in the graph. This code creates new codes plus connects them to the appropriate CUIs. Note: By the time this code executes, the insert_new_cuis method should have already inserted. So this code does not need to insert them. This also means this method is dependent upon the insert_new_cuis method. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] for table_info in node_metadata_info: table_name = table_info['table_name'] current_sab = table_info['sab'] sql = """SELECT nm.ontology_uri as ontology_uri, nm.codeid as codeid, oum.cui as cui, nm.sab as sab FROM {table_name} nm, ontology_uri_map oum WHERE oum.ontology_uri = nm.ontology_uri and oum.codeid IS NOT NULL and nm.codeid not in (select codeid from umls_codes)""".format(table_name=table_name) # this SQL finds all the codes in the current node_metadata missing from the umls_codes table # these are the codes we need to add cursor.execute(sql) result = cursor.fetchall() print ("Creating new codes for sab: {sab}".format(sab=current_sab)) for row in result: cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] sql = """INSERT INTO umls_codes (codeid, sab,code) VALUES ('{codeid}','{sab}','{code}')""".format(codeid=codeid,sab=current_sab,code=code) cursor.execute(sql) connection.commit() sql = """INSERT INTO cui_codes_updated (start_id, end_id) VALUES ('{cui}','{codeid}')""".format(cui=cui,codeid=codeid) cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_defs(config): ''' Add the defintions from the PHeKnowLator data for the UBERON and CL nodes. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE defs_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE def_rel_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE new_def_map" cursor.execute(truncate_table_sql) connection.commit() print("") print ("Copying defs INTO defs_updated") sql = """INSERT INTO defs_updated SELECT * FROM umls_defs""" cursor.execute(sql) connection.commit() print ("Copying def_rel INTO def_rel_updated") sql = """INSERT INTO def_rel_updated SELECT * FROM umls_def_rel""" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 # start SUI numbering at one for table_info in node_metadata_info: table_name = table_info['table_name'] sab = table_info['sab'] sql = """SELECT oum.cui, nm.node_definition, '{sab}' as sab FROM {table_name} nm, ontology_uri_map oum WHERE nm.ontology_uri = oum.ontology_uri AND oum.mapping_type = 'PRIMARY' AND node_definition <> 'None' AND node_definition <> '.'""".format(table_name=table_name,sab=sab) cursor.execute(sql) result = cursor.fetchall() print("") print ("Loading tables defs_updated, def_rels_updated, and new_def_map", end='', flush=True) for row in result: cui = row['cui'] node_definition = row['node_definition'] sab = row['sab'] atui = 'HAT' + str(record_count).zfill(6) record_count = record_count + 1 if '"' in node_definition: node_definition = node_definition.replace('"','\\"') sql = """INSERT INTO defs_updated (atui, sab, def) VALUES ('{atui}','{sab}',"{node_definition}")""".format(atui=atui,sab=sab,node_definition=node_definition) cursor.execute(sql) sql = """INSERT INTO def_rel_updated (start_id, end_id, type, sab) VALUES ('{cui}','{atui}','DEF','{sab}')""".format(atui=atui,sab=sab,cui=cui) cursor.execute(sql) sql = """INSERT INTO new_def_map (cui, atui, node_definition, sab) VALUES ('{cui}','{atui}',"{node_definition}", '{sab}')""".format(atui=atui,sab=sab,cui=cui,node_definition=node_definition) cursor.execute(sql) #commit every 10,000 records if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def transform(config): ''' This coordinates the transform methods. param dict config: The configuration data for this application. ''' build_xref_table(config) # This code is temporary. It should be moved to a pre-processing step fix_dbxrefs(config) # END This code is temporary. It should be moved to a pre-processing step build_ambiguous_codes_table(config) build_ontology_uri_to_umls_map_table(config) build_relations_table(config) insert_new_cuis(config) insert_new_codes(config) insert_new_terms(config) insert_new_defs(config) insert_new_cui_cui_relations(config) print('') # do this to disable the 'end' flag in prior print statements print("Done with transform process") def load(config): ''' This method initiates the .CSV file export process. param dict config: The configuration data for this application. ''' export_files(config) print('') # do this to disable the 'end' flag in prior print statements print("Done with load process") def export_files(config): ''' This method walks through the subset of mysql tables and generates a sets of .CSV files. These .CSV files adhere to the Neo4j 'CSV file header format' found here: https://neo4j.com/docs/operations-manual/current/tools/import/file-header-format/ This method matches the mysql table with a file_name and manages any column header adjustments that need to be made. param dict config: The configuration data for this application. ''' connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) export_table_info = [{'table_name': 'umls_codes', 'file_name':'CODEs.csv','sql_columns':['codeid','sab','code'],'file_columns':['CodeID:ID','SAB','CODE']}, {'table_name': 'umls_tui_rel', 'file_name':'TUIrel.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'umls_cui_tuis', 'file_name':'CUI-TUIs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'umls_cui_cuis', 'file_name':'CUI-CUIs.csv','sql_columns':['start_id','end_id','type','sab'],'file_columns':[':START_ID',':END_ID',':TYPE','SAB']}, {'table_name': 'cui_codes_updated', 'file_name':'CUI-CODEs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'code_suis_updated', 'file_name':'CODE-SUIs.csv','sql_columns':['start_id','end_id','type','cui'],'file_columns':[':START_ID',':END_ID',':TYPE','CUI']}, {'table_name': 'cui_suis_updated', 'file_name':'CUI-SUIs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'cuis_updated', 'file_name':'CUIs.csv','sql_columns':['cui'],'file_columns':['CUI:ID']}, {'table_name': 'suis_updated', 'file_name':'SUIs.csv','sql_columns':['sui','name'],'file_columns':['SUI:ID','name']}, {'table_name': 'umls_tuis', 'file_name':'TUIs.csv','sql_columns':['tui','name','stn','def'],'file_columns':['TUI:ID','name','STN','DEF']}, {'table_name': 'defs_updated', 'file_name':'DEFs.csv','sql_columns':['atui','sab','def'],'file_columns':['ATUI:ID','SAB','DEF']}, {'table_name': 'def_rel_updated', 'file_name':'DEFrel.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}] ''' This method walks through the subset of mysql tables found in the export_table_info variable. Each entry in export_table_info contains: table_name: the mysql table name to export file_name: the name of the .CSV file to be generated sql_columns: a list of the columns to be includes in the SELECT statement file_columns: a list of the column headers to use when writing the data to the .CSV files The sql_columns and file_columns should map 1:1. For example in the table_name umls_codes and file_name CODEs.csv entry: codeid SQL column becomes -> CodeID:ID in the .CSV file sab SQL column becomes -> SAB in the .CSV file code SQL column becomes -> CODE in the .CSV file ''' for export_info in export_table_info: # walk through all the entries in the export_table_info list table_name = export_info['table_name'] file_name = export_info['file_name'] sql_columns = export_info['sql_columns'] file_columns = export_info['file_columns'] file_path = os.path.join(config['OUTPUT_DIR'],file_name) # set the output file path sql = """SELECT DISTINCT {col_list} FROM {table_name}""".format(table_name=table_name,col_list=",".join(sql_columns)) # build the SELECT statement from the sql_columns variable. Also, apply a SQL 'DISTINCT' keyword to avoid duplicates cursor.execute(sql) result = cursor.fetchall() print("") print ("Writing data from {table_name} to file {file_path}".format(table_name=table_name,file_path=file_path), end='', flush=True) f = open(file_path, 'w') record_count = 0 writer = csv.writer(f,quoting=csv.QUOTE_ALL) writer.writerow(file_columns) # write the file_columns as the headers for the .CSV file data_rows = [] for result_row in result: data_list = [] for field in sql_columns: data_list.append(result_row[field]) data_rows.append(data_list) record_count = record_count + 1 #write every 100,000 records if record_count % 100000 == 0: print('.', end='', flush=True) writer.writerows(data_rows) # clear data_rows data_rows = [] writer.writerows(data_rows) f.close() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() # utility function def isascii(s): """Check if the characters in string s are in ASCII, U+0-U+7F.""" return len(s) == len(s.encode()) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('commands', type=str, nargs='+',default='extract transform load') command_list = [] try: args = parser.parse_args() command_list = args.commands except: command_list = ['extract','extract_non_umls','transform','load'] file_path = os.path.join(os.path.abspath(os.path.dirname(__file__))) #file_path = '/home/chb69/git/ontology-api/src/neo4j_loader' file_name = 'app.cfg' config = load_config(file_path, file_name) #extract_non_umls(config) #transform(config) #load(config) if 'extract_non_umls' in command_list: extract_non_umls(config) if 'extract' in command_list: extract(config) if 'transform' in command_list: transform(config) if 'load' in command_list: load(config) print("Done")

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import sys import os import types import mysql.connector from mysql.connector import errorcode import csv import argparse config = {} def load_config(root_path, filename): filename = os.path.join(root_path, filename) d = types.ModuleType("config") d.__file__ = filename return_dict = {} try: with open(filename, mode="rb") as config_file: exec(compile(config_file.read(), filename, "exec"), d.__dict__) for config_key in d.__dict__: if str(config_key).startswith('__') == False: return_dict[config_key] = d.__dict__[config_key] except OSError as e: e.strerror = f"Unable to load configuration file ({e.strerror})" raise return return_dict def create_database(config): connection = None try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD']) cursor = connection.cursor() with open(config['TABLE_CREATE_SQL_FILEPATH'], encoding="utf-8") as f: commands = f.read().split(';') for command in commands: if str(command).strip() != "": print('Executing: ' + command) cursor.execute(command) print ("Done creating database tables.") except mysql.connector.Error as err: if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) finally: if connection != None: connection.close() def create_indices(config): connection = None try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME']) cursor = connection.cursor() for table_info in config['NODE_METADATA_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_node_label_idx (node_label(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_codeid_idx (codeid(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['EDGE_LIST_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_subject_idx (subject(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_predicate_idx (predicate(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_object_idx (object(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['DBXREF_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD FULLTEXT INDEX {table_name}_dbxrefs_idx (dbxrefs(700))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['RELATIONS_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_relation_id_idx (relation_id(100))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_relation_label_idx (relation_label(50))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_inverse_relation_label_idx (inverse_relation_label(50))".format(table_name=table_info['table_name']) cursor.execute(sql) for table_info in config['SYNONYM_LIST_FILE_TABLE_INFO']: sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_ontology_uri_idx (ontology_uri(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_synonym_idx (synonym(500))".format(table_name=table_info['table_name']) cursor.execute(sql) sql = "ALTER TABLE {table_name} ADD INDEX {table_name}_sab_idx (sab(50))".format(table_name=table_info['table_name']) cursor.execute(sql) print ("Done creating database indices.") except mysql.connector.Error as err: if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) finally: if connection != None: connection.close() def load_edge_list(config): edge_list_list = config['EDGE_LIST_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in edge_list_list: table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, subject VARCHAR(2048) NOT NULL, predicate VARCHAR(2048) NOT NULL, object VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_synonym_list(config): if 'SYNONYM_LIST_FILE_TABLE_INFO' not in config: return synonym_list = config['SYNONYM_LIST_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in synonym_list: # walk through the list of synonym files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048) NOT NULL, synonym VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the generic SQL to create a synonym table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) # add the SAB for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_relations(config): node_metadata_list = config['RELATIONS_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: # walk through the list of relations files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, relation_id VARCHAR(2048) NOT NULL, relation_label VARCHAR(2048) NOT NULL, inverse_relation_label VARCHAR(2048), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the generic create relations SQL statement cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def create_missing_codeids(config): node_metadata_list = config['NODE_METADATA_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: table_name = table_data['table_name'] sql = """UPDATE {table_name} SET codeid = REPLACE(REPLACE(ontology_uri, 'http://purl.obolibrary.org/obo/',''), '_', ' ') WHERE codeid IS NULL""".format(table_name=table_name) # add a codeid for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def fix_dbxrefs(config): connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) table_name = 'dbxrefs' sql = """UPDATE {table_name} SET xref = UPPER(xref)""".format(table_name=table_name) # uppercase all dbxrefs data in table cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'NCIT:', 'NCI:') WHERE xref LIKE 'NCIT:%'""".format(table_name=table_name) # convert all the NCI codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'HTTP://WWW.SNOMEDBROWSER.COM/CODES/DETAILS/', 'SNOMEDCT_US:') WHERE xref LIKE 'HTTP://WWW.SNOMEDBROWSER.COM/CODES/DETAILS/%'""".format(table_name=table_name) # convert all the SNOMED codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, 'MESH:', 'MSH:') WHERE xref LIKE 'MESH:%' AND instr(xref, 'MESH:D') > 0 AND instr(xref, 'MESH:D24') = 0""".format(table_name=table_name) # convert all the MeSH codes cursor.execute(sql) connection.commit() sql = """UPDATE {table_name} SET xref = REPLACE(xref, ':', ' ')""".format(table_name=table_name) # replace all remaining colons with spaces dbxrefs data in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_node_metadata(config): node_metadata_list = config['NODE_METADATA_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in node_metadata_list: # walk through the list of node_metadata files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048), node_label VARCHAR(2048) NOT NULL, node_definition VARCHAR(2048) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this SQL creates the generic node_metadata table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) sql = "UPDATE {table_name} SET sab = '{sab}'".format(table_name=table_name,sab=sab) # add the SAB for all records in table cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_dbxref(config): dbxref_list = config['DBXREF_FILE_TABLE_INFO'] connection = None sql = '' record_count = 0 try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_data in dbxref_list: # walk through the list of dbxref files found in the config file. # for each entry, read the corresponding file and load it into the referenced # mysql table. table_name = table_data['table_name'] file_name = table_data['file_name'] sab = table_data['sab'] drop_table_sql = "DROP TABLE IF EXISTS {table_name}".format(table_name=table_name) cursor.execute(drop_table_sql) table_create_sql = """CREATE TABLE {table_name} ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, dbxrefs VARCHAR(5120) NOT NULL, sab VARCHAR(50), PRIMARY KEY(id) )""".format(table_name=table_name) # this is the SQL to create a generic dbxref table cursor.execute(table_create_sql) connection.commit() print("Created table: " + table_name) file_path = os.path.join(config['ONTOLOGY_SOURCE_DIR'], file_name) load_file(config, file_path, table_name) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_umls_codes(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CODEs.csv') table_name = 'umls_codes' load_file(config, file_path, table_name) def load_umls_defs(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'DEFs.csv') table_name = 'umls_defs' load_file(config, file_path, table_name) def load_umls_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'SUIs.csv') table_name = 'umls_suis' load_file(config, file_path, table_name) def load_umls_cuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUIs.csv') table_name = 'umls_cuis' load_file(config, file_path, table_name) def load_umls_tuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'TUIs.csv') table_name = 'umls_tuis' load_file(config, file_path, table_name) def load_umls_code_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CODE-SUIs.csv') table_name = 'umls_code_suis' load_file(config, file_path, table_name) def load_umls_cui_codes(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-CODEs.csv') table_name = 'umls_cui_codes' load_file(config, file_path, table_name) def load_umls_cui_cuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-CUIs.csv') table_name = 'umls_cui_cuis' load_file(config, file_path, table_name) def load_umls_cui_suis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-SUIs.csv') table_name = 'umls_cui_suis' load_file(config, file_path, table_name) def load_umls_cui_tuis(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'CUI-TUIs.csv') table_name = 'umls_cui_tuis' load_file(config, file_path, table_name) def load_umls_def_rel(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'DEFrel.csv') table_name = 'umls_def_rel' load_file(config, file_path, table_name) def load_umls_tui_rel(config): file_path = os.path.join(config['UMLS_SOURCE_DIR'],'TUIrel.csv') table_name = 'umls_tui_rel' load_file(config, file_path, table_name) def build_xref_table(config): dbxref_list = config['DBXREF_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS dbxrefs" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE dbxrefs ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, xref VARCHAR(2048) NOT NULL, PRIMARY KEY(id) );""" cursor.execute(create_table_sql) for table_data in dbxref_list: table_name = table_data['table_name'] sab = table_data['sab'] cursor.execute("SELECT ontology_uri, dbxrefs FROM {table_name}".format(table_name=table_name)) print("Loading {sab} data into table {table_name}".format(table_name="dbxrefs", sab=sab), end='', flush=True) result = cursor.fetchall() record_count = 0 for row in result: ontology_uri = row['ontology_uri'] all_xrefs = row['dbxrefs'] xref_list = all_xrefs.split('|') # For each row in the ontology_dbxref table, split the dbxrefs column into a list for ref in xref_list: # for each xref in the list, insert a new row into the dbxrefs table ref = ref.replace("'","''") sql = "INSERT INTO dbxrefs (ontology_uri, xref) VALUES ('{ontology_uri}','{ref}')".format(ontology_uri=ontology_uri, ref=ref) cursor.execute(sql) record_count = record_count + 1 if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() print('') connection.commit() print ("Done loading the {table_name} table.".format(table_name="dbxrefs")) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def load_file(config, file_path, table_name): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor() record_count = 0 with open(file_path) as csvfile: myCSVReader = None if file_path.endswith('.txt'): myCSVReader = csv.DictReader(csvfile, delimiter='\t') else: myCSVReader = csv.DictReader(csvfile) field_names = myCSVReader.fieldnames if 'name_lc' in field_names: field_names.remove('name_lc') if 'REL' in field_names: field_names.remove('REL') if 'RELA' in field_names: field_names.remove('RELA') if (file_path.endswith('CUI-SUIs.csv') or file_path.endswith('CUI-TUIs.csv') or file_path.endswith('DEFrel.csv') or file_path.endswith('TUIrel.csv')): field_names.append('type') field_list_str = '%s' % ', '.join(map(str, field_names)) field_list_str = field_list_str.replace(':ID', '') field_list_str = field_list_str.replace(':', '') value_list_str = '' for field in field_names: value_list_str += '%({field})s, '.format(field=field) value_list_str = value_list_str[:-2] sql = """INSERT INTO {table_name}({field_list}) VALUE ({value_list})""".format(table_name=table_name, field_list=field_list_str, value_list=value_list_str) print("Loading data from {file_name} into table {table_name}".format(file_name=file_path, table_name=table_name), end='', flush=True) for row in myCSVReader: if file_path.endswith('CUI-SUIs.csv'): row['type'] = 'PREF_TERM' if file_path.endswith('CUI-TUIs.csv'): row['type'] = 'STY' if file_path.endswith('DEFrel.csv'): row['type'] = 'DEF' if file_path.endswith('TUIrel.csv'): row['type'] = 'ISA_STY' if table_name == 'suis': if None in row.keys(): row.pop(None) if None in row.keys(): row.pop(None) cursor.execute(sql, row) record_count = record_count + 1 if record_count % 200000 == 0: print('.', end='', flush=True) connection.commit() print('') connection.commit() print ("Done loading the {table_name} table.".format(table_name=table_name)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def extract_non_umls(config): load_node_metadata(config) load_relations(config) load_dbxref(config) load_edge_list(config) load_synonym_list(config) create_missing_codeids(config) def extract(config): create_database(config) load_node_metadata(config) load_relations(config) load_dbxref(config) load_edge_list(config) load_synonym_list(config) load_umls_codes(config) load_umls_defs(config) load_umls_suis(config) load_umls_cuis(config) load_umls_tuis(config) load_umls_cui_codes(config) load_umls_code_suis(config) load_umls_cui_cuis(config) load_umls_cui_suis(config) load_umls_cui_tuis(config) load_umls_def_rel(config) load_umls_tui_rel(config) create_missing_codeids(config) create_indices(config) print("Done with extract process") def build_ambiguous_codes_table(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS temp_ambiguous_codes" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE temp_ambiguous_codes ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, codeid VARCHAR(2048), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table temp_ambiguous_codes") sql = """INSERT INTO temp_ambiguous_codes (ontology_uri, codeid) SELECT DISTINCT ontology_uri, xref as codeid FROM dbxrefs, umls_cui_codes as rel WHERE xref = rel.end_id GROUP BY ontology_uri, xref HAVING COUNT(DISTINCT rel.start_id) > 1""" cursor.execute(sql) connection.commit() print("Loaded codes into table temp_ambiguous_codes") except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def temp_build_ccf_code_cui_table(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS temp_ccf_cui_codes" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE temp_ccf_cui_codes ( id INT NOT NULL AUTO_INCREMENT, codeid VARCHAR(2048) NOT NULL, cui VARCHAR(2048), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table temp_ccf_cui_codes") sql = "ALTER TABLE temp_ccf_cui_codes ADD INDEX temp_ccf_cui_codes_codeid(codeid(50))" cursor.execute(sql) sql = "ALTER TABLE temp_ccf_cui_codes ADD INDEX temp_ccf_cui_codes_cui_idx(cui(50))" cursor.execute(sql) cursor = connection.cursor() record_count = 0 file_path = '/home/chb69/umls_data/ccf/CCF-CUI.csv' with open(file_path) as csvfile: myCSVReader = None if file_path.endswith('.txt'): myCSVReader = csv.DictReader(csvfile, delimiter='\t') else: myCSVReader = csv.DictReader(csvfile) field_names = myCSVReader.fieldnames print("Loading data from {file_name} into table {table_name}".format(file_name=file_path, table_name='temp_ccf_cui_codes'), end='', flush=True) for row in myCSVReader: sql = "INSERT INTO temp_ccf_cui_codes (codeid, cui) VALUES ('{codeid}','{cui}')".format(codeid=row['b.CodeID'],cui=row['c.CUI']) cursor.execute(sql) connection.commit() print ("Done loading the {table_name} table.".format(table_name="temp_ccf_cui_codes")) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def build_ontology_uri_to_umls_map_table(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS ontology_uri_map" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE ontology_uri_map ( id INT NOT NULL AUTO_INCREMENT, ontology_uri VARCHAR(2048) NOT NULL, cui VARCHAR(2048), codeid VARCHAR(2048), type VARCHAR(50), mapping_type VARCHAR(50), sab VARCHAR(50), PRIMARY KEY(id) );""" cursor.execute(create_table_sql) print("Created table ontology_uri_map") sql = """INSERT INTO ontology_uri_map (ontology_uri, cui) SELECT DISTINCT ontology_uri, substr(xref,6) as CUI FROM dbxrefs WHERE xref LIKE 'UMLS%'""" # these records will have their codeid column set to NULL cursor.execute(sql) connection.commit() print("Loaded UMLS map into table ontology_uri_map") sql = """INSERT INTO ontology_uri_map (ontology_uri, codeid, cui, type, sab) SELECT DISTINCT ontology_uri, xref as codeid, rel.start_id as cui, 'PT' as type, substring_index(xref,' ', 1) as sab FROM dbxrefs, umls_cui_codes as rel WHERE xref = rel.end_id AND (ontology_uri, xref) NOT IN (SELECT ontology_uri,codeid FROM temp_ambiguous_codes)""" # This query loads all the ontology_uri's that map to a code according to the dbxrefs table cursor.execute(sql) connection.commit() print("Loaded map into table ontology_uri_map") sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_ontology_uri_idx(ontology_uri(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_cui_idx(cui(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_codeid_idx(codeid(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_type_idx(type(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_mapping_type_idx(mapping_type(50))" cursor.execute(sql) sql = "ALTER TABLE ontology_uri_map ADD INDEX ontology_uri_map_sab_idx(sab(50))" cursor.execute(sql) print("Built indices for table ontology_uri_map") sql = """UPDATE ontology_uri_map SET mapping_type = 'PRIMARY' where codeid is null AND ontology_uri IN ( SELECT ontology_uri from (SELECT ontology_uri FROM ontology_uri_map where codeid is null group by ontology_uri having count(distinct cui) = 1) as table_one)""" cursor.execute(sql) connection.commit() print("Loaded PRIMARY CUI map data into table ontology_uri_map") except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def build_relations_table(config): relations_table_info = config['RELATIONS_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) drop_table_sql = "DROP TABLE IF EXISTS relations" cursor.execute(drop_table_sql) create_table_sql = """CREATE TABLE relations ( id INT NOT NULL AUTO_INCREMENT, relation_id VARCHAR(2048) NOT NULL, relation_label VARCHAR(2048) NOT NULL, inverse_relation_label VARCHAR(2048), sab VARCHAR(50), PRIMARY KEY(id));""" cursor.execute(create_table_sql) print("Created table relations") for table_info in relations_table_info: table_name = table_info['table_name'] sab = table_info['sab'] sql = """INSERT INTO relations (relation_id, relation_label, inverse_relation_label, sab) SELECT relation_id, relation_label, inverse_relation_label, '{sab}' FROM {table_name}""".format(table_name=table_name, sab=sab) cursor.execute(sql) connection.commit() print("Loaded {sab} relations data into table relations".format(sab=sab)) sql = """UPDATE relations r1 LEFT JOIN relations r2 ON r1.relation_id = r2.relation_id SET r1.inverse_relation_label = CONCAT('inverse ', r2.relation_label) WHERE r2.inverse_relation_label IS NULL""" cursor.execute(sql) connection.commit() print("Added inverse relations for {sab} data into table relations".format(sab=sab)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_cui_cui_relations(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) edge_list_file_info = config['EDGE_LIST_FILE_TABLE_INFO'] for edge_list_info in edge_list_file_info: sab = edge_list_info['sab'] table_name = edge_list_info['table_name'] sql = """DELETE FROM umls_cui_cuis WHERE sab = '{sab}'""".format(sab=sab) cursor.execute(sql) connection.commit() print('') print("Deleted {sab} map from table umls_cui_cuis".format(sab=sab)) sql = """INSERT INTO umls_cui_cuis (start_id, type, end_id, sab) SELECT DISTINCT subject_table.cui as start_id, lower(replace(rel.relation_label,' ','_')) as type, object_table.cui as end_id, 'UBERON' as sab FROM {table_name} el, relations rel, ontology_uri_map subject_table, ontology_uri_map object_table WHERE rel.relation_id = el.predicate AND subject_table.ontology_uri = el.subject AND subject_table.mapping_type = 'PRIMARY' AND object_table.ontology_uri = el.object AND object_table.mapping_type = 'PRIMARY' AND subject_table.cui != object_table.cui AND el.sab = '{sab}'""".format(table_name=table_name,sab=sab) cursor.execute(sql) connection.commit() print("Loaded {sab} map into table umls_cui_cuis".format(sab=sab)) sql = """INSERT INTO umls_cui_cuis (start_id, type, end_id, sab) SELECT DISTINCT object_table.cui as start_id, lower(replace(rel.inverse_relation_label,' ','_')) as type, subject_table.cui as end_id, 'UBERON' as sab FROM {table_name} el, relations rel, ontology_uri_map subject_table, ontology_uri_map object_table WHERE rel.relation_id = el.predicate AND subject_table.ontology_uri = el.subject AND subject_table.mapping_type = 'PRIMARY' AND object_table.ontology_uri = el.object AND object_table.mapping_type = 'PRIMARY' AND subject_table.cui != object_table.cui AND rel.inverse_relation_label IS NOT NULL AND el.sab = '{sab}'""".format(table_name=table_name,sab=sab) cursor.execute(sql) connection.commit() print("Loaded {sab} inverse relation map into table umls_cui_cuis".format(sab=sab)) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_terms(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE suis_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE code_suis_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE new_sui_map" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = """TRUNCATE cui_suis_updated""" cursor.execute(truncate_table_sql) connection.commit() print ("Copying cui_suis INTO cui_suis_updated") sql = """INSERT INTO cui_suis_updated SELECT * FROM umls_cui_suis""" cursor.execute(sql) connection.commit() sql = "DROP INDEX suis_updated_sui_idx ON suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX suis_updated_name_idx ON suis_updated" cursor.execute(sql) connection.commit() print ("Copying suis INTO suis_updated") sql = """INSERT INTO suis_updated SELECT * FROM umls_suis""" cursor.execute(sql) connection.commit() sql = "ALTER TABLE suis_updated ADD INDEX suis_updated_sui_idx (sui(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE suis_updated ADD INDEX suis_updated_name_idx (name(500))" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_start_id_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_end_id_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_type_idx ON code_suis_updated" cursor.execute(sql) connection.commit() sql = "DROP INDEX code_suis_updated_cui_idx ON code_suis_updated" cursor.execute(sql) connection.commit() print ("Copying code_suis INTO code_suis_updated") sql = """INSERT INTO code_suis_updated SELECT * FROM umls_code_suis""" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_start_id_idx (start_id(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_end_id_idx (end_id(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_type_idx (type(100))" cursor.execute(sql) connection.commit() sql = "ALTER TABLE code_suis_updated ADD INDEX code_suis_updated_cui_idx (cui(100))" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 for table_info in node_metadata_info: table_name = table_info['table_name'] sab = table_info['sab'] dict_new_suis = {} sql = """SELECT oum.ontology_uri as ontology_uri, oum.cui AS cui, IFNULL(oum.codeid,nm.codeid) AS codeid, nm.node_label AS label, '{sab}' as sab, su.sui AS sui, 'PT' AS term_type FROM {table_name} nm INNER JOIN ontology_uri_map oum ON nm.ontology_uri = oum.ontology_uri AND oum.mapping_type = 'PRIMARY' LEFT OUTER JOIN suis_updated su ON nm.node_label = su.name WHERE oum.codeid is null OR oum.codeid NOT IN (select start_id FROM code_suis_updated)""".format(table_name=table_name,sab=sab) cursor.execute(sql) result = cursor.fetchall() print("") print ("Loading tables suis_updated, code_suis_updated, and new_sui_map for SAB: {sab}".format(sab=sab), end='', flush=True) for row in result: ontology_uri = row['ontology_uri'] cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] label = row['label'] term_type = row['term_type'] sui = row['sui'] if sui == None: if label in dict_new_suis.keys(): sui = dict_new_suis[label] else: sui = 'HS' + str(record_count).zfill(6) sql = """INSERT INTO suis_updated (sui, name) VALUES ('{sui}',"{name}")""".format(sui=sui,name=label) cursor.execute(sql) sql = """INSERT INTO new_sui_map (codeid, sui, name) VALUES ('{codeid}','{sui}',"{name}")""".format(codeid=codeid,sui=sui,name=label) cursor.execute(sql) dict_new_suis[label] = sui sql = """INSERT INTO code_suis_updated (start_id, end_id, type, cui) VALUES ('{codeid}','{sui}','{term_type}','{cui}')""".format(codeid=codeid,sui=sui,cui=cui,term_type=term_type) cursor.execute(sql) if 'HC' in cui and term_type == 'PT': sql = """INSERT INTO cui_suis_updated (start_id, end_id, type) VALUES ('{cui}','{sui}','PREF_TERM')""".format(cui=cui,sui=sui) cursor.execute(sql) record_count = record_count + 1 if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() print('') insert_new_synonyms(config, record_count) except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_synonyms(config, record_count): if 'SYNONYM_LIST_FILE_TABLE_INFO' not in config: return synonym_list = config['SYNONYM_LIST_FILE_TABLE_INFO'] connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) for table_info in synonym_list: sab = table_info['sab'] table_name = table_info['table_name'] dict_new_suis = {} sql = """SELECT DISTINCT oum.ontology_uri as ontology_uri, oum.cui AS cui,nm.codeid AS codeid, nm.synonym AS label, '{sab}' as sab, su.sui AS sui, 'SY' AS term_type FROM {table_name} nm INNER JOIN ontology_uri_map oum ON nm.ontology_uri = oum.ontology_uri LEFT OUTER JOIN suis_updated su ON nm.synonym = su.name""".format(table_name=table_name,sab=sab) cursor.execute(sql) result = cursor.fetchall() print ("Loading tables suis_updated, code_suis_updated, and new_sui_map for SAB: {sab}".format(sab=sab), end='', flush=True) for row in result: ontology_uri = row['ontology_uri'] cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] label = row['label'] term_type = row['term_type'] sui = row['sui'] if sui == None: if label in dict_new_suis.keys(): sui = dict_new_suis[label] else: sui = 'HS' + str(record_count).zfill(6) sql = """INSERT INTO suis_updated (sui, name) VALUES ('{sui}',"{name}")""".format(sui=sui,name=label) cursor.execute(sql) sql = """INSERT INTO new_sui_map (codeid, sui, name) VALUES ('{codeid}','{sui}',"{name}")""".format(codeid=codeid,sui=sui,name=label) cursor.execute(sql) dict_new_suis[label] = sui sql = """INSERT INTO code_suis_updated (start_id, end_id, type, cui) VALUES ('{codeid}','{sui}','{term_type}','{cui}')""".format(codeid=codeid,sui=sui,cui=cui,term_type=term_type) cursor.execute(sql) record_count = record_count + 1 if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_cuis(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE cuis_updated" cursor.execute(truncate_table_sql) connection.commit() print ("Truncating cui_codes_updated") sql = """TRUNCATE cui_codes_updated""" cursor.execute(sql) connection.commit() print ("Copying cuis INTO cuis_updated") sql = """INSERT INTO cuis_updated SELECT * FROM umls_cuis""" cursor.execute(sql) connection.commit() print ("Deleting HuBMAP CUIs") sql = """DELETE FROM ontology_uri_map WHERE cui LIKE 'HC%'""" cursor.execute(sql) connection.commit() print ("Copying cuis INTO cui_codes_updated") sql = """INSERT INTO cui_codes_updated SELECT * FROM umls_cui_codes""" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 print ("Creating new HCUI's and codes") for table_info in node_metadata_info: sab = table_info['sab'] table_name = table_info['table_name'] print ("Deleting {sab} codes from umls_codes".format(sab=sab)) sql = """DELETE FROM umls_codes WHERE sab = '{sab}'""".format(sab=sab) # remove old records for the sab cursor.execute(sql) connection.commit() print("Loading node metadata for {sab}".format(sab=sab)) sql = """SELECT ontology_uri AS ontology_uri, codeid AS codeid, sab AS sab FROM {table_name} nm WHERE nm.ontology_uri NOT IN (SELECT ontology_uri FROM ontology_uri_map WHERE mapping_type = 'PRIMARY')""".format(table_name=table_name) cursor.execute(sql) result = cursor.fetchall() for row in result: ontology_uri = row['ontology_uri'] cui = 'HC' + str(record_count).zfill(6) # mint a new CUI using the HC prefix record_count = record_count + 1 current_sab = sab codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] sql = """INSERT INTO ontology_uri_map (ontology_uri,codeid,cui,sab,mapping_type) VALUES ('{ontology_uri}','{codeid}','{cui}','{sab}','PRIMARY')""".format(codeid=codeid,cui=cui,ontology_uri=ontology_uri,sab=current_sab) # add the new HCUI to the ontology_uri_map cursor.execute(sql) sql = """INSERT INTO cuis_updated (cui) VALUES ('{cui}')""".format(cui=cui) # add the new HCUI to the cuis_updated table cursor.execute(sql) connection.commit() sql = """INSERT INTO umls_codes (codeid, sab,code) VALUES ('{codeid}','{sab}','{code}')""".format(codeid=codeid,sab=current_sab,code=code) # add the new Code information to umls_codes cursor.execute(sql) connection.commit() sql = """INSERT INTO cui_codes_updated (start_id, end_id) VALUES ('{cui}','{codeid}')""".format(cui=cui,codeid=codeid) # connect the new HCUI to its new Code cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_codes(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] for table_info in node_metadata_info: table_name = table_info['table_name'] current_sab = table_info['sab'] sql = """SELECT nm.ontology_uri as ontology_uri, nm.codeid as codeid, oum.cui as cui, nm.sab as sab FROM {table_name} nm, ontology_uri_map oum WHERE oum.ontology_uri = nm.ontology_uri and oum.codeid IS NOT NULL and nm.codeid not in (select codeid from umls_codes)""".format(table_name=table_name) # this SQL finds all the codes in the current node_metadata missing from the umls_codes table # these are the codes we need to add cursor.execute(sql) result = cursor.fetchall() print ("Creating new codes for sab: {sab}".format(sab=current_sab)) for row in result: cui = row['cui'] codeid = row['codeid'] code_list = str(codeid).split(' ') code = code_list[1] sql = """INSERT INTO umls_codes (codeid, sab,code) VALUES ('{codeid}','{sab}','{code}')""".format(codeid=codeid,sab=current_sab,code=code) cursor.execute(sql) connection.commit() sql = """INSERT INTO cui_codes_updated (start_id, end_id) VALUES ('{cui}','{codeid}')""".format(cui=cui,codeid=codeid) cursor.execute(sql) connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def insert_new_defs(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) truncate_table_sql = "TRUNCATE defs_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE def_rel_updated" cursor.execute(truncate_table_sql) connection.commit() truncate_table_sql = "TRUNCATE new_def_map" cursor.execute(truncate_table_sql) connection.commit() print("") print ("Copying defs INTO defs_updated") sql = """INSERT INTO defs_updated SELECT * FROM umls_defs""" cursor.execute(sql) connection.commit() print ("Copying def_rel INTO def_rel_updated") sql = """INSERT INTO def_rel_updated SELECT * FROM umls_def_rel""" cursor.execute(sql) connection.commit() node_metadata_info = config['NODE_METADATA_FILE_TABLE_INFO'] record_count = 1 # start SUI numbering at one for table_info in node_metadata_info: table_name = table_info['table_name'] sab = table_info['sab'] sql = """SELECT oum.cui, nm.node_definition, '{sab}' as sab FROM {table_name} nm, ontology_uri_map oum WHERE nm.ontology_uri = oum.ontology_uri AND oum.mapping_type = 'PRIMARY' AND node_definition <> 'None' AND node_definition <> '.'""".format(table_name=table_name,sab=sab) cursor.execute(sql) result = cursor.fetchall() print("") print ("Loading tables defs_updated, def_rels_updated, and new_def_map", end='', flush=True) for row in result: cui = row['cui'] node_definition = row['node_definition'] sab = row['sab'] atui = 'HAT' + str(record_count).zfill(6) record_count = record_count + 1 if '"' in node_definition: node_definition = node_definition.replace('"','\\"') sql = """INSERT INTO defs_updated (atui, sab, def) VALUES ('{atui}','{sab}',"{node_definition}")""".format(atui=atui,sab=sab,node_definition=node_definition) cursor.execute(sql) sql = """INSERT INTO def_rel_updated (start_id, end_id, type, sab) VALUES ('{cui}','{atui}','DEF','{sab}')""".format(atui=atui,sab=sab,cui=cui) cursor.execute(sql) sql = """INSERT INTO new_def_map (cui, atui, node_definition, sab) VALUES ('{cui}','{atui}',"{node_definition}", '{sab}')""".format(atui=atui,sab=sab,cui=cui,node_definition=node_definition) cursor.execute(sql) #commit every 10,000 records if record_count % 10000 == 0: print('.', end='', flush=True) connection.commit() connection.commit() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() def transform(config): build_xref_table(config) # This code is temporary. It should be moved to a pre-processing step fix_dbxrefs(config) # END This code is temporary. It should be moved to a pre-processing step build_ambiguous_codes_table(config) build_ontology_uri_to_umls_map_table(config) build_relations_table(config) insert_new_cuis(config) insert_new_codes(config) insert_new_terms(config) insert_new_defs(config) insert_new_cui_cui_relations(config) print('') # do this to disable the 'end' flag in prior print statements print("Done with transform process") def load(config): export_files(config) print('') # do this to disable the 'end' flag in prior print statements print("Done with load process") def export_files(config): connection = None sql = '' try: connection = mysql.connector.connect( host=config['MYSQL_HOSTNAME'], user=config['MYSQL_USERNAME'], password=config['MYSQL_PASSWORD'], database=config['MYSQL_DATABASE_NAME'], charset='utf8mb4',collation='utf8mb4_bin') cursor = connection.cursor(dictionary=True) export_table_info = [{'table_name': 'umls_codes', 'file_name':'CODEs.csv','sql_columns':['codeid','sab','code'],'file_columns':['CodeID:ID','SAB','CODE']}, {'table_name': 'umls_tui_rel', 'file_name':'TUIrel.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'umls_cui_tuis', 'file_name':'CUI-TUIs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'umls_cui_cuis', 'file_name':'CUI-CUIs.csv','sql_columns':['start_id','end_id','type','sab'],'file_columns':[':START_ID',':END_ID',':TYPE','SAB']}, {'table_name': 'cui_codes_updated', 'file_name':'CUI-CODEs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'code_suis_updated', 'file_name':'CODE-SUIs.csv','sql_columns':['start_id','end_id','type','cui'],'file_columns':[':START_ID',':END_ID',':TYPE','CUI']}, {'table_name': 'cui_suis_updated', 'file_name':'CUI-SUIs.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}, {'table_name': 'cuis_updated', 'file_name':'CUIs.csv','sql_columns':['cui'],'file_columns':['CUI:ID']}, {'table_name': 'suis_updated', 'file_name':'SUIs.csv','sql_columns':['sui','name'],'file_columns':['SUI:ID','name']}, {'table_name': 'umls_tuis', 'file_name':'TUIs.csv','sql_columns':['tui','name','stn','def'],'file_columns':['TUI:ID','name','STN','DEF']}, {'table_name': 'defs_updated', 'file_name':'DEFs.csv','sql_columns':['atui','sab','def'],'file_columns':['ATUI:ID','SAB','DEF']}, {'table_name': 'def_rel_updated', 'file_name':'DEFrel.csv','sql_columns':['start_id','end_id'],'file_columns':[':START_ID',':END_ID']}] for export_info in export_table_info: # walk through all the entries in the export_table_info list table_name = export_info['table_name'] file_name = export_info['file_name'] sql_columns = export_info['sql_columns'] file_columns = export_info['file_columns'] file_path = os.path.join(config['OUTPUT_DIR'],file_name) # set the output file path sql = """SELECT DISTINCT {col_list} FROM {table_name}""".format(table_name=table_name,col_list=",".join(sql_columns)) # build the SELECT statement from the sql_columns variable. Also, apply a SQL 'DISTINCT' keyword to avoid duplicates cursor.execute(sql) result = cursor.fetchall() print("") print ("Writing data from {table_name} to file {file_path}".format(table_name=table_name,file_path=file_path), end='', flush=True) f = open(file_path, 'w') record_count = 0 writer = csv.writer(f,quoting=csv.QUOTE_ALL) writer.writerow(file_columns) # write the file_columns as the headers for the .CSV file data_rows = [] for result_row in result: data_list = [] for field in sql_columns: data_list.append(result_row[field]) data_rows.append(data_list) record_count = record_count + 1 #write every 100,000 records if record_count % 100000 == 0: print('.', end='', flush=True) writer.writerows(data_rows) # clear data_rows data_rows = [] writer.writerows(data_rows) f.close() except mysql.connector.Error as err: print("Error in SQL: " + sql ) if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: print("Something is wrong with your user name or password") elif err.errno == errorcode.ER_BAD_DB_ERROR: print("Database does not exist") else: print(err) connection.rollback() finally: if connection != None: connection.close() # utility function def isascii(s): return len(s) == len(s.encode()) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('commands', type=str, nargs='+',default='extract transform load') command_list = [] try: args = parser.parse_args() command_list = args.commands except: command_list = ['extract','extract_non_umls','transform','load'] file_path = os.path.join(os.path.abspath(os.path.dirname(__file__))) #file_path = '/home/chb69/git/ontology-api/src/neo4j_loader' file_name = 'app.cfg' config = load_config(file_path, file_name) #extract_non_umls(config) #transform(config) #load(config) if 'extract_non_umls' in command_list: extract_non_umls(config) if 'extract' in command_list: extract(config) if 'transform' in command_list: transform(config) if 'load' in command_list: load(config) print("Done")

true

f7201460d7fc455a3f1d476f67b45706ae5482ed

4,736

py

Python

baselines/Termination_DEOC/run_atari_miniworld.py

anandkamat05/TDEOC

11749457c3a7550e11ba1acc4784e8545f8087aa

[ "MIT" ]

5

2020-11-10T21:38:04.000Z

2021-08-11T01:34:50.000Z

baselines/Termination_DEOC/run_atari_miniworld.py

LARS12llt/TDEOC

11749457c3a7550e11ba1acc4784e8545f8087aa

[ "MIT" ]

8

2020-09-26T01:31:02.000Z

2022-02-10T02:19:53.000Z

baselines/Termination_DEOC/run_atari_miniworld.py

LARS12llt/TDEOC

11749457c3a7550e11ba1acc4784e8545f8087aa

[ "MIT" ]

1

2020-11-18T03:20:26.000Z

# !/usr/bin/env python from baselines.common import set_global_seeds, tf_util as U from baselines import bench import os.path as osp import gym, logging from mpi4py import MPI import pdb from gym_extensions.continuous import mujoco import gym_miniworld from baselines import logger import sys def train(env_id, num_timesteps, seed, num_options,app, saves ,wsaves, epoch,dc, render=False, caption='', deoc=False, tradeoff=0.1, term_mult=1.0, lr_mult=1.0, tdeoc=False): from baselines.Termination_DEOC import cnn_policy, pposgd_simple # U.make_session(num_cpu=1).__enter__() # set_global_seeds(seed) rank = MPI.COMM_WORLD.Get_rank() sess = U.single_threaded_session() sess.__enter__() if rank == 0: logger.configure() else: logger.configure(format_strs=[]) workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() if seed is not None else None set_global_seeds(workerseed) env = gym.make(env_id) env.seed(workerseed) def policy_fn(name, ob_space, ac_space): return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=64, num_hid_layers=2, num_options=num_options, dc=dc) env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank))) if num_options ==1: optimsize=64 elif num_options >1 and num_options < 5: optimsize=32 else: print("Only upto 3 options or primitive actions is currently supported.") sys.exit() # ATARI HYPERPARAMETERS # pposgd_simple.learn(env, policy_fn, # max_timesteps=num_timesteps*1.1, # timesteps_per_batch=256, # clip_param=0.2, entcoeff=0.001, # optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=optimsize, # gamma=0.99, lam=0.95, schedule='linear', num_options=num_options, # app=app, saves=saves, wsaves=wsaves, epoch=epoch, seed=seed,dc=dc, render=render, caption=caption, # deoc=deoc, tradeoff=tradeoff, term_mult=term_mult, lr_mult=lr_mult, tdeoc=tdeoc # ) # MINIWORLD HYPERPARAMETERS pposgd_simple.learn(env, policy_fn, max_timesteps=num_timesteps, timesteps_per_batch=2048, clip_param=0.2, entcoeff=0.01, optim_epochs=4, optim_stepsize=3e-4, optim_batchsize=optimsize, gamma=0.99, lam=0.95, schedule='linear', num_options=num_options, app=app, saves=saves, wsaves=wsaves, epoch=epoch, seed=seed,dc=dc, render=render, caption=caption, deoc=deoc, tradeoff=tradeoff, term_mult=term_mult, lr_mult=lr_mult, tdeoc=tdeoc ) env.close() def main(): import argparse parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--env', help='environment ID', default='MiniWorld-OneRoom-v0') parser.add_argument('--seed', help='RNG seed', type=int, default=16) parser.add_argument('--opt', help='number of options', type=int, default=2) parser.add_argument('--app', help='Append to folder name', type=str, default='') parser.add_argument('--saves', dest='saves', action='store_true', default=False) parser.add_argument('--wsaves', dest='wsaves', action='store_true', default=False) parser.add_argument('--epoch', help='Epoch', type=int, default=-1) parser.add_argument('--dc', type=float, default=0.) parser.add_argument('--render', dest='render', action='store_true', default=False) parser.add_argument('--caption', help='Caption for run', default='') parser.add_argument('--deoc', help='Augment reward with diversity', action='store_true', default=False) parser.add_argument('--tradeoff', type=float, default=0.0) parser.add_argument('--term_mult', type=float, default=1.0) parser.add_argument('--lr_mult', type=float, default=1.0) parser.add_argument('--tdeoc', help='Use diversity in termination objective', action='store_true', default=False) args = parser.parse_args() if args.tdeoc and not args.deoc: print("Setting deoc arg to True...") args.deoc = True train(args.env, num_timesteps=2e6, seed=args.seed, num_options=args.opt, app=args.app, saves=args.saves, wsaves=args.wsaves, epoch=args.epoch,dc=args.dc, render=args.render, caption=args.caption, deoc=args.deoc, tradeoff=args.tradeoff, term_mult=args.term_mult, lr_mult=args.lr_mult, tdeoc=args.tdeoc) if __name__ == '__main__': main()

47.36

174

0.655617

from baselines.common import set_global_seeds, tf_util as U from baselines import bench import os.path as osp import gym, logging from mpi4py import MPI import pdb from gym_extensions.continuous import mujoco import gym_miniworld from baselines import logger import sys def train(env_id, num_timesteps, seed, num_options,app, saves ,wsaves, epoch,dc, render=False, caption='', deoc=False, tradeoff=0.1, term_mult=1.0, lr_mult=1.0, tdeoc=False): from baselines.Termination_DEOC import cnn_policy, pposgd_simple rank = MPI.COMM_WORLD.Get_rank() sess = U.single_threaded_session() sess.__enter__() if rank == 0: logger.configure() else: logger.configure(format_strs=[]) workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank() if seed is not None else None set_global_seeds(workerseed) env = gym.make(env_id) env.seed(workerseed) def policy_fn(name, ob_space, ac_space): return cnn_policy.CnnPolicy(name=name, ob_space=ob_space, ac_space=ac_space, hid_size=64, num_hid_layers=2, num_options=num_options, dc=dc) env = bench.Monitor(env, logger.get_dir() and osp.join(logger.get_dir(), str(rank))) if num_options ==1: optimsize=64 elif num_options >1 and num_options < 5: optimsize=32 else: print("Only upto 3 options or primitive actions is currently supported.") sys.exit() pposgd_simple.learn(env, policy_fn, max_timesteps=num_timesteps, timesteps_per_batch=2048, clip_param=0.2, entcoeff=0.01, optim_epochs=4, optim_stepsize=3e-4, optim_batchsize=optimsize, gamma=0.99, lam=0.95, schedule='linear', num_options=num_options, app=app, saves=saves, wsaves=wsaves, epoch=epoch, seed=seed,dc=dc, render=render, caption=caption, deoc=deoc, tradeoff=tradeoff, term_mult=term_mult, lr_mult=lr_mult, tdeoc=tdeoc ) env.close() def main(): import argparse parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--env', help='environment ID', default='MiniWorld-OneRoom-v0') parser.add_argument('--seed', help='RNG seed', type=int, default=16) parser.add_argument('--opt', help='number of options', type=int, default=2) parser.add_argument('--app', help='Append to folder name', type=str, default='') parser.add_argument('--saves', dest='saves', action='store_true', default=False) parser.add_argument('--wsaves', dest='wsaves', action='store_true', default=False) parser.add_argument('--epoch', help='Epoch', type=int, default=-1) parser.add_argument('--dc', type=float, default=0.) parser.add_argument('--render', dest='render', action='store_true', default=False) parser.add_argument('--caption', help='Caption for run', default='') parser.add_argument('--deoc', help='Augment reward with diversity', action='store_true', default=False) parser.add_argument('--tradeoff', type=float, default=0.0) parser.add_argument('--term_mult', type=float, default=1.0) parser.add_argument('--lr_mult', type=float, default=1.0) parser.add_argument('--tdeoc', help='Use diversity in termination objective', action='store_true', default=False) args = parser.parse_args() if args.tdeoc and not args.deoc: print("Setting deoc arg to True...") args.deoc = True train(args.env, num_timesteps=2e6, seed=args.seed, num_options=args.opt, app=args.app, saves=args.saves, wsaves=args.wsaves, epoch=args.epoch,dc=args.dc, render=args.render, caption=args.caption, deoc=args.deoc, tradeoff=args.tradeoff, term_mult=args.term_mult, lr_mult=args.lr_mult, tdeoc=args.tdeoc) if __name__ == '__main__': main()

true

f72014b925bc545ca989597e22d048c3a184e38f

1,430

py

Python

Transposition/transpositionFileCipher.py

a1exlism/HackingSecretCiphersWithPy

d7ec59d9eb5c5ae55c68ce911a3973ae0c526698

[ "MIT" ]

null

Transposition/transpositionFileCipher.py

a1exlism/HackingSecretCiphersWithPy

d7ec59d9eb5c5ae55c68ce911a3973ae0c526698

[ "MIT" ]

null

Transposition/transpositionFileCipher.py

a1exlism/HackingSecretCiphersWithPy

d7ec59d9eb5c5ae55c68ce911a3973ae0c526698

[ "MIT" ]

null

import os, sys, time, Transposition.transpositionEncrypt as ENC, \ Transposition.transpositionDecrypt as DEC def main(): f_key = 10 # f_mode = 'encrypt' f_mode = 'decrypt' if f_mode == 'decrypt': input_filename = 'frankenstein.encrypt.txt' else: input_filename = 'frankenstein.txt' output_filename = f'frankenstein.{f_mode}.txt' if not os.path.exists(input_filename): print(f'File {input_filename} not exist, Quitting...') sys.exit() if os.path.exists(output_filename): print( f'File {output_filename} existed, will be overwrite. (C)ontinue or (Q)uit?') response = input('> ') if not response.lower().startswith('c'): sys.exit() # read file file_obj = open(input_filename) content = file_obj.read() file_obj.close() print(f'{f_mode.title()}ing...') start_time = time.time() if f_mode == 'encrypt': transformed = ENC.encrypt_msg(f_key, content) else: transformed = DEC.decrypt_msg(f_key, content) total_time = round(time.time() - start_time, 2) print(f'{f_mode.title()}sion tookes {total_time} seconds.') # write to file output_file_obj = open(output_filename, 'w') output_file_obj.write(transformed) output_file_obj.close() print(f'{output_filename} with {len(content)} {f_mode}ed done.') if __name__ == '__main__': main()

28.039216

88

0.636364

import os, sys, time, Transposition.transpositionEncrypt as ENC, \ Transposition.transpositionDecrypt as DEC def main(): f_key = 10 f_mode = 'decrypt' if f_mode == 'decrypt': input_filename = 'frankenstein.encrypt.txt' else: input_filename = 'frankenstein.txt' output_filename = f'frankenstein.{f_mode}.txt' if not os.path.exists(input_filename): print(f'File {input_filename} not exist, Quitting...') sys.exit() if os.path.exists(output_filename): print( f'File {output_filename} existed, will be overwrite. (C)ontinue or (Q)uit?') response = input('> ') if not response.lower().startswith('c'): sys.exit() file_obj = open(input_filename) content = file_obj.read() file_obj.close() print(f'{f_mode.title()}ing...') start_time = time.time() if f_mode == 'encrypt': transformed = ENC.encrypt_msg(f_key, content) else: transformed = DEC.decrypt_msg(f_key, content) total_time = round(time.time() - start_time, 2) print(f'{f_mode.title()}sion tookes {total_time} seconds.') output_file_obj = open(output_filename, 'w') output_file_obj.write(transformed) output_file_obj.close() print(f'{output_filename} with {len(content)} {f_mode}ed done.') if __name__ == '__main__': main()

true

f720154bd7ce3ed32ed597d87d874fe71e148ab1

7,664

py

Python

test/jit/test_misc.py

metacpp/pytorch

1e7a4d6bbe1fac4fb94f6b62f24c6e242db1e952

[ "Intel" ]

1

2022-03-02T00:28:04.000Z

test/jit/test_misc.py

metacpp/pytorch

1e7a4d6bbe1fac4fb94f6b62f24c6e242db1e952

[ "Intel" ]

1

2022-03-01T06:10:50.000Z

test/jit/test_misc.py

metacpp/pytorch

1e7a4d6bbe1fac4fb94f6b62f24c6e242db1e952

[ "Intel" ]

null

# Owner(s): ["oncall: jit"] from typing import Any, Dict, List, Optional, Tuple from torch.testing._internal.jit_utils import JitTestCase, make_global from torch.testing import FileCheck from torch import jit from jit.test_module_interface import TestModuleInterface # noqa: F401 import os import sys import torch import torch.testing._internal.jit_utils import torch.nn as nn # Make the helper files in test/ importable pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) sys.path.append(pytorch_test_dir) if __name__ == '__main__': raise RuntimeError("This test file is not meant to be run directly, use:\n\n" "\tpython test/test_jit.py TESTNAME\n\n" "instead.") class TestMisc(JitTestCase): def test_joined_str(self): def func(x): hello, test = "Hello", "test" print(f"{hello + ' ' + test}, I'm a {test}") print("format blank") hi = 'hi' print(f"stuff before {hi}") print(f"{hi} stuff after") return x + 1 x = torch.arange(4., requires_grad=True) # TODO: Add support for f-strings in string parser frontend # self.checkScript(func, [x], optimize=True, capture_output=True) with self.capture_stdout() as captured: out = func(x) scripted = torch.jit.script(func) with self.capture_stdout() as captured_script: out_script = func(x) self.assertEqual(out, out_script) self.assertEqual(captured, captured_script) def test_kwarg_support(self): with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "variable number of arguments"): class M(torch.nn.Module): def forward(self, *, n_tokens: int, device_name: str = 2): pass torch.jit.script(M()) class M(torch.nn.Module): def forward(self, *, n_tokens: int, device_name: str): return n_tokens, device_name sm = torch.jit.script(M()) with self.assertRaisesRegex(RuntimeError, "missing value for argument 'n_tokens'"): sm() with self.assertRaisesRegex(RuntimeError, "positional arg"): sm(3, 'hello') self.assertEqual(sm(n_tokens=3, device_name='hello'), (3, 'hello')) def test_tuple_subscripted_assign(self): with self.assertRaisesRegex(RuntimeError, "subscripted assignment"): @torch.jit.script def foo(a: Tuple[int, int]) -> None: a[0] = a[1] with self.assertRaisesRegex(RuntimeError, "augmented assignment"): @torch.jit.script def bar(a: Tuple[int, int]) -> None: a[0] += a[1] def test_subexpression_List_Future(self): @torch.jit.script def fn(x: List[torch.jit.Future[int]]) -> torch.jit.Future[int]: return x[0] FileCheck().check('Future[int]').check('Future[int]').run(fn.graph) def test_subexpression_Future_annotate(self): @torch.jit.script def fn() -> torch.jit.Future[int]: x: List[torch.jit.Future[int]] = [] return x[0] FileCheck().check("Future[int][]").run(fn.graph) def test_future_isinstance(self): @torch.jit.script def fn(x: Any) -> torch.jit.Future[int]: assert isinstance(x, jit.Future[int]) return x FileCheck().check("Future[int]").run(fn.graph) def test_str_refine_any(self): def forward(x: Any) -> str: if isinstance(x, str): return x return "foo" forward = torch.jit.script(forward) self.assertEqual(forward(1), "foo") self.assertEqual(forward("bar"), "bar") def test_subexpression_Tuple_int_int_Future(self): @torch.jit.script def fn(x: Tuple[int, int, torch.jit.Future[int]]) -> Tuple[int, torch.jit.Future[int]]: return x[0], x[2] FileCheck().check('(int, int, Future[int])').check('(int, Future[int])').run(fn.graph) def test_subexpression_Dict_int_Future(self): @torch.jit.script def fn(x: Dict[int, torch.jit.Future[int]], y: int) -> torch.jit.Future[int]: return x[y] FileCheck().check('Dict(int, Future(int))').check('Future[int]').run(fn.graph) def test_subexpression_Optional(self): @torch.jit.script def fn(x: Optional[Dict[int, torch.jit.Future[int]]]) -> Optional[torch.jit.Future[int]]: if x is not None: return x[0] else: return None FileCheck().check('Dict(int, Future(int))?').run(fn.graph) def test_if_returning_any(self): """ Check that an if statement can return different types early from each branch when the return type of the function is Any. """ def if_function(inp: torch.Tensor) -> Any: if inp.shape[0] == 1: return inp * inp else: return "str" self.checkScript(if_function, (torch.randn(5),)) def test_export_opnames_interface(self): @torch.jit.interface class OneTwoModule(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: pass def two(self, x: torch.Tensor) -> torch.Tensor: pass def forward(self, x: torch.Tensor) -> torch.Tensor: pass class FooMod(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return x + y def two(self, x: torch.Tensor) -> torch.Tensor: return 2 * x def forward(self, x: torch.Tensor) -> torch.Tensor: return self.one(self.two(x), x) class BarMod(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return x * y def two(self, x: torch.Tensor) -> torch.Tensor: return 2 / x def forward(self, x: torch.Tensor) -> torch.Tensor: return self.two(self.one(x, x)) make_global(OneTwoModule) class M(nn.Module): sub : OneTwoModule def __init__(self): super(M, self).__init__() self.sub = BarMod() def forward(self, x: torch.Tensor) -> torch.Tensor: return self.sub.forward(x) def use_module_interface(mod_list: List[OneTwoModule], x: torch.Tensor): return mod_list[0].forward(x) + mod_list[1].forward(x) torch._C._enable_mobile_interface_call_export() scripted_M_mod = torch.jit.script(M()) self.assertTrue(set(['aten::mul.Scalar', 'aten::mul.Tensor', 'aten::reciprocal']).issubset( set(torch.jit.export_opnames(scripted_M_mod)))) scripted_M_mod.sub = torch.jit.script(FooMod()) self.assertTrue(set(['aten::add.Tensor', 'aten::mul.Scalar']).issubset( set(torch.jit.export_opnames(scripted_M_mod)))) def test_broadcasting_list(self): """ Test BroadcastingList and torch.nn._size_N_t alias """ from torch._jit_internal import BroadcastingList2 from torch.nn.common_types import _size_2_t def sum_i(x: _size_2_t) -> int: return x[0] + x[1] def sum_f(x: BroadcastingList2[float]) -> float: return x[0] + x[1] self.assertTrue(torch.jit.script(sum_i)(4) == 8) self.assertTrue(torch.jit.script(sum_f)(4.5) == 9.)

33.762115

106

0.584551

from typing import Any, Dict, List, Optional, Tuple from torch.testing._internal.jit_utils import JitTestCase, make_global from torch.testing import FileCheck from torch import jit from jit.test_module_interface import TestModuleInterface import os import sys import torch import torch.testing._internal.jit_utils import torch.nn as nn pytorch_test_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) sys.path.append(pytorch_test_dir) if __name__ == '__main__': raise RuntimeError("This test file is not meant to be run directly, use:\n\n" "\tpython test/test_jit.py TESTNAME\n\n" "instead.") class TestMisc(JitTestCase): def test_joined_str(self): def func(x): hello, test = "Hello", "test" print(f"{hello + ' ' + test}, I'm a {test}") print("format blank") hi = 'hi' print(f"stuff before {hi}") print(f"{hi} stuff after") return x + 1 x = torch.arange(4., requires_grad=True) # TODO: Add support for f-strings in string parser frontend # self.checkScript(func, [x], optimize=True, capture_output=True) with self.capture_stdout() as captured: out = func(x) scripted = torch.jit.script(func) with self.capture_stdout() as captured_script: out_script = func(x) self.assertEqual(out, out_script) self.assertEqual(captured, captured_script) def test_kwarg_support(self): with self.assertRaisesRegex(torch.jit.frontend.NotSupportedError, "variable number of arguments"): class M(torch.nn.Module): def forward(self, *, n_tokens: int, device_name: str = 2): pass torch.jit.script(M()) class M(torch.nn.Module): def forward(self, *, n_tokens: int, device_name: str): return n_tokens, device_name sm = torch.jit.script(M()) with self.assertRaisesRegex(RuntimeError, "missing value for argument 'n_tokens'"): sm() with self.assertRaisesRegex(RuntimeError, "positional arg"): sm(3, 'hello') self.assertEqual(sm(n_tokens=3, device_name='hello'), (3, 'hello')) def test_tuple_subscripted_assign(self): with self.assertRaisesRegex(RuntimeError, "subscripted assignment"): @torch.jit.script def foo(a: Tuple[int, int]) -> None: a[0] = a[1] with self.assertRaisesRegex(RuntimeError, "augmented assignment"): @torch.jit.script def bar(a: Tuple[int, int]) -> None: a[0] += a[1] def test_subexpression_List_Future(self): @torch.jit.script def fn(x: List[torch.jit.Future[int]]) -> torch.jit.Future[int]: return x[0] FileCheck().check('Future[int]').check('Future[int]').run(fn.graph) def test_subexpression_Future_annotate(self): @torch.jit.script def fn() -> torch.jit.Future[int]: x: List[torch.jit.Future[int]] = [] return x[0] FileCheck().check("Future[int][]").run(fn.graph) def test_future_isinstance(self): @torch.jit.script def fn(x: Any) -> torch.jit.Future[int]: assert isinstance(x, jit.Future[int]) return x FileCheck().check("Future[int]").run(fn.graph) def test_str_refine_any(self): def forward(x: Any) -> str: if isinstance(x, str): return x return "foo" forward = torch.jit.script(forward) self.assertEqual(forward(1), "foo") self.assertEqual(forward("bar"), "bar") def test_subexpression_Tuple_int_int_Future(self): @torch.jit.script def fn(x: Tuple[int, int, torch.jit.Future[int]]) -> Tuple[int, torch.jit.Future[int]]: return x[0], x[2] FileCheck().check('(int, int, Future[int])').check('(int, Future[int])').run(fn.graph) def test_subexpression_Dict_int_Future(self): @torch.jit.script def fn(x: Dict[int, torch.jit.Future[int]], y: int) -> torch.jit.Future[int]: return x[y] FileCheck().check('Dict(int, Future(int))').check('Future[int]').run(fn.graph) def test_subexpression_Optional(self): @torch.jit.script def fn(x: Optional[Dict[int, torch.jit.Future[int]]]) -> Optional[torch.jit.Future[int]]: if x is not None: return x[0] else: return None FileCheck().check('Dict(int, Future(int))?').run(fn.graph) def test_if_returning_any(self): def if_function(inp: torch.Tensor) -> Any: if inp.shape[0] == 1: return inp * inp else: return "str" self.checkScript(if_function, (torch.randn(5),)) def test_export_opnames_interface(self): @torch.jit.interface class OneTwoModule(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: pass def two(self, x: torch.Tensor) -> torch.Tensor: pass def forward(self, x: torch.Tensor) -> torch.Tensor: pass class FooMod(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return x + y def two(self, x: torch.Tensor) -> torch.Tensor: return 2 * x def forward(self, x: torch.Tensor) -> torch.Tensor: return self.one(self.two(x), x) class BarMod(nn.Module): def one(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: return x * y def two(self, x: torch.Tensor) -> torch.Tensor: return 2 / x def forward(self, x: torch.Tensor) -> torch.Tensor: return self.two(self.one(x, x)) make_global(OneTwoModule) class M(nn.Module): sub : OneTwoModule def __init__(self): super(M, self).__init__() self.sub = BarMod() def forward(self, x: torch.Tensor) -> torch.Tensor: return self.sub.forward(x) def use_module_interface(mod_list: List[OneTwoModule], x: torch.Tensor): return mod_list[0].forward(x) + mod_list[1].forward(x) torch._C._enable_mobile_interface_call_export() scripted_M_mod = torch.jit.script(M()) self.assertTrue(set(['aten::mul.Scalar', 'aten::mul.Tensor', 'aten::reciprocal']).issubset( set(torch.jit.export_opnames(scripted_M_mod)))) scripted_M_mod.sub = torch.jit.script(FooMod()) self.assertTrue(set(['aten::add.Tensor', 'aten::mul.Scalar']).issubset( set(torch.jit.export_opnames(scripted_M_mod)))) def test_broadcasting_list(self): from torch._jit_internal import BroadcastingList2 from torch.nn.common_types import _size_2_t def sum_i(x: _size_2_t) -> int: return x[0] + x[1] def sum_f(x: BroadcastingList2[float]) -> float: return x[0] + x[1] self.assertTrue(torch.jit.script(sum_i)(4) == 8) self.assertTrue(torch.jit.script(sum_f)(4.5) == 9.)

true

f72015bb9160c0942bb83fe1f3d4aa6377a9797d

5,563

py

Python

natlas-server/app/elastic/client.py

m4rcu5/natlas

d1057c5349a5443cecffb3db9a6428f7271b07ad

[ "Apache-2.0" ]

null

natlas-server/app/elastic/client.py

m4rcu5/natlas

d1057c5349a5443cecffb3db9a6428f7271b07ad

[ "Apache-2.0" ]

null

natlas-server/app/elastic/client.py

m4rcu5/natlas

d1057c5349a5443cecffb3db9a6428f7271b07ad

[ "Apache-2.0" ]

null

import json from config import Config import elasticsearch import time from datetime import datetime import logging from opencensus.trace import execution_context from opencensus.trace import span as span_module import semver class ElasticClient: es = None lastReconnectAttempt = None mapping = {} natlasIndices = ["nmap", "nmap_history"] status = False # Quiets the elasticsearch logger because otherwise connection errors print tracebacks to the WARNING level, even when the exception is handled. logger = logging.getLogger('elasticsearch') logger.setLevel('ERROR') def __init__(self, elasticURL): # Elastic is initialized outside an application context so we have to instatiate Config ourselves to get BASEDIR with open(Config().BASEDIR + '/defaults/elastic/mapping.json') as mapfile: self.mapping = json.loads(mapfile.read()) try: self.es = elasticsearch.Elasticsearch(elasticURL, timeout=5, max_retries=1) self.status = self._ping() if self.status: self.esversion = semver.VersionInfo.parse(self.es.info()['version']['number']) self.logger.info("Elastic Version: " + str(self.esversion)) self._initialize_indices() self.logger.info("Initialized Elasticsearch indices") except Exception: self.status = False raise finally: # Set the lastReconnectAttempt to the timestamp after initialization self.lastReconnectAttempt = datetime.utcnow() return def _initialize_indices(self): ''' Check each required index and make sure it exists, if it doesn't then create it ''' for index in self.natlasIndices: if not self.es.indices.exists(index): self.es.indices.create(index) # Avoid a race condition time.sleep(2) for index in self.natlasIndices: if self.esversion.match(">=7.0.0"): self.es.indices.put_mapping(index=index, doc_type='_doc', body=self.mapping, include_type_name=True) else: self.es.indices.put_mapping(index=index, doc_type='_doc', body=self.mapping) def _ping(self): ''' Returns True if the cluster is up, False otherwise''' with self._new_trace_span(operation='ping'): return self.es.ping() def _attempt_reconnect(self): ''' Attempt to reconnect if we haven't tried to reconnect too recently ''' now = datetime.utcnow() delta = now - self.lastReconnectAttempt if delta.seconds < 30: return self.status else: self.status = self._ping() return self.status def _check_status(self): ''' If we're in a known bad state, try to reconnect ''' if not self.status and not self._attempt_reconnect(): raise elasticsearch.ConnectionError return self.status def get_collection(self, **kwargs): ''' Execute a search and return a collection of results ''' results = self.execute_search(**kwargs) if not results: return 0, [] docsources = self.collate_source(results['hits']['hits']) return results['hits']['total'], docsources def get_single_host(self, **kwargs): ''' Execute a search and return a single result ''' results = self.execute_search(**kwargs) if not results or results['hits']['total'] == 0: return 0, None return results['hits']['total'], results['hits']['hits'][0]['_source'] def collate_source(self, documents): return map(lambda doc: doc['_source'], documents) # Mid-level query executor abstraction. def execute_search(self, **kwargs): ''' Execute an arbitrary search.''' with self._new_trace_span(operation='search', **kwargs) as span: results = self._execute_raw_query(self.es.search, doc_type='_doc', rest_total_hits_as_int=True, **kwargs) span.add_attribute('es.hits.total', results['hits']['total']) self._attach_shard_span_attrs(span, results) return results def execute_count(self, **kwargs): ''' Executes an arbitrary count.''' results = None with self._new_trace_span(operation='count', **kwargs) as span: results = self._execute_raw_query(self.es.count, doc_type='_doc', **kwargs) self._attach_shard_span_attrs(span, results) if not results: return 0 return results def execute_delete_by_query(self, **kwargs): ''' Executes an arbitrary delete_by_query.''' with self._new_trace_span(operation='delete_by', **kwargs) as span: results = self._execute_raw_query(self.es.delete_by_query, doc_type='_doc', **kwargs) self._attach_shard_span_attrs(span, results) return results def execute_index(self, **kwargs): ''' Executes an arbitrary index. ''' with self._new_trace_span(operation='index', **kwargs): results = self._execute_raw_query(self.es.index, doc_type='_doc', **kwargs) return results # Inner-most query executor. All queries route through here. def _execute_raw_query(self, func, **kwargs): ''' Wraps the es client to make sure that ConnectionErrors are handled uniformly ''' self._check_status() try: return func(**kwargs) except elasticsearch.ConnectionError: self.status = False raise elasticsearch.ConnectionError # Tracing methods def _new_trace_span(self, operation, **kwargs): tracer = execution_context.get_opencensus_tracer() span_name = "elasticsearch" if 'index' in kwargs: span_name += '.' + operation span = tracer.span(name=span_name) span.span_kind = span_module.SpanKind.CLIENT if 'index' in kwargs: span.add_attribute('es.index', kwargs['index']) if 'body' in kwargs: span.add_attribute('es.query', kwargs['body']) return span def _attach_shard_span_attrs(self, span, results): span.add_attribute('es.shards.total', results['_shards']['total']) span.add_attribute('es.shards.successful', results['_shards']['successful'])

35.660256

145

0.732339

import json from config import Config import elasticsearch import time from datetime import datetime import logging from opencensus.trace import execution_context from opencensus.trace import span as span_module import semver class ElasticClient: es = None lastReconnectAttempt = None mapping = {} natlasIndices = ["nmap", "nmap_history"] status = False logger = logging.getLogger('elasticsearch') logger.setLevel('ERROR') def __init__(self, elasticURL): with open(Config().BASEDIR + '/defaults/elastic/mapping.json') as mapfile: self.mapping = json.loads(mapfile.read()) try: self.es = elasticsearch.Elasticsearch(elasticURL, timeout=5, max_retries=1) self.status = self._ping() if self.status: self.esversion = semver.VersionInfo.parse(self.es.info()['version']['number']) self.logger.info("Elastic Version: " + str(self.esversion)) self._initialize_indices() self.logger.info("Initialized Elasticsearch indices") except Exception: self.status = False raise finally: self.lastReconnectAttempt = datetime.utcnow() return def _initialize_indices(self): for index in self.natlasIndices: if not self.es.indices.exists(index): self.es.indices.create(index) time.sleep(2) for index in self.natlasIndices: if self.esversion.match(">=7.0.0"): self.es.indices.put_mapping(index=index, doc_type='_doc', body=self.mapping, include_type_name=True) else: self.es.indices.put_mapping(index=index, doc_type='_doc', body=self.mapping) def _ping(self): with self._new_trace_span(operation='ping'): return self.es.ping() def _attempt_reconnect(self): now = datetime.utcnow() delta = now - self.lastReconnectAttempt if delta.seconds < 30: return self.status else: self.status = self._ping() return self.status def _check_status(self): if not self.status and not self._attempt_reconnect(): raise elasticsearch.ConnectionError return self.status def get_collection(self, **kwargs): results = self.execute_search(**kwargs) if not results: return 0, [] docsources = self.collate_source(results['hits']['hits']) return results['hits']['total'], docsources def get_single_host(self, **kwargs): results = self.execute_search(**kwargs) if not results or results['hits']['total'] == 0: return 0, None return results['hits']['total'], results['hits']['hits'][0]['_source'] def collate_source(self, documents): return map(lambda doc: doc['_source'], documents) def execute_search(self, **kwargs): with self._new_trace_span(operation='search', **kwargs) as span: results = self._execute_raw_query(self.es.search, doc_type='_doc', rest_total_hits_as_int=True, **kwargs) span.add_attribute('es.hits.total', results['hits']['total']) self._attach_shard_span_attrs(span, results) return results def execute_count(self, **kwargs): results = None with self._new_trace_span(operation='count', **kwargs) as span: results = self._execute_raw_query(self.es.count, doc_type='_doc', **kwargs) self._attach_shard_span_attrs(span, results) if not results: return 0 return results def execute_delete_by_query(self, **kwargs): with self._new_trace_span(operation='delete_by', **kwargs) as span: results = self._execute_raw_query(self.es.delete_by_query, doc_type='_doc', **kwargs) self._attach_shard_span_attrs(span, results) return results def execute_index(self, **kwargs): with self._new_trace_span(operation='index', **kwargs): results = self._execute_raw_query(self.es.index, doc_type='_doc', **kwargs) return results def _execute_raw_query(self, func, **kwargs): self._check_status() try: return func(**kwargs) except elasticsearch.ConnectionError: self.status = False raise elasticsearch.ConnectionError def _new_trace_span(self, operation, **kwargs): tracer = execution_context.get_opencensus_tracer() span_name = "elasticsearch" if 'index' in kwargs: span_name += '.' + operation span = tracer.span(name=span_name) span.span_kind = span_module.SpanKind.CLIENT if 'index' in kwargs: span.add_attribute('es.index', kwargs['index']) if 'body' in kwargs: span.add_attribute('es.query', kwargs['body']) return span def _attach_shard_span_attrs(self, span, results): span.add_attribute('es.shards.total', results['_shards']['total']) span.add_attribute('es.shards.successful', results['_shards']['successful'])

true

f7201983075ff6e117cd811c5af6d092bb3c77bd

79,711

py

Python

src/pretix/control/views/orders.py

joelbcastillo/pretix

1005437c69d5fed2a0ea2525b41481b0952fe6f1

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

null

src/pretix/control/views/orders.py

joelbcastillo/pretix

1005437c69d5fed2a0ea2525b41481b0952fe6f1

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

1

2020-06-12T00:09:40.000Z

src/pretix/control/views/orders.py

joelbcastillo/pretix

1005437c69d5fed2a0ea2525b41481b0952fe6f1

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

null

import json import logging import mimetypes import os import re from datetime import datetime, time, timedelta from decimal import Decimal, DecimalException from urllib.parse import urlencode import vat_moss.id from django.conf import settings from django.contrib import messages from django.core.files import File from django.db import transaction from django.db.models import ( Count, IntegerField, OuterRef, Prefetch, ProtectedError, Q, Subquery, Sum, ) from django.forms import formset_factory from django.http import ( FileResponse, Http404, HttpResponseNotAllowed, HttpResponseRedirect, JsonResponse, ) from django.shortcuts import get_object_or_404, redirect, render from django.urls import reverse from django.utils import formats from django.utils.functional import cached_property from django.utils.http import is_safe_url from django.utils.timezone import make_aware, now from django.utils.translation import ugettext_lazy as _ from django.views.generic import ( DetailView, FormView, ListView, TemplateView, View, ) from i18nfield.strings import LazyI18nString from pretix.base.channels import get_all_sales_channels from pretix.base.email import get_email_context from pretix.base.i18n import language from pretix.base.models import ( CachedCombinedTicket, CachedFile, CachedTicket, Invoice, InvoiceAddress, Item, ItemVariation, LogEntry, Order, QuestionAnswer, Quota, generate_position_secret, generate_secret, ) from pretix.base.models.orders import ( OrderFee, OrderPayment, OrderPosition, OrderRefund, ) from pretix.base.models.tax import EU_COUNTRIES, cc_to_vat_prefix from pretix.base.payment import PaymentException from pretix.base.services import tickets from pretix.base.services.export import export from pretix.base.services.invoices import ( generate_cancellation, generate_invoice, invoice_pdf, invoice_pdf_task, invoice_qualified, regenerate_invoice, ) from pretix.base.services.locking import LockTimeoutException from pretix.base.services.mail import SendMailException, render_mail from pretix.base.services.orders import ( OrderChangeManager, OrderError, approve_order, cancel_order, deny_order, extend_order, mark_order_expired, mark_order_refunded, notify_user_changed_order, ) from pretix.base.services.stats import order_overview from pretix.base.services.tickets import generate from pretix.base.signals import ( order_modified, register_data_exporters, register_ticket_outputs, ) from pretix.base.templatetags.money import money_filter from pretix.base.templatetags.rich_text import markdown_compile_email from pretix.base.views.mixins import OrderQuestionsViewMixin from pretix.base.views.tasks import AsyncAction from pretix.control.forms.filter import ( EventOrderFilterForm, OverviewFilterForm, RefundFilterForm, ) from pretix.control.forms.orders import ( CancelForm, CommentForm, ConfirmPaymentForm, ExporterForm, ExtendForm, MarkPaidForm, OrderContactForm, OrderFeeChangeForm, OrderLocaleForm, OrderMailForm, OrderPositionAddForm, OrderPositionAddFormset, OrderPositionChangeForm, OrderRefundForm, OtherOperationsForm, ) from pretix.control.permissions import EventPermissionRequiredMixin from pretix.control.views import PaginationMixin from pretix.helpers.safedownload import check_token from pretix.presale.signals import question_form_fields logger = logging.getLogger(__name__) class OrderList(EventPermissionRequiredMixin, PaginationMixin, ListView): model = Order context_object_name = 'orders' template_name = 'pretixcontrol/orders/index.html' permission = 'can_view_orders' def get_queryset(self): qs = Order.objects.filter( event=self.request.event ).select_related('invoice_address') if self.filter_form.is_valid(): qs = self.filter_form.filter_qs(qs) return qs def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['filter_form'] = self.filter_form # Only compute this annotations for this page (query optimization) s = OrderPosition.objects.filter( order=OuterRef('pk') ).order_by().values('order').annotate(k=Count('id')).values('k') annotated = { o['pk']: o for o in Order.annotate_overpayments(Order.objects).filter( pk__in=[o.pk for o in ctx['orders']] ).annotate( pcnt=Subquery(s, output_field=IntegerField()) ).values( 'pk', 'pcnt', 'is_overpaid', 'is_underpaid', 'is_pending_with_full_payment', 'has_external_refund', 'has_pending_refund' ) } for o in ctx['orders']: if o.pk not in annotated: continue o.pcnt = annotated.get(o.pk)['pcnt'] o.is_overpaid = annotated.get(o.pk)['is_overpaid'] o.is_underpaid = annotated.get(o.pk)['is_underpaid'] o.is_pending_with_full_payment = annotated.get(o.pk)['is_pending_with_full_payment'] o.has_external_refund = annotated.get(o.pk)['has_external_refund'] o.has_pending_refund = annotated.get(o.pk)['has_pending_refund'] if ctx['page_obj'].paginator.count < 1000: # Performance safeguard: Only count positions if the data set is small ctx['sums'] = self.get_queryset().annotate( pcnt=Subquery(s, output_field=IntegerField()) ).aggregate( s=Sum('total'), pc=Sum('pcnt'), c=Count('id') ) else: ctx['sums'] = self.get_queryset().aggregate(s=Sum('total'), c=Count('id')) return ctx @cached_property def filter_form(self): return EventOrderFilterForm(data=self.request.GET, event=self.request.event) class OrderView(EventPermissionRequiredMixin, DetailView): context_object_name = 'order' model = Order def get_object(self, queryset=None): try: return Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) except Order.DoesNotExist: raise Http404() def _redirect_back(self): return redirect('control:event.order', event=self.request.event.slug, organizer=self.request.event.organizer.slug, code=self.order.code) @cached_property def order(self): return self.get_object() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['can_generate_invoice'] = invoice_qualified(self.order) and ( self.request.event.settings.invoice_generate in ('admin', 'user', 'paid', 'True') ) and ( not self.order.invoices.exists() or ( self.order.status in (Order.STATUS_PAID, Order.STATUS_PENDING) and self.order.invoices.filter(is_cancellation=True).count() >= self.order.invoices.filter(is_cancellation=False).count() ) ) return ctx def get_order_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) class OrderDetail(OrderView): template_name = 'pretixcontrol/order/index.html' permission = 'can_view_orders' def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['items'] = self.get_items() ctx['event'] = self.request.event ctx['payments'] = self.order.payments.order_by('-created') ctx['refunds'] = self.order.refunds.select_related('payment').order_by('-created') for p in ctx['payments']: if p.payment_provider: p.html_info = (p.payment_provider.payment_control_render(self.request, p) or "").strip() for r in ctx['refunds']: if r.payment_provider: r.html_info = (r.payment_provider.refund_control_render(self.request, r) or "").strip() ctx['invoices'] = list(self.order.invoices.all().select_related('event')) ctx['comment_form'] = CommentForm(initial={ 'comment': self.order.comment, 'checkin_attention': self.order.checkin_attention }) ctx['display_locale'] = dict(settings.LANGUAGES)[self.object.locale or self.request.event.settings.locale] ctx['overpaid'] = self.order.pending_sum * -1 ctx['sales_channel'] = get_all_sales_channels().get(self.order.sales_channel) ctx['download_buttons'] = self.download_buttons return ctx @cached_property def download_buttons(self): buttons = [] responses = register_ticket_outputs.send(self.request.event) for receiver, response in responses: provider = response(self.request.event) buttons.append({ 'text': provider.download_button_text or 'Ticket', 'icon': provider.download_button_icon or 'fa-download', 'identifier': provider.identifier, 'multi': provider.multi_download_enabled, 'javascript_required': provider.javascript_required }) return buttons def get_items(self): queryset = self.object.all_positions cartpos = queryset.order_by( 'item', 'variation' ).select_related( 'item', 'variation', 'addon_to', 'tax_rule' ).prefetch_related( 'item__questions', 'issued_gift_cards', Prefetch('answers', queryset=QuestionAnswer.objects.prefetch_related('options').select_related('question')), 'checkins', 'checkins__list' ).order_by('positionid') positions = [] for p in cartpos: responses = question_form_fields.send(sender=self.request.event, position=p) p.additional_fields = [] data = p.meta_info_data for r, response in sorted(responses, key=lambda r: str(r[0])): if response: for key, value in response.items(): p.additional_fields.append({ 'answer': data.get('question_form_data', {}).get(key), 'question': value.label }) p.has_questions = ( p.additional_fields or (p.item.admission and self.request.event.settings.attendee_names_asked) or (p.item.admission and self.request.event.settings.attendee_emails_asked) or p.item.questions.all() ) p.cache_answers() positions.append(p) positions.sort(key=lambda p: p.sort_key) return { 'positions': positions, 'raw': cartpos, 'total': self.object.total, 'fees': self.object.all_fees.all(), 'net_total': self.object.net_total, 'tax_total': self.object.tax_total, } class OrderDownload(AsyncAction, OrderView): task = generate permission = 'can_view_orders' def get_success_url(self, value): return self.get_self_url() def get_error_url(self): return self.get_order_url() def get_self_url(self): return reverse('control:event.order.download.ticket', kwargs=self.kwargs) @cached_property def output(self): responses = register_ticket_outputs.send(self.request.event) for receiver, response in responses: provider = response(self.request.event) if provider.identifier == self.kwargs.get('output'): return provider @cached_property def order_position(self): try: return self.order.positions.get(pk=self.kwargs.get('position')) except OrderPosition.DoesNotExist: return None def get(self, request, *args, **kwargs): if 'async_id' in request.GET and settings.HAS_CELERY: return self.get_result(request) ct = self.get_last_ct() if ct: return self.success(ct) return self.http_method_not_allowed(request) def post(self, request, *args, **kwargs): if not self.output: return self.error(_('You requested an invalid ticket output type.')) if not self.order_position: raise Http404(_('Unknown order code or not authorized to access this order.')) if 'position' in kwargs and not self.order_position.generate_ticket: return self.error(_('Ticket download is not enabled for this product.')) ct = self.get_last_ct() if ct: return self.success(ct) return self.do('orderposition' if 'position' in kwargs else 'order', self.order_position.pk if 'position' in kwargs else self.order.pk, self.output.identifier) def get_success_message(self, value): return "" def success(self, value): if "ajax" in self.request.POST or "ajax" in self.request.GET: return JsonResponse({ 'ready': True, 'success': True, 'redirect': self.get_success_url(value), 'message': str(self.get_success_message(value)) }) if isinstance(value, CachedTicket): if value.type == 'text/uri-list': resp = HttpResponseRedirect(value.file.file.read()) return resp else: resp = FileResponse(value.file.file, content_type=value.type) resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}-{}{}"'.format( self.request.event.slug.upper(), self.order.code, self.order_position.positionid, self.output.identifier, value.extension ) return resp elif isinstance(value, CachedCombinedTicket): resp = FileResponse(value.file.file, content_type=value.type) resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}{}"'.format( self.request.event.slug.upper(), self.order.code, self.output.identifier, value.extension ) return resp else: return redirect(self.get_self_url()) def get_last_ct(self): if 'position' in self.kwargs: ct = CachedTicket.objects.filter( order_position=self.order_position, provider=self.output.identifier, file__isnull=False ).last() else: ct = CachedCombinedTicket.objects.filter( order=self.order, provider=self.output.identifier, file__isnull=False ).last() if not ct or not ct.file: return None return ct class OrderComment(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): form = CommentForm(self.request.POST) if form.is_valid(): if form.cleaned_data.get('comment') != self.order.comment: self.order.comment = form.cleaned_data.get('comment') self.order.log_action('pretix.event.order.comment', user=self.request.user, data={ 'new_comment': form.cleaned_data.get('comment') }) if form.cleaned_data.get('checkin_attention') != self.order.checkin_attention: self.order.checkin_attention = form.cleaned_data.get('checkin_attention') self.order.log_action('pretix.event.order.checkin_attention', user=self.request.user, data={ 'new_value': form.cleaned_data.get('checkin_attention') }) self.order.save(update_fields=['checkin_attention', 'comment']) messages.success(self.request, _('The comment has been updated.')) else: messages.error(self.request, _('Could not update the comment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderApprove(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.require_approval: try: approve_order(self.order, user=self.request.user) except OrderError as e: messages.error(self.request, str(e)) else: messages.success(self.request, _('The order has been approved.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/approve.html', { 'order': self.order, }) class OrderDelete(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.testmode: try: with transaction.atomic(): self.order.gracefully_delete(user=self.request.user) messages.success(self.request, _('The order has been deleted.')) return redirect(reverse('control:event.orders', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.organizer.slug, })) except ProtectedError: messages.error(self.request, _('The order could not be deleted as some constraints (e.g. data created ' 'by plug-ins) do not allow it.')) return self.get(self.request, *self.args, **self.kwargs) return redirect(self.get_order_url()) def get(self, *args, **kwargs): if not self.order.testmode: messages.error(self.request, _('Only orders created in test mode can be deleted.')) return redirect(self.get_order_url()) return render(self.request, 'pretixcontrol/order/delete.html', { 'order': self.order, }) class OrderDeny(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.require_approval: try: deny_order(self.order, user=self.request.user, comment=self.request.POST.get('comment'), send_mail=self.request.POST.get('send_email') == 'on') except OrderError as e: messages.error(self.request, str(e)) else: messages.success(self.request, _('The order has been denied and is therefore now canceled.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/deny.html', { 'order': self.order, }) class OrderPaymentCancel(OrderView): permission = 'can_change_orders' @cached_property def payment(self): return get_object_or_404(self.order.payments, pk=self.kwargs['payment']) def post(self, *args, **kwargs): if self.payment.state in (OrderPayment.PAYMENT_STATE_CREATED, OrderPayment.PAYMENT_STATE_PENDING): try: with transaction.atomic(): self.payment.payment_provider.cancel_payment(self.payment) self.order.log_action('pretix.event.order.payment.canceled', { 'local_id': self.payment.local_id, 'provider': self.payment.provider, }, user=self.request.user if self.request.user.is_authenticated else None) except PaymentException as e: self.order.log_action( 'pretix.event.order.payment.canceled.failed', { 'local_id': self.payment.local_id, 'provider': self.payment.provider, 'error': str(e) }, user=self.request.user if self.request.user.is_authenticated else None, ) messages.error(self.request, str(e)) else: messages.success(self.request, _('This payment has been canceled.')) else: messages.error(self.request, _('This payment can not be canceled at the moment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/pay_cancel.html', { 'order': self.order, }) class OrderRefundCancel(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state in (OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT, OrderRefund.REFUND_STATE_EXTERNAL): with transaction.atomic(): self.refund.state = OrderRefund.REFUND_STATE_CANCELED self.refund.save() self.order.log_action('pretix.event.order.refund.canceled', { 'local_id': self.refund.local_id, 'provider': self.refund.provider, }, user=self.request.user) messages.success(self.request, _('The refund has been canceled.')) else: messages.error(self.request, _('This refund can not be canceled at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_cancel.html', { 'order': self.order, }) class OrderRefundProcess(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state == OrderRefund.REFUND_STATE_EXTERNAL: self.refund.done(user=self.request.user) if self.request.POST.get("action") == "r" and self.order.status != Order.STATUS_CANCELED: mark_order_refunded(self.order, user=self.request.user) elif not (self.order.status == Order.STATUS_PAID and self.order.pending_sum <= 0): self.order.status = Order.STATUS_PENDING self.order.set_expires( now(), self.order.event.subevents.filter( id__in=self.order.positions.values_list('subevent_id', flat=True)) ) self.order.save(update_fields=['status', 'expires']) messages.success(self.request, _('The refund has been processed.')) else: messages.error(self.request, _('This refund can not be processed at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_process.html', { 'order': self.order, 'refund': self.refund, 'pending_sum': self.order.pending_sum + self.refund.amount, 'propose_cancel': self.order.pending_sum + self.refund.amount >= self.order.total }) class OrderRefundDone(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state in (OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT): self.refund.done(user=self.request.user) messages.success(self.request, _('The refund has been marked as done.')) else: messages.error(self.request, _('This refund can not be processed at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_done.html', { 'order': self.order, }) class OrderPaymentConfirm(OrderView): permission = 'can_change_orders' @cached_property def payment(self): return get_object_or_404(self.order.payments, pk=self.kwargs['payment']) @cached_property def mark_paid_form(self): return ConfirmPaymentForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) def post(self, *args, **kwargs): if self.payment.state in (OrderPayment.PAYMENT_STATE_CREATED, OrderPayment.PAYMENT_STATE_PENDING): if not self.mark_paid_form.is_valid(): return render(self.request, 'pretixcontrol/order/pay_complete.html', { 'form': self.mark_paid_form, 'order': self.order, }) try: self.payment.confirm(user=self.request.user, count_waitinglist=False, force=self.mark_paid_form.cleaned_data.get('force', False)) except Quota.QuotaExceededException as e: messages.error(self.request, str(e)) except PaymentException as e: messages.error(self.request, str(e)) except SendMailException: messages.warning(self.request, _('The payment has been marked as complete, but we were unable to send a ' 'confirmation mail.')) else: messages.success(self.request, _('The payment has been marked as complete.')) else: messages.error(self.request, _('This payment can not be confirmed at the moment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/pay_complete.html', { 'form': self.mark_paid_form, 'order': self.order, }) class OrderRefundView(OrderView): permission = 'can_change_orders' @cached_property def start_form(self): return OrderRefundForm( order=self.order, data=self.request.POST if self.request.method == "POST" else ( self.request.GET if "start-action" in self.request.GET else None ), prefix='start', initial={ 'partial_amount': self.order.payment_refund_sum, 'action': ( 'mark_pending' if self.order.status == Order.STATUS_PAID else 'do_nothing' ) } ) def choose_form(self): payments = list(self.order.payments.filter(state=OrderPayment.PAYMENT_STATE_CONFIRMED)) if self.start_form.cleaned_data.get('mode') == 'full': full_refund = self.order.payment_refund_sum else: full_refund = self.start_form.cleaned_data.get('partial_amount') proposals = self.order.propose_auto_refunds(full_refund, payments=payments) to_refund = full_refund - sum(proposals.values()) for p in payments: p.propose_refund = proposals.get(p, 0) if 'perform' in self.request.POST: refund_selected = Decimal('0.00') refunds = [] is_valid = True manual_value = self.request.POST.get('refund-manual', '0') or '0' manual_value = formats.sanitize_separators(manual_value) try: manual_value = Decimal(manual_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: refund_selected += manual_value if manual_value: refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=( OrderRefund.REFUND_STATE_DONE if self.request.POST.get('manual_state') == 'done' else OrderRefund.REFUND_STATE_CREATED ), amount=manual_value, provider='manual' )) giftcard_value = self.request.POST.get('refund-new-giftcard', '0') or '0' giftcard_value = formats.sanitize_separators(giftcard_value) try: giftcard_value = Decimal(giftcard_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if giftcard_value: refund_selected += giftcard_value giftcard = self.request.organizer.issued_gift_cards.create( currency=self.request.event.currency, testmode=self.order.testmode ) giftcard.log_action('pretix.giftcards.created', user=self.request.user, data={}) refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_CREATED, execution_date=now(), amount=giftcard_value, provider='giftcard', info=json.dumps({ 'gift_card': giftcard.pk }) )) offsetting_value = self.request.POST.get('refund-offsetting', '0') or '0' offsetting_value = formats.sanitize_separators(offsetting_value) try: offsetting_value = Decimal(offsetting_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if offsetting_value: refund_selected += offsetting_value try: order = Order.objects.get(code=self.request.POST.get('order-offsetting'), event__organizer=self.request.organizer) except Order.DoesNotExist: messages.error(self.request, _('You entered an order that could not be found.')) is_valid = False else: refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_DONE, execution_date=now(), amount=offsetting_value, provider='offsetting', info=json.dumps({ 'orders': [order.code] }) )) for p in payments: value = self.request.POST.get('refund-{}'.format(p.pk), '0') or '0' value = formats.sanitize_separators(value) try: value = Decimal(value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if value == 0: continue elif value > p.available_amount: messages.error(self.request, _('You can not refund more than the amount of a ' 'payment that is not yet refunded.')) is_valid = False break elif value != p.amount and not p.partial_refund_possible: messages.error(self.request, _('You selected a partial refund for a payment method that ' 'only supports full refunds.')) is_valid = False break elif (p.partial_refund_possible or p.full_refund_possible) and value > 0: refund_selected += value refunds.append(OrderRefund( order=self.order, payment=p, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_CREATED, amount=value, provider=p.provider )) any_success = False if refund_selected == full_refund and is_valid: for r in refunds: r.save() self.order.log_action('pretix.event.order.refund.created', { 'local_id': r.local_id, 'provider': r.provider, }, user=self.request.user) if r.payment or r.provider == "offsetting" or r.provider == "giftcard": try: r.payment_provider.execute_refund(r) except PaymentException as e: r.state = OrderRefund.REFUND_STATE_FAILED r.save() messages.error(self.request, _('One of the refunds failed to be processed. You should ' 'retry to refund in a different way. The error message ' 'was: {}').format(str(e))) else: any_success = True if r.state == OrderRefund.REFUND_STATE_DONE: messages.success(self.request, _('A refund of {} has been processed.').format( money_filter(r.amount, self.request.event.currency) )) elif r.state == OrderRefund.REFUND_STATE_CREATED: messages.info(self.request, _('A refund of {} has been saved, but not yet ' 'fully executed. You can mark it as complete ' 'below.').format( money_filter(r.amount, self.request.event.currency) )) else: any_success = True if any_success: if self.start_form.cleaned_data.get('action') == 'mark_refunded': mark_order_refunded(self.order, user=self.request.user) elif self.start_form.cleaned_data.get('action') == 'mark_pending': if not (self.order.status == Order.STATUS_PAID and self.order.pending_sum <= 0): self.order.status = Order.STATUS_PENDING self.order.set_expires( now(), self.order.event.subevents.filter( id__in=self.order.positions.values_list('subevent_id', flat=True)) ) self.order.save(update_fields=['status', 'expires']) if giftcard_value and self.order.email: messages.success(self.request, _('A new gift card was created. You can now send the user their ' 'gift card code.')) return redirect(reverse('control:event.order.sendmail', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) + '?' + urlencode({ 'subject': _('Your gift card code'), 'message': _('Hello,\n\nwe have refunded you {amount} for your order.\n\nYou can use the gift ' 'card code {giftcard} to pay for future ticket purchases in our shop.\n\n' 'Your {event} team').format( event="{event}", amount=money_filter(giftcard_value, self.request.event.currency), giftcard=giftcard.secret, ) })) return redirect(self.get_order_url()) else: messages.error(self.request, _('The refunds you selected do not match the selected total refund ' 'amount.')) return render(self.request, 'pretixcontrol/order/refund_choose.html', { 'payments': payments, 'remainder': to_refund, 'order': self.order, 'partial_amount': ( self.request.POST.get('start-partial_amount') if self.request.method == 'POST' else self.request.GET.get('start-partial_amount') ), 'start_form': self.start_form }) def post(self, *args, **kwargs): if self.start_form.is_valid(): return self.choose_form() return self.get(*args, **kwargs) def get(self, *args, **kwargs): if self.start_form.is_valid(): return self.choose_form() return render(self.request, 'pretixcontrol/order/refund_start.html', { 'form': self.start_form, 'order': self.order, }) class OrderTransition(OrderView): permission = 'can_change_orders' @cached_property def mark_paid_form(self): return MarkPaidForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) @cached_property def mark_canceled_form(self): return CancelForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) def post(self, *args, **kwargs): to = self.request.POST.get('status', '') if self.order.status in (Order.STATUS_PENDING, Order.STATUS_EXPIRED) and to == 'p' and self.mark_paid_form.is_valid(): ps = self.mark_paid_form.cleaned_data['amount'] try: p = self.order.payments.get( state__in=(OrderPayment.PAYMENT_STATE_PENDING, OrderPayment.PAYMENT_STATE_CREATED), provider='manual', amount=ps ) except OrderPayment.DoesNotExist: for p in self.order.payments.filter(state__in=(OrderPayment.PAYMENT_STATE_PENDING, OrderPayment.PAYMENT_STATE_CREATED)): try: with transaction.atomic(): p.payment_provider.cancel_payment(p) self.order.log_action('pretix.event.order.payment.canceled', { 'local_id': p.local_id, 'provider': p.provider, }, user=self.request.user if self.request.user.is_authenticated else None) except PaymentException as e: self.order.log_action( 'pretix.event.order.payment.canceled.failed', { 'local_id': p.local_id, 'provider': p.provider, 'error': str(e) }, user=self.request.user if self.request.user.is_authenticated else None, ) p = self.order.payments.create( state=OrderPayment.PAYMENT_STATE_CREATED, provider='manual', amount=ps, fee=None ) payment_date = None if self.mark_paid_form.cleaned_data['payment_date'] != now().date(): payment_date = make_aware(datetime.combine( self.mark_paid_form.cleaned_data['payment_date'], time(hour=0, minute=0, second=0) ), self.order.event.timezone) try: p.confirm(user=self.request.user, count_waitinglist=False, payment_date=payment_date, force=self.mark_paid_form.cleaned_data.get('force', False)) except Quota.QuotaExceededException as e: p.state = OrderPayment.PAYMENT_STATE_FAILED p.save() self.order.log_action('pretix.event.order.payment.failed', { 'local_id': p.local_id, 'provider': p.provider, 'message': str(e) }) messages.error(self.request, str(e)) except PaymentException as e: p.state = OrderPayment.PAYMENT_STATE_FAILED p.save() self.order.log_action('pretix.event.order.payment.failed', { 'local_id': p.local_id, 'provider': p.provider, 'message': str(e) }) messages.error(self.request, str(e)) except SendMailException: messages.warning(self.request, _('The order has been marked as paid, but we were unable to send a ' 'confirmation mail.')) else: messages.success(self.request, _('The payment has been created successfully.')) elif self.order.cancel_allowed() and to == 'c' and self.mark_canceled_form.is_valid(): try: cancel_order(self.order, user=self.request.user, send_mail=self.mark_canceled_form.cleaned_data['send_email'], cancellation_fee=self.mark_canceled_form.cleaned_data.get('cancellation_fee')) except OrderError as e: messages.error(self.request, str(e)) else: self.order.refresh_from_db() if self.order.pending_sum < 0: messages.success(self.request, _('The order has been canceled. You can now select how you want to ' 'transfer the money back to the user.')) return redirect(reverse('control:event.order.refunds.start', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) + '?start-action=do_nothing&start-mode=partial&start-partial_amount={}'.format( self.order.pending_sum * -1 )) messages.success(self.request, _('The order has been canceled.')) elif self.order.status == Order.STATUS_PENDING and to == 'e': mark_order_expired(self.order, user=self.request.user) messages.success(self.request, _('The order has been marked as expired.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): to = self.request.GET.get('status', '') if self.order.status in (Order.STATUS_PENDING, Order.STATUS_EXPIRED) and to == 'p': return render(self.request, 'pretixcontrol/order/pay.html', { 'form': self.mark_paid_form, 'order': self.order, }) elif self.order.cancel_allowed() and to == 'c': return render(self.request, 'pretixcontrol/order/cancel.html', { 'form': self.mark_canceled_form, 'order': self.order, }) else: return HttpResponseNotAllowed(['POST']) class OrderInvoiceCreate(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): has_inv = self.order.invoices.exists() and not ( self.order.status in (Order.STATUS_PAID, Order.STATUS_PENDING) and self.order.invoices.filter(is_cancellation=True).count() >= self.order.invoices.filter(is_cancellation=False).count() ) if self.request.event.settings.get('invoice_generate') not in ('admin', 'user', 'paid', 'True') or not invoice_qualified(self.order): messages.error(self.request, _('You cannot generate an invoice for this order.')) elif has_inv: messages.error(self.request, _('An invoice for this order already exists.')) else: inv = generate_invoice(self.order) self.order.log_action('pretix.event.order.invoice.generated', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been generated.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderCheckVATID(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: messages.error(self.request, _('No VAT ID specified.')) return redirect(self.get_order_url()) else: if not ia.vat_id: messages.error(self.request, _('No VAT ID specified.')) return redirect(self.get_order_url()) if not ia.country: messages.error(self.request, _('No country specified.')) return redirect(self.get_order_url()) if str(ia.country) not in EU_COUNTRIES: messages.error(self.request, _('VAT ID could not be checked since a non-EU country has been ' 'specified.')) return redirect(self.get_order_url()) if ia.vat_id[:2] != cc_to_vat_prefix(str(ia.country)): messages.error(self.request, _('Your VAT ID does not match the selected country.')) return redirect(self.get_order_url()) try: result = vat_moss.id.validate(ia.vat_id) if result: country_code, normalized_id, company_name = result ia.vat_id_validated = True ia.vat_id = normalized_id ia.save() except vat_moss.errors.InvalidError: messages.error(self.request, _('This VAT ID is not valid.')) except vat_moss.errors.WebServiceUnavailableError: logger.exception('VAT ID checking failed for country {}'.format(ia.country)) messages.error(self.request, _('The VAT ID could not be checked, as the VAT checking service of ' 'the country is currently not available.')) else: messages.success(self.request, _('This VAT ID is valid.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): # NOQA return HttpResponseNotAllowed(['POST']) class OrderInvoiceRegenerate(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: inv = self.order.invoices.get(pk=kwargs.get('id')) except Invoice.DoesNotExist: messages.error(self.request, _('Unknown invoice.')) else: if inv.canceled: messages.error(self.request, _('The invoice has already been canceled.')) elif inv.shredded: messages.error(self.request, _('The invoice has been cleaned of personal data.')) else: inv = regenerate_invoice(inv) self.order.log_action('pretix.event.order.invoice.regenerated', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been regenerated.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): # NOQA return HttpResponseNotAllowed(['POST']) class OrderInvoiceReissue(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: inv = self.order.invoices.get(pk=kwargs.get('id')) except Invoice.DoesNotExist: messages.error(self.request, _('Unknown invoice.')) else: if inv.canceled: messages.error(self.request, _('The invoice has already been canceled.')) elif inv.shredded: messages.error(self.request, _('The invoice has been cleaned of personal data.')) else: c = generate_cancellation(inv) if self.order.status != Order.STATUS_CANCELED: inv = generate_invoice(self.order) else: inv = c self.order.log_action('pretix.event.order.invoice.reissued', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been reissued.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): # NOQA return HttpResponseNotAllowed(['POST']) class OrderResendLink(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: if 'position' in kwargs: p = get_object_or_404(self.order.positions, pk=kwargs['position']) p.resend_link(user=self.request.user) else: self.order.resend_link(user=self.request.user) except SendMailException: messages.error(self.request, _('There was an error sending the mail. Please try again later.')) return redirect(self.get_order_url()) messages.success(self.request, _('The email has been queued to be sent.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class InvoiceDownload(EventPermissionRequiredMixin, View): permission = 'can_view_orders' def get_order_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.invoice.order.code }) def get(self, request, *args, **kwargs): try: self.invoice = Invoice.objects.get( event=self.request.event, id=self.kwargs['invoice'] ) except Invoice.DoesNotExist: raise Http404(_('This invoice has not been found')) if not self.invoice.file: invoice_pdf(self.invoice.pk) self.invoice = Invoice.objects.get(pk=self.invoice.pk) if self.invoice.shredded: messages.error(request, _('The invoice file is no longer stored on the server.')) return redirect(self.get_order_url()) if not self.invoice.file: # This happens if we have celery installed and the file will be generated in the background messages.warning(request, _('The invoice file has not yet been generated, we will generate it for you ' 'now. Please try again in a few seconds.')) return redirect(self.get_order_url()) try: resp = FileResponse(self.invoice.file.file, content_type='application/pdf') except FileNotFoundError: invoice_pdf_task.apply(args=(self.invoice.pk,)) return self.get(request, *args, **kwargs) resp['Content-Disposition'] = 'inline; filename="{}.pdf"'.format(self.invoice.number) resp._csp_ignore = True # Some browser's PDF readers do not work with CSP return resp class OrderExtend(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.form.is_valid(): try: extend_order( self.order, new_date=self.form.cleaned_data.get('expires'), force=self.form.cleaned_data.get('quota_ignore', False), user=self.request.user ) messages.success(self.request, _('The payment term has been changed.')) except OrderError as e: messages.error(self.request, str(e)) return self._redirect_here() except LockTimeoutException: messages.error(self.request, _('We were not able to process the request completely as the ' 'server was too busy.')) return self._redirect_back() else: return self.get(*args, **kwargs) def dispatch(self, request, *args, **kwargs): if self.order.status not in (Order.STATUS_PENDING, Order.STATUS_EXPIRED): messages.error(self.request, _('This action is only allowed for pending orders.')) return self._redirect_back() return super().dispatch(request, *kwargs, **kwargs) def _redirect_here(self): return redirect('control:event.order.extend', event=self.request.event.slug, organizer=self.request.event.organizer.slug, code=self.order.code) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/extend.html', { 'order': self.order, 'form': self.form, }) @cached_property def form(self): return ExtendForm(instance=self.order, data=self.request.POST if self.request.method == "POST" else None) class OrderChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change.html' def dispatch(self, request, *args, **kwargs): if self.order.status not in (Order.STATUS_PENDING, Order.STATUS_PAID): messages.error(self.request, _('This action is only allowed for pending or paid orders.')) return self._redirect_back() return super().dispatch(request, *args, **kwargs) @cached_property def other_form(self): return OtherOperationsForm(prefix='other', order=self.order, data=self.request.POST if self.request.method == "POST" else None) @cached_property def add_formset(self): ff = formset_factory( OrderPositionAddForm, formset=OrderPositionAddFormset, can_order=False, can_delete=True, extra=0 ) return ff( prefix='add', order=self.order, data=self.request.POST if self.request.method == "POST" else None ) @cached_property def fees(self): fees = list(self.order.fees.all()) for f in fees: f.form = OrderFeeChangeForm(prefix='of-{}'.format(f.pk), instance=f, data=self.request.POST if self.request.method == "POST" else None) try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: ia = None f.apply_tax = self.request.event.settings.tax_rate_default and self.request.event.settings.tax_rate_default.tax_applicable(invoice_address=ia) return fees @cached_property def positions(self): positions = list(self.order.positions.all()) for p in positions: p.form = OrderPositionChangeForm(prefix='op-{}'.format(p.pk), instance=p, initial={'seat': p.seat.seat_guid if p.seat else None}, data=self.request.POST if self.request.method == "POST" else None) try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: ia = None p.apply_tax = p.item.tax_rule and p.item.tax_rule.tax_applicable(invoice_address=ia) return positions def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['positions'] = self.positions ctx['fees'] = self.fees ctx['add_formset'] = self.add_formset ctx['other_form'] = self.other_form return ctx def _process_other(self, ocm): if not self.other_form.is_valid(): return False else: if self.other_form.cleaned_data['recalculate_taxes']: ocm.recalculate_taxes() return True def _process_add(self, ocm): if not self.add_formset.is_valid(): return False else: for f in self.add_formset.forms: if f in self.add_formset.deleted_forms or not f.has_changed(): continue if '-' in f.cleaned_data['itemvar']: itemid, varid = f.cleaned_data['itemvar'].split('-') else: itemid, varid = f.cleaned_data['itemvar'], None item = Item.objects.get(pk=itemid, event=self.request.event) if varid: variation = ItemVariation.objects.get(pk=varid, item=item) else: variation = None try: ocm.add_position(item, variation, f.cleaned_data['price'], f.cleaned_data.get('addon_to'), f.cleaned_data.get('subevent'), f.cleaned_data.get('seat')) except OrderError as e: f.custom_error = str(e) return False return True def _process_fees(self, ocm): for f in self.fees: if not f.form.is_valid(): return False try: if f.form.cleaned_data['operation_cancel']: ocm.cancel_fee(f) continue if f.form.cleaned_data['value'] != f.value: ocm.change_fee(f, f.form.cleaned_data['value']) except OrderError as e: f.custom_error = str(e) return False return True def _process_change(self, ocm): for p in self.positions: if not p.form.is_valid(): return False try: if p.form.cleaned_data['operation_cancel']: ocm.cancel(p) continue if p.form.cleaned_data['itemvar']: if '-' in p.form.cleaned_data['itemvar']: itemid, varid = p.form.cleaned_data['itemvar'].split('-') else: itemid, varid = p.form.cleaned_data['itemvar'], None item = Item.objects.get(pk=itemid, event=self.request.event) if varid: variation = ItemVariation.objects.get(pk=varid, item=item) else: variation = None if item != p.item or variation != p.variation: ocm.change_item(p, item, variation) if self.request.event.has_subevents and p.form.cleaned_data['subevent'] and p.form.cleaned_data['subevent'] != p.subevent: ocm.change_subevent(p, p.form.cleaned_data['subevent']) if p.seat and p.form.cleaned_data['seat'] and p.form.cleaned_data['seat'] != p.seat.seat_guid: ocm.change_seat(p, p.form.cleaned_data['seat']) if p.form.cleaned_data['price'] != p.price: ocm.change_price(p, p.form.cleaned_data['price']) if p.form.cleaned_data['operation_split']: ocm.split(p) if p.form.cleaned_data['operation_secret']: ocm.regenerate_secret(p) except OrderError as e: p.custom_error = str(e) return False return True def post(self, *args, **kwargs): notify = self.other_form.cleaned_data['notify'] if self.other_form.is_valid() else True ocm = OrderChangeManager( self.order, user=self.request.user, notify=notify, reissue_invoice=self.other_form.cleaned_data['reissue_invoice'] if self.other_form.is_valid() else True ) form_valid = self._process_add(ocm) and self._process_fees(ocm) and self._process_change(ocm) and self._process_other(ocm) if not form_valid: messages.error(self.request, _('An error occurred. Please see the details below.')) else: try: ocm.commit(check_quotas=not self.other_form.cleaned_data['ignore_quotas']) except OrderError as e: messages.error(self.request, str(e)) else: if notify: messages.success(self.request, _('The order has been changed and the user has been notified.')) else: messages.success(self.request, _('The order has been changed.')) return self._redirect_back() return self.get(*args, **kwargs) class OrderModifyInformation(OrderQuestionsViewMixin, OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_questions.html' only_user_visible = False all_optional = True def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['other_form'] = self.other_form return ctx @cached_property def other_form(self): return OtherOperationsForm(prefix='other', order=self.order, initial={'notify': False}, data=self.request.POST if self.request.method == "POST" else None) def post(self, request, *args, **kwargs): failed = not self.save() or not self.invoice_form.is_valid() or not self.other_form.is_valid() notify = self.other_form.cleaned_data['notify'] if self.other_form.is_valid() else True if failed: messages.error(self.request, _("We had difficulties processing your input. Please review the errors below.")) return self.get(request, *args, **kwargs) if notify: notify_user_changed_order(self.order) if hasattr(self.invoice_form, 'save'): self.invoice_form.save() self.order.log_action('pretix.event.order.modified', { 'invoice_data': self.invoice_form.cleaned_data, 'data': [{ k: (f.cleaned_data.get(k).name if isinstance(f.cleaned_data.get(k), File) else f.cleaned_data.get(k)) for k in f.changed_data } for f in self.forms] }, user=request.user) if self.invoice_form.has_changed(): success_message = ('The invoice address has been updated. If you want to generate a new invoice, ' 'you need to do this manually.') messages.success(self.request, _(success_message)) tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) order_modified.send(sender=self.request.event, order=self.order) return redirect(self.get_order_url()) class OrderContactChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_contact.html' def get_context_data(self, **kwargs): ctx = super().get_context_data() ctx['form'] = self.form return ctx @cached_property def form(self): return OrderContactForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None ) def post(self, *args, **kwargs): old_email = self.order.email changed = False if self.form.is_valid(): new_email = self.form.cleaned_data['email'] if new_email != old_email: changed = True self.order.log_action( 'pretix.event.order.contact.changed', data={ 'old_email': old_email, 'new_email': self.form.cleaned_data['email'], }, user=self.request.user, ) if self.form.cleaned_data['regenerate_secrets']: changed = True self.order.secret = generate_secret() for op in self.order.all_positions.all(): op.secret = generate_position_secret() op.save() tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) self.order.log_action('pretix.event.order.secret.changed', user=self.request.user) self.form.save() if changed: messages.success(self.request, _('The order has been changed.')) else: messages.success(self.request, _('Nothing about the order had to be changed.')) return redirect(self.get_order_url()) return self.get(*args, **kwargs) class OrderLocaleChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_locale.html' def get_context_data(self, **kwargs): ctx = super().get_context_data() ctx['form'] = self.form return ctx @cached_property def form(self): return OrderLocaleForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None ) def post(self, *args, **kwargs): old_locale = self.order.locale if self.form.is_valid(): self.order.log_action( 'pretix.event.order.locale.changed', data={ 'old_locale': old_locale, 'new_locale': self.form.cleaned_data['locale'], }, user=self.request.user, ) self.form.save() tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) messages.success(self.request, _('The order has been changed.')) return redirect(self.get_order_url()) return self.get(*args, **kwargs) class OrderViewMixin: def get_object(self, queryset=None): try: return Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) except Order.DoesNotExist: raise Http404() @cached_property def order(self): return self.get_object() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['order'] = self.order return ctx class OrderSendMail(EventPermissionRequiredMixin, OrderViewMixin, FormView): template_name = 'pretixcontrol/order/sendmail.html' permission = 'can_change_orders' form_class = OrderMailForm def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['order'] = Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) kwargs['initial'] = {} if self.request.GET.get('subject'): kwargs['initial']['subject'] = self.request.GET.get('subject') if self.request.GET.get('message'): kwargs['initial']['message'] = self.request.GET.get('message') return kwargs def form_invalid(self, form): messages.error(self.request, _('We could not send the email. See below for details.')) return super().form_invalid(form) def form_valid(self, form): order = Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) self.preview_output = {} with language(order.locale): email_context = get_email_context(event=order.event, order=order) email_template = LazyI18nString(form.cleaned_data['message']) email_content = render_mail(email_template, email_context) if self.request.POST.get('action') == 'preview': self.preview_output = { 'subject': _('Subject: {subject}').format(subject=form.cleaned_data['subject']), 'html': markdown_compile_email(email_content) } return self.get(self.request, *self.args, **self.kwargs) else: try: order.send_mail( form.cleaned_data['subject'], email_template, email_context, 'pretix.event.order.email.custom_sent', self.request.user, auto_email=False ) messages.success(self.request, _('Your message has been queued and will be sent to {}.'.format(order.email))) except SendMailException: messages.error( self.request, _('Failed to send mail to the following user: {}'.format(order.email)) ) return super(OrderSendMail, self).form_valid(form) def get_success_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.kwargs['code'] }) def get_context_data(self, *args, **kwargs): ctx = super().get_context_data(*args, **kwargs) ctx['preview_output'] = getattr(self, 'preview_output', None) return ctx class OrderEmailHistory(EventPermissionRequiredMixin, OrderViewMixin, ListView): template_name = 'pretixcontrol/order/mail_history.html' permission = 'can_view_orders' model = LogEntry context_object_name = 'logs' paginate_by = 10 def get_queryset(self): order = get_object_or_404( Order, event=self.request.event, code=self.kwargs['code'].upper() ) qs = order.all_logentries() qs = qs.filter( action_type__contains="order.email" ) return qs class AnswerDownload(EventPermissionRequiredMixin, OrderViewMixin, ListView): permission = 'can_view_orders' def get(self, request, *args, **kwargs): answid = kwargs.get('answer') token = request.GET.get('token', '') answer = get_object_or_404(QuestionAnswer, orderposition__order=self.order, id=answid) if not answer.file: raise Http404() if not check_token(request, answer, token): raise Http404(_("This link is no longer valid. Please go back, refresh the page, and try again.")) ftype, ignored = mimetypes.guess_type(answer.file.name) resp = FileResponse(answer.file, content_type=ftype or 'application/binary') resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}-{}"'.format( self.request.event.slug.upper(), self.order.code, answer.orderposition.positionid, os.path.basename(answer.file.name).split('.', 1)[1] ) return resp class OverView(EventPermissionRequiredMixin, TemplateView): template_name = 'pretixcontrol/orders/overview.html' permission = 'can_view_orders' @cached_property def filter_form(self): return OverviewFilterForm(data=self.request.GET, event=self.request.event) def get_context_data(self, **kwargs): ctx = super().get_context_data() if self.filter_form.is_valid(): ctx['items_by_category'], ctx['total'] = order_overview( self.request.event, subevent=self.filter_form.cleaned_data.get('subevent'), date_filter=self.filter_form.cleaned_data['date_axis'], date_from=self.filter_form.cleaned_data['date_from'], date_until=self.filter_form.cleaned_data['date_until'], fees=True ) else: ctx['items_by_category'], ctx['total'] = order_overview( self.request.event, fees=True ) ctx['subevent_warning'] = ( self.request.event.has_subevents and self.filter_form.is_valid() and self.filter_form.cleaned_data.get('subevent') and OrderFee.objects.filter(order__event=self.request.event).exclude(value=0).exists() ) ctx['filter_form'] = self.filter_form return ctx class OrderGo(EventPermissionRequiredMixin, View): permission = 'can_view_orders' def get_order(self, code): try: return Order.objects.get(code=code, event=self.request.event) except Order.DoesNotExist: return Order.objects.get(code=Order.normalize_code(code), event=self.request.event) def get(self, request, *args, **kwargs): code = request.GET.get("code", "").upper().strip() if '://' in code: m = re.match('.*/ORDER/([A-Z0-9]{' + str(settings.ENTROPY['order_code']) + '})/.*', code) if m: code = m.group(1) try: if code.startswith(request.event.slug.upper()): code = code[len(request.event.slug):] if code.startswith('-'): code = code[1:] order = self.get_order(code) return redirect('control:event.order', event=request.event.slug, organizer=request.event.organizer.slug, code=order.code) except Order.DoesNotExist: try: i = self.request.event.invoices.get(Q(invoice_no=code) | Q(full_invoice_no=code)) return redirect('control:event.order', event=request.event.slug, organizer=request.event.organizer.slug, code=i.order.code) except Invoice.DoesNotExist: pass messages.error(request, _('There is no order with the given order code.')) return redirect('control:event.orders', event=request.event.slug, organizer=request.event.organizer.slug) class ExportMixin: @cached_property def exporters(self): exporters = [] responses = register_data_exporters.send(self.request.event) for ex in sorted([response(self.request.event) for r, response in responses], key=lambda ex: str(ex.verbose_name)): if self.request.GET.get("identifier") and ex.identifier != self.request.GET.get("identifier"): continue # Use form parse cycle to generate useful defaults test_form = ExporterForm(data=self.request.GET, prefix=ex.identifier) test_form.fields = ex.export_form_fields test_form.is_valid() initial = { k: v for k, v in test_form.cleaned_data.items() if ex.identifier + "-" + k in self.request.GET } ex.form = ExporterForm( data=(self.request.POST if self.request.method == 'POST' else None), prefix=ex.identifier, initial=initial ) ex.form.fields = ex.export_form_fields exporters.append(ex) return exporters class ExportDoView(EventPermissionRequiredMixin, ExportMixin, AsyncAction, View): permission = 'can_view_orders' known_errortypes = ['ExportError'] task = export def get_success_message(self, value): return None def get_success_url(self, value): return reverse('cachedfile.download', kwargs={'id': str(value)}) def get_error_url(self): return reverse('control:event.orders.export', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug }) @cached_property def exporter(self): for ex in self.exporters: if ex.identifier == self.request.POST.get("exporter"): return ex def post(self, request, *args, **kwargs): if not self.exporter: messages.error(self.request, _('The selected exporter was not found.')) return redirect('control:event.orders.export', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug }) if not self.exporter.form.is_valid(): messages.error(self.request, _('There was a problem processing your input. See below for error details.')) return self.get(request, *args, **kwargs) cf = CachedFile() cf.date = now() cf.expires = now() + timedelta(days=3) cf.save() return self.do(self.request.event.id, str(cf.id), self.exporter.identifier, self.exporter.form.cleaned_data) class ExportView(EventPermissionRequiredMixin, ExportMixin, TemplateView): permission = 'can_view_orders' template_name = 'pretixcontrol/orders/export.html' def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['exporters'] = self.exporters return ctx class RefundList(EventPermissionRequiredMixin, PaginationMixin, ListView): model = OrderRefund context_object_name = 'refunds' template_name = 'pretixcontrol/orders/refunds.html' permission = 'can_view_orders' def get_queryset(self): qs = OrderRefund.objects.filter( order__event=self.request.event ).select_related('order') if self.filter_form.is_valid(): qs = self.filter_form.filter_qs(qs) return qs.distinct() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['filter_form'] = self.filter_form return ctx @cached_property def filter_form(self): return RefundFilterForm(data=self.request.GET, event=self.request.event, initial={'status': 'open'})

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import json import logging import mimetypes import os import re from datetime import datetime, time, timedelta from decimal import Decimal, DecimalException from urllib.parse import urlencode import vat_moss.id from django.conf import settings from django.contrib import messages from django.core.files import File from django.db import transaction from django.db.models import ( Count, IntegerField, OuterRef, Prefetch, ProtectedError, Q, Subquery, Sum, ) from django.forms import formset_factory from django.http import ( FileResponse, Http404, HttpResponseNotAllowed, HttpResponseRedirect, JsonResponse, ) from django.shortcuts import get_object_or_404, redirect, render from django.urls import reverse from django.utils import formats from django.utils.functional import cached_property from django.utils.http import is_safe_url from django.utils.timezone import make_aware, now from django.utils.translation import ugettext_lazy as _ from django.views.generic import ( DetailView, FormView, ListView, TemplateView, View, ) from i18nfield.strings import LazyI18nString from pretix.base.channels import get_all_sales_channels from pretix.base.email import get_email_context from pretix.base.i18n import language from pretix.base.models import ( CachedCombinedTicket, CachedFile, CachedTicket, Invoice, InvoiceAddress, Item, ItemVariation, LogEntry, Order, QuestionAnswer, Quota, generate_position_secret, generate_secret, ) from pretix.base.models.orders import ( OrderFee, OrderPayment, OrderPosition, OrderRefund, ) from pretix.base.models.tax import EU_COUNTRIES, cc_to_vat_prefix from pretix.base.payment import PaymentException from pretix.base.services import tickets from pretix.base.services.export import export from pretix.base.services.invoices import ( generate_cancellation, generate_invoice, invoice_pdf, invoice_pdf_task, invoice_qualified, regenerate_invoice, ) from pretix.base.services.locking import LockTimeoutException from pretix.base.services.mail import SendMailException, render_mail from pretix.base.services.orders import ( OrderChangeManager, OrderError, approve_order, cancel_order, deny_order, extend_order, mark_order_expired, mark_order_refunded, notify_user_changed_order, ) from pretix.base.services.stats import order_overview from pretix.base.services.tickets import generate from pretix.base.signals import ( order_modified, register_data_exporters, register_ticket_outputs, ) from pretix.base.templatetags.money import money_filter from pretix.base.templatetags.rich_text import markdown_compile_email from pretix.base.views.mixins import OrderQuestionsViewMixin from pretix.base.views.tasks import AsyncAction from pretix.control.forms.filter import ( EventOrderFilterForm, OverviewFilterForm, RefundFilterForm, ) from pretix.control.forms.orders import ( CancelForm, CommentForm, ConfirmPaymentForm, ExporterForm, ExtendForm, MarkPaidForm, OrderContactForm, OrderFeeChangeForm, OrderLocaleForm, OrderMailForm, OrderPositionAddForm, OrderPositionAddFormset, OrderPositionChangeForm, OrderRefundForm, OtherOperationsForm, ) from pretix.control.permissions import EventPermissionRequiredMixin from pretix.control.views import PaginationMixin from pretix.helpers.safedownload import check_token from pretix.presale.signals import question_form_fields logger = logging.getLogger(__name__) class OrderList(EventPermissionRequiredMixin, PaginationMixin, ListView): model = Order context_object_name = 'orders' template_name = 'pretixcontrol/orders/index.html' permission = 'can_view_orders' def get_queryset(self): qs = Order.objects.filter( event=self.request.event ).select_related('invoice_address') if self.filter_form.is_valid(): qs = self.filter_form.filter_qs(qs) return qs def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['filter_form'] = self.filter_form s = OrderPosition.objects.filter( order=OuterRef('pk') ).order_by().values('order').annotate(k=Count('id')).values('k') annotated = { o['pk']: o for o in Order.annotate_overpayments(Order.objects).filter( pk__in=[o.pk for o in ctx['orders']] ).annotate( pcnt=Subquery(s, output_field=IntegerField()) ).values( 'pk', 'pcnt', 'is_overpaid', 'is_underpaid', 'is_pending_with_full_payment', 'has_external_refund', 'has_pending_refund' ) } for o in ctx['orders']: if o.pk not in annotated: continue o.pcnt = annotated.get(o.pk)['pcnt'] o.is_overpaid = annotated.get(o.pk)['is_overpaid'] o.is_underpaid = annotated.get(o.pk)['is_underpaid'] o.is_pending_with_full_payment = annotated.get(o.pk)['is_pending_with_full_payment'] o.has_external_refund = annotated.get(o.pk)['has_external_refund'] o.has_pending_refund = annotated.get(o.pk)['has_pending_refund'] if ctx['page_obj'].paginator.count < 1000: ctx['sums'] = self.get_queryset().annotate( pcnt=Subquery(s, output_field=IntegerField()) ).aggregate( s=Sum('total'), pc=Sum('pcnt'), c=Count('id') ) else: ctx['sums'] = self.get_queryset().aggregate(s=Sum('total'), c=Count('id')) return ctx @cached_property def filter_form(self): return EventOrderFilterForm(data=self.request.GET, event=self.request.event) class OrderView(EventPermissionRequiredMixin, DetailView): context_object_name = 'order' model = Order def get_object(self, queryset=None): try: return Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) except Order.DoesNotExist: raise Http404() def _redirect_back(self): return redirect('control:event.order', event=self.request.event.slug, organizer=self.request.event.organizer.slug, code=self.order.code) @cached_property def order(self): return self.get_object() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['can_generate_invoice'] = invoice_qualified(self.order) and ( self.request.event.settings.invoice_generate in ('admin', 'user', 'paid', 'True') ) and ( not self.order.invoices.exists() or ( self.order.status in (Order.STATUS_PAID, Order.STATUS_PENDING) and self.order.invoices.filter(is_cancellation=True).count() >= self.order.invoices.filter(is_cancellation=False).count() ) ) return ctx def get_order_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) class OrderDetail(OrderView): template_name = 'pretixcontrol/order/index.html' permission = 'can_view_orders' def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['items'] = self.get_items() ctx['event'] = self.request.event ctx['payments'] = self.order.payments.order_by('-created') ctx['refunds'] = self.order.refunds.select_related('payment').order_by('-created') for p in ctx['payments']: if p.payment_provider: p.html_info = (p.payment_provider.payment_control_render(self.request, p) or "").strip() for r in ctx['refunds']: if r.payment_provider: r.html_info = (r.payment_provider.refund_control_render(self.request, r) or "").strip() ctx['invoices'] = list(self.order.invoices.all().select_related('event')) ctx['comment_form'] = CommentForm(initial={ 'comment': self.order.comment, 'checkin_attention': self.order.checkin_attention }) ctx['display_locale'] = dict(settings.LANGUAGES)[self.object.locale or self.request.event.settings.locale] ctx['overpaid'] = self.order.pending_sum * -1 ctx['sales_channel'] = get_all_sales_channels().get(self.order.sales_channel) ctx['download_buttons'] = self.download_buttons return ctx @cached_property def download_buttons(self): buttons = [] responses = register_ticket_outputs.send(self.request.event) for receiver, response in responses: provider = response(self.request.event) buttons.append({ 'text': provider.download_button_text or 'Ticket', 'icon': provider.download_button_icon or 'fa-download', 'identifier': provider.identifier, 'multi': provider.multi_download_enabled, 'javascript_required': provider.javascript_required }) return buttons def get_items(self): queryset = self.object.all_positions cartpos = queryset.order_by( 'item', 'variation' ).select_related( 'item', 'variation', 'addon_to', 'tax_rule' ).prefetch_related( 'item__questions', 'issued_gift_cards', Prefetch('answers', queryset=QuestionAnswer.objects.prefetch_related('options').select_related('question')), 'checkins', 'checkins__list' ).order_by('positionid') positions = [] for p in cartpos: responses = question_form_fields.send(sender=self.request.event, position=p) p.additional_fields = [] data = p.meta_info_data for r, response in sorted(responses, key=lambda r: str(r[0])): if response: for key, value in response.items(): p.additional_fields.append({ 'answer': data.get('question_form_data', {}).get(key), 'question': value.label }) p.has_questions = ( p.additional_fields or (p.item.admission and self.request.event.settings.attendee_names_asked) or (p.item.admission and self.request.event.settings.attendee_emails_asked) or p.item.questions.all() ) p.cache_answers() positions.append(p) positions.sort(key=lambda p: p.sort_key) return { 'positions': positions, 'raw': cartpos, 'total': self.object.total, 'fees': self.object.all_fees.all(), 'net_total': self.object.net_total, 'tax_total': self.object.tax_total, } class OrderDownload(AsyncAction, OrderView): task = generate permission = 'can_view_orders' def get_success_url(self, value): return self.get_self_url() def get_error_url(self): return self.get_order_url() def get_self_url(self): return reverse('control:event.order.download.ticket', kwargs=self.kwargs) @cached_property def output(self): responses = register_ticket_outputs.send(self.request.event) for receiver, response in responses: provider = response(self.request.event) if provider.identifier == self.kwargs.get('output'): return provider @cached_property def order_position(self): try: return self.order.positions.get(pk=self.kwargs.get('position')) except OrderPosition.DoesNotExist: return None def get(self, request, *args, **kwargs): if 'async_id' in request.GET and settings.HAS_CELERY: return self.get_result(request) ct = self.get_last_ct() if ct: return self.success(ct) return self.http_method_not_allowed(request) def post(self, request, *args, **kwargs): if not self.output: return self.error(_('You requested an invalid ticket output type.')) if not self.order_position: raise Http404(_('Unknown order code or not authorized to access this order.')) if 'position' in kwargs and not self.order_position.generate_ticket: return self.error(_('Ticket download is not enabled for this product.')) ct = self.get_last_ct() if ct: return self.success(ct) return self.do('orderposition' if 'position' in kwargs else 'order', self.order_position.pk if 'position' in kwargs else self.order.pk, self.output.identifier) def get_success_message(self, value): return "" def success(self, value): if "ajax" in self.request.POST or "ajax" in self.request.GET: return JsonResponse({ 'ready': True, 'success': True, 'redirect': self.get_success_url(value), 'message': str(self.get_success_message(value)) }) if isinstance(value, CachedTicket): if value.type == 'text/uri-list': resp = HttpResponseRedirect(value.file.file.read()) return resp else: resp = FileResponse(value.file.file, content_type=value.type) resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}-{}{}"'.format( self.request.event.slug.upper(), self.order.code, self.order_position.positionid, self.output.identifier, value.extension ) return resp elif isinstance(value, CachedCombinedTicket): resp = FileResponse(value.file.file, content_type=value.type) resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}{}"'.format( self.request.event.slug.upper(), self.order.code, self.output.identifier, value.extension ) return resp else: return redirect(self.get_self_url()) def get_last_ct(self): if 'position' in self.kwargs: ct = CachedTicket.objects.filter( order_position=self.order_position, provider=self.output.identifier, file__isnull=False ).last() else: ct = CachedCombinedTicket.objects.filter( order=self.order, provider=self.output.identifier, file__isnull=False ).last() if not ct or not ct.file: return None return ct class OrderComment(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): form = CommentForm(self.request.POST) if form.is_valid(): if form.cleaned_data.get('comment') != self.order.comment: self.order.comment = form.cleaned_data.get('comment') self.order.log_action('pretix.event.order.comment', user=self.request.user, data={ 'new_comment': form.cleaned_data.get('comment') }) if form.cleaned_data.get('checkin_attention') != self.order.checkin_attention: self.order.checkin_attention = form.cleaned_data.get('checkin_attention') self.order.log_action('pretix.event.order.checkin_attention', user=self.request.user, data={ 'new_value': form.cleaned_data.get('checkin_attention') }) self.order.save(update_fields=['checkin_attention', 'comment']) messages.success(self.request, _('The comment has been updated.')) else: messages.error(self.request, _('Could not update the comment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderApprove(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.require_approval: try: approve_order(self.order, user=self.request.user) except OrderError as e: messages.error(self.request, str(e)) else: messages.success(self.request, _('The order has been approved.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/approve.html', { 'order': self.order, }) class OrderDelete(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.testmode: try: with transaction.atomic(): self.order.gracefully_delete(user=self.request.user) messages.success(self.request, _('The order has been deleted.')) return redirect(reverse('control:event.orders', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.organizer.slug, })) except ProtectedError: messages.error(self.request, _('The order could not be deleted as some constraints (e.g. data created ' 'by plug-ins) do not allow it.')) return self.get(self.request, *self.args, **self.kwargs) return redirect(self.get_order_url()) def get(self, *args, **kwargs): if not self.order.testmode: messages.error(self.request, _('Only orders created in test mode can be deleted.')) return redirect(self.get_order_url()) return render(self.request, 'pretixcontrol/order/delete.html', { 'order': self.order, }) class OrderDeny(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.order.require_approval: try: deny_order(self.order, user=self.request.user, comment=self.request.POST.get('comment'), send_mail=self.request.POST.get('send_email') == 'on') except OrderError as e: messages.error(self.request, str(e)) else: messages.success(self.request, _('The order has been denied and is therefore now canceled.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/deny.html', { 'order': self.order, }) class OrderPaymentCancel(OrderView): permission = 'can_change_orders' @cached_property def payment(self): return get_object_or_404(self.order.payments, pk=self.kwargs['payment']) def post(self, *args, **kwargs): if self.payment.state in (OrderPayment.PAYMENT_STATE_CREATED, OrderPayment.PAYMENT_STATE_PENDING): try: with transaction.atomic(): self.payment.payment_provider.cancel_payment(self.payment) self.order.log_action('pretix.event.order.payment.canceled', { 'local_id': self.payment.local_id, 'provider': self.payment.provider, }, user=self.request.user if self.request.user.is_authenticated else None) except PaymentException as e: self.order.log_action( 'pretix.event.order.payment.canceled.failed', { 'local_id': self.payment.local_id, 'provider': self.payment.provider, 'error': str(e) }, user=self.request.user if self.request.user.is_authenticated else None, ) messages.error(self.request, str(e)) else: messages.success(self.request, _('This payment has been canceled.')) else: messages.error(self.request, _('This payment can not be canceled at the moment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/pay_cancel.html', { 'order': self.order, }) class OrderRefundCancel(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state in (OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT, OrderRefund.REFUND_STATE_EXTERNAL): with transaction.atomic(): self.refund.state = OrderRefund.REFUND_STATE_CANCELED self.refund.save() self.order.log_action('pretix.event.order.refund.canceled', { 'local_id': self.refund.local_id, 'provider': self.refund.provider, }, user=self.request.user) messages.success(self.request, _('The refund has been canceled.')) else: messages.error(self.request, _('This refund can not be canceled at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_cancel.html', { 'order': self.order, }) class OrderRefundProcess(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state == OrderRefund.REFUND_STATE_EXTERNAL: self.refund.done(user=self.request.user) if self.request.POST.get("action") == "r" and self.order.status != Order.STATUS_CANCELED: mark_order_refunded(self.order, user=self.request.user) elif not (self.order.status == Order.STATUS_PAID and self.order.pending_sum <= 0): self.order.status = Order.STATUS_PENDING self.order.set_expires( now(), self.order.event.subevents.filter( id__in=self.order.positions.values_list('subevent_id', flat=True)) ) self.order.save(update_fields=['status', 'expires']) messages.success(self.request, _('The refund has been processed.')) else: messages.error(self.request, _('This refund can not be processed at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_process.html', { 'order': self.order, 'refund': self.refund, 'pending_sum': self.order.pending_sum + self.refund.amount, 'propose_cancel': self.order.pending_sum + self.refund.amount >= self.order.total }) class OrderRefundDone(OrderView): permission = 'can_change_orders' @cached_property def refund(self): return get_object_or_404(self.order.refunds, pk=self.kwargs['refund']) def post(self, *args, **kwargs): if self.refund.state in (OrderRefund.REFUND_STATE_CREATED, OrderRefund.REFUND_STATE_TRANSIT): self.refund.done(user=self.request.user) messages.success(self.request, _('The refund has been marked as done.')) else: messages.error(self.request, _('This refund can not be processed at the moment.')) if "next" in self.request.GET and is_safe_url(self.request.GET.get("next"), allowed_hosts=None): return redirect(self.request.GET.get("next")) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/refund_done.html', { 'order': self.order, }) class OrderPaymentConfirm(OrderView): permission = 'can_change_orders' @cached_property def payment(self): return get_object_or_404(self.order.payments, pk=self.kwargs['payment']) @cached_property def mark_paid_form(self): return ConfirmPaymentForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) def post(self, *args, **kwargs): if self.payment.state in (OrderPayment.PAYMENT_STATE_CREATED, OrderPayment.PAYMENT_STATE_PENDING): if not self.mark_paid_form.is_valid(): return render(self.request, 'pretixcontrol/order/pay_complete.html', { 'form': self.mark_paid_form, 'order': self.order, }) try: self.payment.confirm(user=self.request.user, count_waitinglist=False, force=self.mark_paid_form.cleaned_data.get('force', False)) except Quota.QuotaExceededException as e: messages.error(self.request, str(e)) except PaymentException as e: messages.error(self.request, str(e)) except SendMailException: messages.warning(self.request, _('The payment has been marked as complete, but we were unable to send a ' 'confirmation mail.')) else: messages.success(self.request, _('The payment has been marked as complete.')) else: messages.error(self.request, _('This payment can not be confirmed at the moment.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/pay_complete.html', { 'form': self.mark_paid_form, 'order': self.order, }) class OrderRefundView(OrderView): permission = 'can_change_orders' @cached_property def start_form(self): return OrderRefundForm( order=self.order, data=self.request.POST if self.request.method == "POST" else ( self.request.GET if "start-action" in self.request.GET else None ), prefix='start', initial={ 'partial_amount': self.order.payment_refund_sum, 'action': ( 'mark_pending' if self.order.status == Order.STATUS_PAID else 'do_nothing' ) } ) def choose_form(self): payments = list(self.order.payments.filter(state=OrderPayment.PAYMENT_STATE_CONFIRMED)) if self.start_form.cleaned_data.get('mode') == 'full': full_refund = self.order.payment_refund_sum else: full_refund = self.start_form.cleaned_data.get('partial_amount') proposals = self.order.propose_auto_refunds(full_refund, payments=payments) to_refund = full_refund - sum(proposals.values()) for p in payments: p.propose_refund = proposals.get(p, 0) if 'perform' in self.request.POST: refund_selected = Decimal('0.00') refunds = [] is_valid = True manual_value = self.request.POST.get('refund-manual', '0') or '0' manual_value = formats.sanitize_separators(manual_value) try: manual_value = Decimal(manual_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: refund_selected += manual_value if manual_value: refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=( OrderRefund.REFUND_STATE_DONE if self.request.POST.get('manual_state') == 'done' else OrderRefund.REFUND_STATE_CREATED ), amount=manual_value, provider='manual' )) giftcard_value = self.request.POST.get('refund-new-giftcard', '0') or '0' giftcard_value = formats.sanitize_separators(giftcard_value) try: giftcard_value = Decimal(giftcard_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if giftcard_value: refund_selected += giftcard_value giftcard = self.request.organizer.issued_gift_cards.create( currency=self.request.event.currency, testmode=self.order.testmode ) giftcard.log_action('pretix.giftcards.created', user=self.request.user, data={}) refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_CREATED, execution_date=now(), amount=giftcard_value, provider='giftcard', info=json.dumps({ 'gift_card': giftcard.pk }) )) offsetting_value = self.request.POST.get('refund-offsetting', '0') or '0' offsetting_value = formats.sanitize_separators(offsetting_value) try: offsetting_value = Decimal(offsetting_value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if offsetting_value: refund_selected += offsetting_value try: order = Order.objects.get(code=self.request.POST.get('order-offsetting'), event__organizer=self.request.organizer) except Order.DoesNotExist: messages.error(self.request, _('You entered an order that could not be found.')) is_valid = False else: refunds.append(OrderRefund( order=self.order, payment=None, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_DONE, execution_date=now(), amount=offsetting_value, provider='offsetting', info=json.dumps({ 'orders': [order.code] }) )) for p in payments: value = self.request.POST.get('refund-{}'.format(p.pk), '0') or '0' value = formats.sanitize_separators(value) try: value = Decimal(value) except (DecimalException, TypeError): messages.error(self.request, _('You entered an invalid number.')) is_valid = False else: if value == 0: continue elif value > p.available_amount: messages.error(self.request, _('You can not refund more than the amount of a ' 'payment that is not yet refunded.')) is_valid = False break elif value != p.amount and not p.partial_refund_possible: messages.error(self.request, _('You selected a partial refund for a payment method that ' 'only supports full refunds.')) is_valid = False break elif (p.partial_refund_possible or p.full_refund_possible) and value > 0: refund_selected += value refunds.append(OrderRefund( order=self.order, payment=p, source=OrderRefund.REFUND_SOURCE_ADMIN, state=OrderRefund.REFUND_STATE_CREATED, amount=value, provider=p.provider )) any_success = False if refund_selected == full_refund and is_valid: for r in refunds: r.save() self.order.log_action('pretix.event.order.refund.created', { 'local_id': r.local_id, 'provider': r.provider, }, user=self.request.user) if r.payment or r.provider == "offsetting" or r.provider == "giftcard": try: r.payment_provider.execute_refund(r) except PaymentException as e: r.state = OrderRefund.REFUND_STATE_FAILED r.save() messages.error(self.request, _('One of the refunds failed to be processed. You should ' 'retry to refund in a different way. The error message ' 'was: {}').format(str(e))) else: any_success = True if r.state == OrderRefund.REFUND_STATE_DONE: messages.success(self.request, _('A refund of {} has been processed.').format( money_filter(r.amount, self.request.event.currency) )) elif r.state == OrderRefund.REFUND_STATE_CREATED: messages.info(self.request, _('A refund of {} has been saved, but not yet ' 'fully executed. You can mark it as complete ' 'below.').format( money_filter(r.amount, self.request.event.currency) )) else: any_success = True if any_success: if self.start_form.cleaned_data.get('action') == 'mark_refunded': mark_order_refunded(self.order, user=self.request.user) elif self.start_form.cleaned_data.get('action') == 'mark_pending': if not (self.order.status == Order.STATUS_PAID and self.order.pending_sum <= 0): self.order.status = Order.STATUS_PENDING self.order.set_expires( now(), self.order.event.subevents.filter( id__in=self.order.positions.values_list('subevent_id', flat=True)) ) self.order.save(update_fields=['status', 'expires']) if giftcard_value and self.order.email: messages.success(self.request, _('A new gift card was created. You can now send the user their ' 'gift card code.')) return redirect(reverse('control:event.order.sendmail', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) + '?' + urlencode({ 'subject': _('Your gift card code'), 'message': _('Hello,\n\nwe have refunded you {amount} for your order.\n\nYou can use the gift ' 'card code {giftcard} to pay for future ticket purchases in our shop.\n\n' 'Your {event} team').format( event="{event}", amount=money_filter(giftcard_value, self.request.event.currency), giftcard=giftcard.secret, ) })) return redirect(self.get_order_url()) else: messages.error(self.request, _('The refunds you selected do not match the selected total refund ' 'amount.')) return render(self.request, 'pretixcontrol/order/refund_choose.html', { 'payments': payments, 'remainder': to_refund, 'order': self.order, 'partial_amount': ( self.request.POST.get('start-partial_amount') if self.request.method == 'POST' else self.request.GET.get('start-partial_amount') ), 'start_form': self.start_form }) def post(self, *args, **kwargs): if self.start_form.is_valid(): return self.choose_form() return self.get(*args, **kwargs) def get(self, *args, **kwargs): if self.start_form.is_valid(): return self.choose_form() return render(self.request, 'pretixcontrol/order/refund_start.html', { 'form': self.start_form, 'order': self.order, }) class OrderTransition(OrderView): permission = 'can_change_orders' @cached_property def mark_paid_form(self): return MarkPaidForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) @cached_property def mark_canceled_form(self): return CancelForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None, ) def post(self, *args, **kwargs): to = self.request.POST.get('status', '') if self.order.status in (Order.STATUS_PENDING, Order.STATUS_EXPIRED) and to == 'p' and self.mark_paid_form.is_valid(): ps = self.mark_paid_form.cleaned_data['amount'] try: p = self.order.payments.get( state__in=(OrderPayment.PAYMENT_STATE_PENDING, OrderPayment.PAYMENT_STATE_CREATED), provider='manual', amount=ps ) except OrderPayment.DoesNotExist: for p in self.order.payments.filter(state__in=(OrderPayment.PAYMENT_STATE_PENDING, OrderPayment.PAYMENT_STATE_CREATED)): try: with transaction.atomic(): p.payment_provider.cancel_payment(p) self.order.log_action('pretix.event.order.payment.canceled', { 'local_id': p.local_id, 'provider': p.provider, }, user=self.request.user if self.request.user.is_authenticated else None) except PaymentException as e: self.order.log_action( 'pretix.event.order.payment.canceled.failed', { 'local_id': p.local_id, 'provider': p.provider, 'error': str(e) }, user=self.request.user if self.request.user.is_authenticated else None, ) p = self.order.payments.create( state=OrderPayment.PAYMENT_STATE_CREATED, provider='manual', amount=ps, fee=None ) payment_date = None if self.mark_paid_form.cleaned_data['payment_date'] != now().date(): payment_date = make_aware(datetime.combine( self.mark_paid_form.cleaned_data['payment_date'], time(hour=0, minute=0, second=0) ), self.order.event.timezone) try: p.confirm(user=self.request.user, count_waitinglist=False, payment_date=payment_date, force=self.mark_paid_form.cleaned_data.get('force', False)) except Quota.QuotaExceededException as e: p.state = OrderPayment.PAYMENT_STATE_FAILED p.save() self.order.log_action('pretix.event.order.payment.failed', { 'local_id': p.local_id, 'provider': p.provider, 'message': str(e) }) messages.error(self.request, str(e)) except PaymentException as e: p.state = OrderPayment.PAYMENT_STATE_FAILED p.save() self.order.log_action('pretix.event.order.payment.failed', { 'local_id': p.local_id, 'provider': p.provider, 'message': str(e) }) messages.error(self.request, str(e)) except SendMailException: messages.warning(self.request, _('The order has been marked as paid, but we were unable to send a ' 'confirmation mail.')) else: messages.success(self.request, _('The payment has been created successfully.')) elif self.order.cancel_allowed() and to == 'c' and self.mark_canceled_form.is_valid(): try: cancel_order(self.order, user=self.request.user, send_mail=self.mark_canceled_form.cleaned_data['send_email'], cancellation_fee=self.mark_canceled_form.cleaned_data.get('cancellation_fee')) except OrderError as e: messages.error(self.request, str(e)) else: self.order.refresh_from_db() if self.order.pending_sum < 0: messages.success(self.request, _('The order has been canceled. You can now select how you want to ' 'transfer the money back to the user.')) return redirect(reverse('control:event.order.refunds.start', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.order.code }) + '?start-action=do_nothing&start-mode=partial&start-partial_amount={}'.format( self.order.pending_sum * -1 )) messages.success(self.request, _('The order has been canceled.')) elif self.order.status == Order.STATUS_PENDING and to == 'e': mark_order_expired(self.order, user=self.request.user) messages.success(self.request, _('The order has been marked as expired.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): to = self.request.GET.get('status', '') if self.order.status in (Order.STATUS_PENDING, Order.STATUS_EXPIRED) and to == 'p': return render(self.request, 'pretixcontrol/order/pay.html', { 'form': self.mark_paid_form, 'order': self.order, }) elif self.order.cancel_allowed() and to == 'c': return render(self.request, 'pretixcontrol/order/cancel.html', { 'form': self.mark_canceled_form, 'order': self.order, }) else: return HttpResponseNotAllowed(['POST']) class OrderInvoiceCreate(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): has_inv = self.order.invoices.exists() and not ( self.order.status in (Order.STATUS_PAID, Order.STATUS_PENDING) and self.order.invoices.filter(is_cancellation=True).count() >= self.order.invoices.filter(is_cancellation=False).count() ) if self.request.event.settings.get('invoice_generate') not in ('admin', 'user', 'paid', 'True') or not invoice_qualified(self.order): messages.error(self.request, _('You cannot generate an invoice for this order.')) elif has_inv: messages.error(self.request, _('An invoice for this order already exists.')) else: inv = generate_invoice(self.order) self.order.log_action('pretix.event.order.invoice.generated', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been generated.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderCheckVATID(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: messages.error(self.request, _('No VAT ID specified.')) return redirect(self.get_order_url()) else: if not ia.vat_id: messages.error(self.request, _('No VAT ID specified.')) return redirect(self.get_order_url()) if not ia.country: messages.error(self.request, _('No country specified.')) return redirect(self.get_order_url()) if str(ia.country) not in EU_COUNTRIES: messages.error(self.request, _('VAT ID could not be checked since a non-EU country has been ' 'specified.')) return redirect(self.get_order_url()) if ia.vat_id[:2] != cc_to_vat_prefix(str(ia.country)): messages.error(self.request, _('Your VAT ID does not match the selected country.')) return redirect(self.get_order_url()) try: result = vat_moss.id.validate(ia.vat_id) if result: country_code, normalized_id, company_name = result ia.vat_id_validated = True ia.vat_id = normalized_id ia.save() except vat_moss.errors.InvalidError: messages.error(self.request, _('This VAT ID is not valid.')) except vat_moss.errors.WebServiceUnavailableError: logger.exception('VAT ID checking failed for country {}'.format(ia.country)) messages.error(self.request, _('The VAT ID could not be checked, as the VAT checking service of ' 'the country is currently not available.')) else: messages.success(self.request, _('This VAT ID is valid.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderInvoiceRegenerate(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: inv = self.order.invoices.get(pk=kwargs.get('id')) except Invoice.DoesNotExist: messages.error(self.request, _('Unknown invoice.')) else: if inv.canceled: messages.error(self.request, _('The invoice has already been canceled.')) elif inv.shredded: messages.error(self.request, _('The invoice has been cleaned of personal data.')) else: inv = regenerate_invoice(inv) self.order.log_action('pretix.event.order.invoice.regenerated', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been regenerated.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderInvoiceReissue(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: inv = self.order.invoices.get(pk=kwargs.get('id')) except Invoice.DoesNotExist: messages.error(self.request, _('Unknown invoice.')) else: if inv.canceled: messages.error(self.request, _('The invoice has already been canceled.')) elif inv.shredded: messages.error(self.request, _('The invoice has been cleaned of personal data.')) else: c = generate_cancellation(inv) if self.order.status != Order.STATUS_CANCELED: inv = generate_invoice(self.order) else: inv = c self.order.log_action('pretix.event.order.invoice.reissued', user=self.request.user, data={ 'invoice': inv.pk }) messages.success(self.request, _('The invoice has been reissued.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class OrderResendLink(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): try: if 'position' in kwargs: p = get_object_or_404(self.order.positions, pk=kwargs['position']) p.resend_link(user=self.request.user) else: self.order.resend_link(user=self.request.user) except SendMailException: messages.error(self.request, _('There was an error sending the mail. Please try again later.')) return redirect(self.get_order_url()) messages.success(self.request, _('The email has been queued to be sent.')) return redirect(self.get_order_url()) def get(self, *args, **kwargs): return HttpResponseNotAllowed(['POST']) class InvoiceDownload(EventPermissionRequiredMixin, View): permission = 'can_view_orders' def get_order_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.invoice.order.code }) def get(self, request, *args, **kwargs): try: self.invoice = Invoice.objects.get( event=self.request.event, id=self.kwargs['invoice'] ) except Invoice.DoesNotExist: raise Http404(_('This invoice has not been found')) if not self.invoice.file: invoice_pdf(self.invoice.pk) self.invoice = Invoice.objects.get(pk=self.invoice.pk) if self.invoice.shredded: messages.error(request, _('The invoice file is no longer stored on the server.')) return redirect(self.get_order_url()) if not self.invoice.file: messages.warning(request, _('The invoice file has not yet been generated, we will generate it for you ' 'now. Please try again in a few seconds.')) return redirect(self.get_order_url()) try: resp = FileResponse(self.invoice.file.file, content_type='application/pdf') except FileNotFoundError: invoice_pdf_task.apply(args=(self.invoice.pk,)) return self.get(request, *args, **kwargs) resp['Content-Disposition'] = 'inline; filename="{}.pdf"'.format(self.invoice.number) resp._csp_ignore = True return resp class OrderExtend(OrderView): permission = 'can_change_orders' def post(self, *args, **kwargs): if self.form.is_valid(): try: extend_order( self.order, new_date=self.form.cleaned_data.get('expires'), force=self.form.cleaned_data.get('quota_ignore', False), user=self.request.user ) messages.success(self.request, _('The payment term has been changed.')) except OrderError as e: messages.error(self.request, str(e)) return self._redirect_here() except LockTimeoutException: messages.error(self.request, _('We were not able to process the request completely as the ' 'server was too busy.')) return self._redirect_back() else: return self.get(*args, **kwargs) def dispatch(self, request, *args, **kwargs): if self.order.status not in (Order.STATUS_PENDING, Order.STATUS_EXPIRED): messages.error(self.request, _('This action is only allowed for pending orders.')) return self._redirect_back() return super().dispatch(request, *kwargs, **kwargs) def _redirect_here(self): return redirect('control:event.order.extend', event=self.request.event.slug, organizer=self.request.event.organizer.slug, code=self.order.code) def get(self, *args, **kwargs): return render(self.request, 'pretixcontrol/order/extend.html', { 'order': self.order, 'form': self.form, }) @cached_property def form(self): return ExtendForm(instance=self.order, data=self.request.POST if self.request.method == "POST" else None) class OrderChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change.html' def dispatch(self, request, *args, **kwargs): if self.order.status not in (Order.STATUS_PENDING, Order.STATUS_PAID): messages.error(self.request, _('This action is only allowed for pending or paid orders.')) return self._redirect_back() return super().dispatch(request, *args, **kwargs) @cached_property def other_form(self): return OtherOperationsForm(prefix='other', order=self.order, data=self.request.POST if self.request.method == "POST" else None) @cached_property def add_formset(self): ff = formset_factory( OrderPositionAddForm, formset=OrderPositionAddFormset, can_order=False, can_delete=True, extra=0 ) return ff( prefix='add', order=self.order, data=self.request.POST if self.request.method == "POST" else None ) @cached_property def fees(self): fees = list(self.order.fees.all()) for f in fees: f.form = OrderFeeChangeForm(prefix='of-{}'.format(f.pk), instance=f, data=self.request.POST if self.request.method == "POST" else None) try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: ia = None f.apply_tax = self.request.event.settings.tax_rate_default and self.request.event.settings.tax_rate_default.tax_applicable(invoice_address=ia) return fees @cached_property def positions(self): positions = list(self.order.positions.all()) for p in positions: p.form = OrderPositionChangeForm(prefix='op-{}'.format(p.pk), instance=p, initial={'seat': p.seat.seat_guid if p.seat else None}, data=self.request.POST if self.request.method == "POST" else None) try: ia = self.order.invoice_address except InvoiceAddress.DoesNotExist: ia = None p.apply_tax = p.item.tax_rule and p.item.tax_rule.tax_applicable(invoice_address=ia) return positions def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['positions'] = self.positions ctx['fees'] = self.fees ctx['add_formset'] = self.add_formset ctx['other_form'] = self.other_form return ctx def _process_other(self, ocm): if not self.other_form.is_valid(): return False else: if self.other_form.cleaned_data['recalculate_taxes']: ocm.recalculate_taxes() return True def _process_add(self, ocm): if not self.add_formset.is_valid(): return False else: for f in self.add_formset.forms: if f in self.add_formset.deleted_forms or not f.has_changed(): continue if '-' in f.cleaned_data['itemvar']: itemid, varid = f.cleaned_data['itemvar'].split('-') else: itemid, varid = f.cleaned_data['itemvar'], None item = Item.objects.get(pk=itemid, event=self.request.event) if varid: variation = ItemVariation.objects.get(pk=varid, item=item) else: variation = None try: ocm.add_position(item, variation, f.cleaned_data['price'], f.cleaned_data.get('addon_to'), f.cleaned_data.get('subevent'), f.cleaned_data.get('seat')) except OrderError as e: f.custom_error = str(e) return False return True def _process_fees(self, ocm): for f in self.fees: if not f.form.is_valid(): return False try: if f.form.cleaned_data['operation_cancel']: ocm.cancel_fee(f) continue if f.form.cleaned_data['value'] != f.value: ocm.change_fee(f, f.form.cleaned_data['value']) except OrderError as e: f.custom_error = str(e) return False return True def _process_change(self, ocm): for p in self.positions: if not p.form.is_valid(): return False try: if p.form.cleaned_data['operation_cancel']: ocm.cancel(p) continue if p.form.cleaned_data['itemvar']: if '-' in p.form.cleaned_data['itemvar']: itemid, varid = p.form.cleaned_data['itemvar'].split('-') else: itemid, varid = p.form.cleaned_data['itemvar'], None item = Item.objects.get(pk=itemid, event=self.request.event) if varid: variation = ItemVariation.objects.get(pk=varid, item=item) else: variation = None if item != p.item or variation != p.variation: ocm.change_item(p, item, variation) if self.request.event.has_subevents and p.form.cleaned_data['subevent'] and p.form.cleaned_data['subevent'] != p.subevent: ocm.change_subevent(p, p.form.cleaned_data['subevent']) if p.seat and p.form.cleaned_data['seat'] and p.form.cleaned_data['seat'] != p.seat.seat_guid: ocm.change_seat(p, p.form.cleaned_data['seat']) if p.form.cleaned_data['price'] != p.price: ocm.change_price(p, p.form.cleaned_data['price']) if p.form.cleaned_data['operation_split']: ocm.split(p) if p.form.cleaned_data['operation_secret']: ocm.regenerate_secret(p) except OrderError as e: p.custom_error = str(e) return False return True def post(self, *args, **kwargs): notify = self.other_form.cleaned_data['notify'] if self.other_form.is_valid() else True ocm = OrderChangeManager( self.order, user=self.request.user, notify=notify, reissue_invoice=self.other_form.cleaned_data['reissue_invoice'] if self.other_form.is_valid() else True ) form_valid = self._process_add(ocm) and self._process_fees(ocm) and self._process_change(ocm) and self._process_other(ocm) if not form_valid: messages.error(self.request, _('An error occurred. Please see the details below.')) else: try: ocm.commit(check_quotas=not self.other_form.cleaned_data['ignore_quotas']) except OrderError as e: messages.error(self.request, str(e)) else: if notify: messages.success(self.request, _('The order has been changed and the user has been notified.')) else: messages.success(self.request, _('The order has been changed.')) return self._redirect_back() return self.get(*args, **kwargs) class OrderModifyInformation(OrderQuestionsViewMixin, OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_questions.html' only_user_visible = False all_optional = True def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['other_form'] = self.other_form return ctx @cached_property def other_form(self): return OtherOperationsForm(prefix='other', order=self.order, initial={'notify': False}, data=self.request.POST if self.request.method == "POST" else None) def post(self, request, *args, **kwargs): failed = not self.save() or not self.invoice_form.is_valid() or not self.other_form.is_valid() notify = self.other_form.cleaned_data['notify'] if self.other_form.is_valid() else True if failed: messages.error(self.request, _("We had difficulties processing your input. Please review the errors below.")) return self.get(request, *args, **kwargs) if notify: notify_user_changed_order(self.order) if hasattr(self.invoice_form, 'save'): self.invoice_form.save() self.order.log_action('pretix.event.order.modified', { 'invoice_data': self.invoice_form.cleaned_data, 'data': [{ k: (f.cleaned_data.get(k).name if isinstance(f.cleaned_data.get(k), File) else f.cleaned_data.get(k)) for k in f.changed_data } for f in self.forms] }, user=request.user) if self.invoice_form.has_changed(): success_message = ('The invoice address has been updated. If you want to generate a new invoice, ' 'you need to do this manually.') messages.success(self.request, _(success_message)) tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) order_modified.send(sender=self.request.event, order=self.order) return redirect(self.get_order_url()) class OrderContactChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_contact.html' def get_context_data(self, **kwargs): ctx = super().get_context_data() ctx['form'] = self.form return ctx @cached_property def form(self): return OrderContactForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None ) def post(self, *args, **kwargs): old_email = self.order.email changed = False if self.form.is_valid(): new_email = self.form.cleaned_data['email'] if new_email != old_email: changed = True self.order.log_action( 'pretix.event.order.contact.changed', data={ 'old_email': old_email, 'new_email': self.form.cleaned_data['email'], }, user=self.request.user, ) if self.form.cleaned_data['regenerate_secrets']: changed = True self.order.secret = generate_secret() for op in self.order.all_positions.all(): op.secret = generate_position_secret() op.save() tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) self.order.log_action('pretix.event.order.secret.changed', user=self.request.user) self.form.save() if changed: messages.success(self.request, _('The order has been changed.')) else: messages.success(self.request, _('Nothing about the order had to be changed.')) return redirect(self.get_order_url()) return self.get(*args, **kwargs) class OrderLocaleChange(OrderView): permission = 'can_change_orders' template_name = 'pretixcontrol/order/change_locale.html' def get_context_data(self, **kwargs): ctx = super().get_context_data() ctx['form'] = self.form return ctx @cached_property def form(self): return OrderLocaleForm( instance=self.order, data=self.request.POST if self.request.method == "POST" else None ) def post(self, *args, **kwargs): old_locale = self.order.locale if self.form.is_valid(): self.order.log_action( 'pretix.event.order.locale.changed', data={ 'old_locale': old_locale, 'new_locale': self.form.cleaned_data['locale'], }, user=self.request.user, ) self.form.save() tickets.invalidate_cache.apply_async(kwargs={'event': self.request.event.pk, 'order': self.order.pk}) messages.success(self.request, _('The order has been changed.')) return redirect(self.get_order_url()) return self.get(*args, **kwargs) class OrderViewMixin: def get_object(self, queryset=None): try: return Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) except Order.DoesNotExist: raise Http404() @cached_property def order(self): return self.get_object() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['order'] = self.order return ctx class OrderSendMail(EventPermissionRequiredMixin, OrderViewMixin, FormView): template_name = 'pretixcontrol/order/sendmail.html' permission = 'can_change_orders' form_class = OrderMailForm def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['order'] = Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) kwargs['initial'] = {} if self.request.GET.get('subject'): kwargs['initial']['subject'] = self.request.GET.get('subject') if self.request.GET.get('message'): kwargs['initial']['message'] = self.request.GET.get('message') return kwargs def form_invalid(self, form): messages.error(self.request, _('We could not send the email. See below for details.')) return super().form_invalid(form) def form_valid(self, form): order = Order.objects.get( event=self.request.event, code=self.kwargs['code'].upper() ) self.preview_output = {} with language(order.locale): email_context = get_email_context(event=order.event, order=order) email_template = LazyI18nString(form.cleaned_data['message']) email_content = render_mail(email_template, email_context) if self.request.POST.get('action') == 'preview': self.preview_output = { 'subject': _('Subject: {subject}').format(subject=form.cleaned_data['subject']), 'html': markdown_compile_email(email_content) } return self.get(self.request, *self.args, **self.kwargs) else: try: order.send_mail( form.cleaned_data['subject'], email_template, email_context, 'pretix.event.order.email.custom_sent', self.request.user, auto_email=False ) messages.success(self.request, _('Your message has been queued and will be sent to {}.'.format(order.email))) except SendMailException: messages.error( self.request, _('Failed to send mail to the following user: {}'.format(order.email)) ) return super(OrderSendMail, self).form_valid(form) def get_success_url(self): return reverse('control:event.order', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug, 'code': self.kwargs['code'] }) def get_context_data(self, *args, **kwargs): ctx = super().get_context_data(*args, **kwargs) ctx['preview_output'] = getattr(self, 'preview_output', None) return ctx class OrderEmailHistory(EventPermissionRequiredMixin, OrderViewMixin, ListView): template_name = 'pretixcontrol/order/mail_history.html' permission = 'can_view_orders' model = LogEntry context_object_name = 'logs' paginate_by = 10 def get_queryset(self): order = get_object_or_404( Order, event=self.request.event, code=self.kwargs['code'].upper() ) qs = order.all_logentries() qs = qs.filter( action_type__contains="order.email" ) return qs class AnswerDownload(EventPermissionRequiredMixin, OrderViewMixin, ListView): permission = 'can_view_orders' def get(self, request, *args, **kwargs): answid = kwargs.get('answer') token = request.GET.get('token', '') answer = get_object_or_404(QuestionAnswer, orderposition__order=self.order, id=answid) if not answer.file: raise Http404() if not check_token(request, answer, token): raise Http404(_("This link is no longer valid. Please go back, refresh the page, and try again.")) ftype, ignored = mimetypes.guess_type(answer.file.name) resp = FileResponse(answer.file, content_type=ftype or 'application/binary') resp['Content-Disposition'] = 'attachment; filename="{}-{}-{}-{}"'.format( self.request.event.slug.upper(), self.order.code, answer.orderposition.positionid, os.path.basename(answer.file.name).split('.', 1)[1] ) return resp class OverView(EventPermissionRequiredMixin, TemplateView): template_name = 'pretixcontrol/orders/overview.html' permission = 'can_view_orders' @cached_property def filter_form(self): return OverviewFilterForm(data=self.request.GET, event=self.request.event) def get_context_data(self, **kwargs): ctx = super().get_context_data() if self.filter_form.is_valid(): ctx['items_by_category'], ctx['total'] = order_overview( self.request.event, subevent=self.filter_form.cleaned_data.get('subevent'), date_filter=self.filter_form.cleaned_data['date_axis'], date_from=self.filter_form.cleaned_data['date_from'], date_until=self.filter_form.cleaned_data['date_until'], fees=True ) else: ctx['items_by_category'], ctx['total'] = order_overview( self.request.event, fees=True ) ctx['subevent_warning'] = ( self.request.event.has_subevents and self.filter_form.is_valid() and self.filter_form.cleaned_data.get('subevent') and OrderFee.objects.filter(order__event=self.request.event).exclude(value=0).exists() ) ctx['filter_form'] = self.filter_form return ctx class OrderGo(EventPermissionRequiredMixin, View): permission = 'can_view_orders' def get_order(self, code): try: return Order.objects.get(code=code, event=self.request.event) except Order.DoesNotExist: return Order.objects.get(code=Order.normalize_code(code), event=self.request.event) def get(self, request, *args, **kwargs): code = request.GET.get("code", "").upper().strip() if '://' in code: m = re.match('.*/ORDER/([A-Z0-9]{' + str(settings.ENTROPY['order_code']) + '})/.*', code) if m: code = m.group(1) try: if code.startswith(request.event.slug.upper()): code = code[len(request.event.slug):] if code.startswith('-'): code = code[1:] order = self.get_order(code) return redirect('control:event.order', event=request.event.slug, organizer=request.event.organizer.slug, code=order.code) except Order.DoesNotExist: try: i = self.request.event.invoices.get(Q(invoice_no=code) | Q(full_invoice_no=code)) return redirect('control:event.order', event=request.event.slug, organizer=request.event.organizer.slug, code=i.order.code) except Invoice.DoesNotExist: pass messages.error(request, _('There is no order with the given order code.')) return redirect('control:event.orders', event=request.event.slug, organizer=request.event.organizer.slug) class ExportMixin: @cached_property def exporters(self): exporters = [] responses = register_data_exporters.send(self.request.event) for ex in sorted([response(self.request.event) for r, response in responses], key=lambda ex: str(ex.verbose_name)): if self.request.GET.get("identifier") and ex.identifier != self.request.GET.get("identifier"): continue # Use form parse cycle to generate useful defaults test_form = ExporterForm(data=self.request.GET, prefix=ex.identifier) test_form.fields = ex.export_form_fields test_form.is_valid() initial = { k: v for k, v in test_form.cleaned_data.items() if ex.identifier + "-" + k in self.request.GET } ex.form = ExporterForm( data=(self.request.POST if self.request.method == 'POST' else None), prefix=ex.identifier, initial=initial ) ex.form.fields = ex.export_form_fields exporters.append(ex) return exporters class ExportDoView(EventPermissionRequiredMixin, ExportMixin, AsyncAction, View): permission = 'can_view_orders' known_errortypes = ['ExportError'] task = export def get_success_message(self, value): return None def get_success_url(self, value): return reverse('cachedfile.download', kwargs={'id': str(value)}) def get_error_url(self): return reverse('control:event.orders.export', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug }) @cached_property def exporter(self): for ex in self.exporters: if ex.identifier == self.request.POST.get("exporter"): return ex def post(self, request, *args, **kwargs): if not self.exporter: messages.error(self.request, _('The selected exporter was not found.')) return redirect('control:event.orders.export', kwargs={ 'event': self.request.event.slug, 'organizer': self.request.event.organizer.slug }) if not self.exporter.form.is_valid(): messages.error(self.request, _('There was a problem processing your input. See below for error details.')) return self.get(request, *args, **kwargs) cf = CachedFile() cf.date = now() cf.expires = now() + timedelta(days=3) cf.save() return self.do(self.request.event.id, str(cf.id), self.exporter.identifier, self.exporter.form.cleaned_data) class ExportView(EventPermissionRequiredMixin, ExportMixin, TemplateView): permission = 'can_view_orders' template_name = 'pretixcontrol/orders/export.html' def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['exporters'] = self.exporters return ctx class RefundList(EventPermissionRequiredMixin, PaginationMixin, ListView): model = OrderRefund context_object_name = 'refunds' template_name = 'pretixcontrol/orders/refunds.html' permission = 'can_view_orders' def get_queryset(self): qs = OrderRefund.objects.filter( order__event=self.request.event ).select_related('order') if self.filter_form.is_valid(): qs = self.filter_form.filter_qs(qs) return qs.distinct() def get_context_data(self, **kwargs): ctx = super().get_context_data(**kwargs) ctx['filter_form'] = self.filter_form return ctx @cached_property def filter_form(self): return RefundFilterForm(data=self.request.GET, event=self.request.event, initial={'status': 'open'})

true

f7201a7642abbd76d0bee748789b15d308c71b10

9,682

py

Python

vspk/cli/cli.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

19

2016-03-07T12:34:22.000Z

2020-06-11T11:09:02.000Z

vspk/cli/cli.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

40

2016-06-13T15:36:54.000Z

2020-11-10T18:14:43.000Z

vspk/cli/cli.py

mohaimenhasan/vspk-python

4c7b297427048340b250cc3c74d9214dc0d4bde1

[ "BSD-3-Clause" ]

15

2016-06-10T22:06:01.000Z

2020-12-15T18:37:42.000Z

#!/usr/bin/env python # # Copyright (c) 2015, Alcatel-Lucent Inc, 2017 Nokia # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import argparse import sys sys.path.append("../") class _HelpAction(argparse._HelpAction): def __call__(self, parser, namespace, values, option_string=None): parser.print_help() subparsers_actions = [ action for action in parser._actions if isinstance(action, argparse._SubParsersAction)] for subparsers_action in subparsers_actions: for choice, subparser in subparsers_action.choices.items(): print("\n{}:\n{}".format(choice.upper(), "-" * (len(choice) + 1))) print(subparser.format_help()) parser.exit() def main(argv=sys.argv): default_parser = argparse.ArgumentParser(description="CLI for VSD", add_help=False) default_parser.add_argument("-v", "--verbose", help="Activate verbose mode", action="store_true") default_parser.add_argument("--username", help="Username to get an api key or set 'VSD_USERNAME' in your variable environment") default_parser.add_argument("--password", help="Password to get an api key or set 'VSD_PASSWORD' in your variable environment") default_parser.add_argument("--api", help="URL of the API endpoint or set 'VSD_API_URL' in your variable environment") default_parser.add_argument("--version", help="Version of the API or set 'VSD_API_VERSION' in your variable environment") default_parser.add_argument("--enterprise", help="Name of the enterprise to connect or set 'VSD_ENTERPRISE' in your variable environment") default_parser.add_argument("--json", help="Add this option get a JSON output or set VSD_JSON_OUTPUT=True", action="store_true") parser = argparse.ArgumentParser(description="CLI for VSD Software Development Kit", add_help=False) parser.add_argument("-h", "--help", action=_HelpAction, help="help for help if you need some help") subparsers = parser.add_subparsers(dest="command", title="All available commands") # List Command list_parser = subparsers.add_parser("list", description="List all objects", parents=[default_parser]) list_parser.add_argument("list", help="Name of the object (See command 'objects' to list all objects name)") list_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the PARENT_NAME and PARENT_UUID") list_parser.add_argument("-f", "--filter", dest="filter", help="Specify a filter predicate") list_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) list_parser.add_argument("-q", "--query", dest="query_parameters", nargs="*", help="List of Key=Value that will be sent as query parameters", required=False) list_parser.add_argument("-p", "--page", dest="page", help="The page number that needs to be retreived. This value is ignored unless you also configure the page size parameter. Default value is 0", type=int, default=0) list_parser.add_argument("-s", "--page-size", dest="page_size", help="The size of a single page that needs to be retreived. If this is configured, the list command will only return a maximum of this amount of results", type=int) # Count Command count_parser = subparsers.add_parser("count", description="Count all objects", parents=[default_parser]) count_parser.add_argument("count", help="Name of the object (See command 'objects' to list all objects name)") count_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the parent name and its uuid") count_parser.add_argument("-f", "--filter", dest="filter", help="Specify a filter predicate") count_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) count_parser.add_argument("-q", "--query", dest="query_parameters", nargs="*", help="List of Key=Value that will be sent as query parameters", required=False) # Show Command show_parser = subparsers.add_parser("show", description="Show a specific object", parents=[default_parser]) show_parser.add_argument("show", help="Name of the object to show (See command 'objects' to list all objects name)") show_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) show_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) # Create Command create_parser = subparsers.add_parser("create", description="Create a new object", parents=[default_parser]) create_parser.add_argument("create", help="Name of the object to create (See command 'objects' to list all objects name)") create_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the parent name and its uuid") create_parser.add_argument("-p", "--params", dest="params", nargs="*", help="List of Key=Value parameters", required=True) # Update Command update_parser = subparsers.add_parser("update", description="Update an existing object", parents=[default_parser]) update_parser.add_argument("update", help="Name of the object to update (See command 'objects' to list all objects name)") update_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) update_parser.add_argument("-p", "--params", dest="params", nargs="*", help="List of Key=Value parameters", required=True) # Delete Command delete_parser = subparsers.add_parser("delete", description="Delete an existing object", parents=[default_parser]) delete_parser.add_argument("delete", help="Name of the object to update (See command 'objects' to list all objects name)") delete_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) # Assign Command assign_parser = subparsers.add_parser('assign', description="Assign a set of new objects according to their identifier", parents=[default_parser]) assign_parser.add_argument('assign', help='Name of the object to assign (See command `objects` to list all objects name)') assign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to assign', required=True) assign_parser.add_argument('--to', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) # Unassign Command unassign_parser = subparsers.add_parser('unassign', description="Unassign a set of new objects according to their identifier", parents=[default_parser]) unassign_parser.add_argument('unassign', help='Name of the object to unassign (See command `objects` to list all objects name)') unassign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to unassign', required=True) unassign_parser.add_argument('--from', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) # Reassign Command reassign_parser = subparsers.add_parser('reassign', description="Reassign all objects according to their identifier", parents=[default_parser]) reassign_parser.add_argument('reassign', help='Name of the object to reassign (See command `objects` to list all objects name)') reassign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to reassign. If --ids is not specified, it will remove all assigned objects') reassign_parser.add_argument('--to', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) # Resources Command objects_parser = subparsers.add_parser("objects", description="Explore all objects", parents=[default_parser]) objects_parser.add_argument("-f", "--filter", dest="filter", help="Filter by name (ex: -f nsg)") objects_parser.add_argument("-p", "--parent", dest="parent", help="Filter by parent (ex -p enterprise)") objects_parser.add_argument("-c", "--child", dest="child", help="Filter by children (ex: -c domain)") args = parser.parse_args() from commands import CLICommand CLICommand.execute(args) if __name__ == "__main__": main()

68.666667

232

0.727639

import argparse import sys sys.path.append("../") class _HelpAction(argparse._HelpAction): def __call__(self, parser, namespace, values, option_string=None): parser.print_help() subparsers_actions = [ action for action in parser._actions if isinstance(action, argparse._SubParsersAction)] for subparsers_action in subparsers_actions: for choice, subparser in subparsers_action.choices.items(): print("\n{}:\n{}".format(choice.upper(), "-" * (len(choice) + 1))) print(subparser.format_help()) parser.exit() def main(argv=sys.argv): default_parser = argparse.ArgumentParser(description="CLI for VSD", add_help=False) default_parser.add_argument("-v", "--verbose", help="Activate verbose mode", action="store_true") default_parser.add_argument("--username", help="Username to get an api key or set 'VSD_USERNAME' in your variable environment") default_parser.add_argument("--password", help="Password to get an api key or set 'VSD_PASSWORD' in your variable environment") default_parser.add_argument("--api", help="URL of the API endpoint or set 'VSD_API_URL' in your variable environment") default_parser.add_argument("--version", help="Version of the API or set 'VSD_API_VERSION' in your variable environment") default_parser.add_argument("--enterprise", help="Name of the enterprise to connect or set 'VSD_ENTERPRISE' in your variable environment") default_parser.add_argument("--json", help="Add this option get a JSON output or set VSD_JSON_OUTPUT=True", action="store_true") parser = argparse.ArgumentParser(description="CLI for VSD Software Development Kit", add_help=False) parser.add_argument("-h", "--help", action=_HelpAction, help="help for help if you need some help") subparsers = parser.add_subparsers(dest="command", title="All available commands") list_parser = subparsers.add_parser("list", description="List all objects", parents=[default_parser]) list_parser.add_argument("list", help="Name of the object (See command 'objects' to list all objects name)") list_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the PARENT_NAME and PARENT_UUID") list_parser.add_argument("-f", "--filter", dest="filter", help="Specify a filter predicate") list_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) list_parser.add_argument("-q", "--query", dest="query_parameters", nargs="*", help="List of Key=Value that will be sent as query parameters", required=False) list_parser.add_argument("-p", "--page", dest="page", help="The page number that needs to be retreived. This value is ignored unless you also configure the page size parameter. Default value is 0", type=int, default=0) list_parser.add_argument("-s", "--page-size", dest="page_size", help="The size of a single page that needs to be retreived. If this is configured, the list command will only return a maximum of this amount of results", type=int) count_parser = subparsers.add_parser("count", description="Count all objects", parents=[default_parser]) count_parser.add_argument("count", help="Name of the object (See command 'objects' to list all objects name)") count_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the parent name and its uuid") count_parser.add_argument("-f", "--filter", dest="filter", help="Specify a filter predicate") count_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) count_parser.add_argument("-q", "--query", dest="query_parameters", nargs="*", help="List of Key=Value that will be sent as query parameters", required=False) show_parser = subparsers.add_parser("show", description="Show a specific object", parents=[default_parser]) show_parser.add_argument("show", help="Name of the object to show (See command 'objects' to list all objects name)") show_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) show_parser.add_argument("-x", "--fields", dest="fields", help="Specify output fields", nargs="+", type=str) create_parser = subparsers.add_parser("create", description="Create a new object", parents=[default_parser]) create_parser.add_argument("create", help="Name of the object to create (See command 'objects' to list all objects name)") create_parser.add_argument("--in", dest="parent_infos", nargs=2, help="Specify the parent name and its uuid") create_parser.add_argument("-p", "--params", dest="params", nargs="*", help="List of Key=Value parameters", required=True) update_parser = subparsers.add_parser("update", description="Update an existing object", parents=[default_parser]) update_parser.add_argument("update", help="Name of the object to update (See command 'objects' to list all objects name)") update_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) update_parser.add_argument("-p", "--params", dest="params", nargs="*", help="List of Key=Value parameters", required=True) delete_parser = subparsers.add_parser("delete", description="Delete an existing object", parents=[default_parser]) delete_parser.add_argument("delete", help="Name of the object to update (See command 'objects' to list all objects name)") delete_parser.add_argument("-i", "--id", dest="id", help="Identifier of the object to show", required=True) assign_parser = subparsers.add_parser('assign', description="Assign a set of new objects according to their identifier", parents=[default_parser]) assign_parser.add_argument('assign', help='Name of the object to assign (See command `objects` to list all objects name)') assign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to assign', required=True) assign_parser.add_argument('--to', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) unassign_parser = subparsers.add_parser('unassign', description="Unassign a set of new objects according to their identifier", parents=[default_parser]) unassign_parser.add_argument('unassign', help='Name of the object to unassign (See command `objects` to list all objects name)') unassign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to unassign', required=True) unassign_parser.add_argument('--from', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) reassign_parser = subparsers.add_parser('reassign', description="Reassign all objects according to their identifier", parents=[default_parser]) reassign_parser.add_argument('reassign', help='Name of the object to reassign (See command `objects` to list all objects name)') reassign_parser.add_argument('--ids', dest='ids', nargs='*', help='Identifier of the object to reassign. If --ids is not specified, it will remove all assigned objects') reassign_parser.add_argument('--to', dest='parent_infos', nargs=2, help="Specify the resource name and its uuid", required=True) objects_parser = subparsers.add_parser("objects", description="Explore all objects", parents=[default_parser]) objects_parser.add_argument("-f", "--filter", dest="filter", help="Filter by name (ex: -f nsg)") objects_parser.add_argument("-p", "--parent", dest="parent", help="Filter by parent (ex -p enterprise)") objects_parser.add_argument("-c", "--child", dest="child", help="Filter by children (ex: -c domain)") args = parser.parse_args() from commands import CLICommand CLICommand.execute(args) if __name__ == "__main__": main()

true

f7201ae4d61deb09277603a9c4d12dfb5b5dd40b

1,565

py

Python

deepmath/deephol/public/proof_assistant.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

830

2016-11-07T21:46:27.000Z

2022-03-23T08:01:03.000Z

deepmath/deephol/public/proof_assistant.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

26

2016-11-07T22:06:31.000Z

2022-02-16T00:18:29.000Z

deepmath/deephol/public/proof_assistant.py

LaudateCorpus1/deepmath

b5b721f54de1d5d6a02d78f5da5995237f9995f9

[ "Apache-2.0" ]

168

2016-11-07T21:48:55.000Z

2022-03-19T02:47:14.000Z

"""A python client interface for ProofAssistantService.""" from __future__ import absolute_import from __future__ import division # Import Type Annotations from __future__ import print_function import grpc import tensorflow as tf from deepmath.proof_assistant import proof_assistant_pb2 from deepmath.proof_assistant import proof_assistant_pb2_grpc tf.flags.DEFINE_string( 'proof_assistant_server_address', 'localhost:2000', 'address (including port) of the proof assistant server') FLAGS = tf.flags.FLAGS GIGABYTE = 1024 * 1024 * 1024 GRPC_MAX_MESSAGE_LENGTH = GIGABYTE class ProofAssistant(object): """Class for intefacing a proof assistant.""" def __init__(self): self.channel = grpc.insecure_channel( FLAGS.proof_assistant_server_address, options=[('grpc.max_send_message_length', GRPC_MAX_MESSAGE_LENGTH), ('grpc.max_receive_message_length', GRPC_MAX_MESSAGE_LENGTH)]) self.stub = proof_assistant_pb2_grpc.ProofAssistantServiceStub(self.channel) def ApplyTactic(self, request: proof_assistant_pb2.ApplyTacticRequest ) -> proof_assistant_pb2.ApplyTacticResponse: return self.stub.ApplyTactic(request) def VerifyProof(self, request: proof_assistant_pb2.VerifyProofRequest ) -> proof_assistant_pb2.VerifyProofResponse: return self.stub.VerifyProof(request) def RegisterTheorem(self, request: proof_assistant_pb2.RegisterTheoremRequest ) -> proof_assistant_pb2.RegisterTheoremResponse: return self.stub.RegisterTheorem(request)

36.395349

80

0.771885

from __future__ import absolute_import from __future__ import division from __future__ import print_function import grpc import tensorflow as tf from deepmath.proof_assistant import proof_assistant_pb2 from deepmath.proof_assistant import proof_assistant_pb2_grpc tf.flags.DEFINE_string( 'proof_assistant_server_address', 'localhost:2000', 'address (including port) of the proof assistant server') FLAGS = tf.flags.FLAGS GIGABYTE = 1024 * 1024 * 1024 GRPC_MAX_MESSAGE_LENGTH = GIGABYTE class ProofAssistant(object): def __init__(self): self.channel = grpc.insecure_channel( FLAGS.proof_assistant_server_address, options=[('grpc.max_send_message_length', GRPC_MAX_MESSAGE_LENGTH), ('grpc.max_receive_message_length', GRPC_MAX_MESSAGE_LENGTH)]) self.stub = proof_assistant_pb2_grpc.ProofAssistantServiceStub(self.channel) def ApplyTactic(self, request: proof_assistant_pb2.ApplyTacticRequest ) -> proof_assistant_pb2.ApplyTacticResponse: return self.stub.ApplyTactic(request) def VerifyProof(self, request: proof_assistant_pb2.VerifyProofRequest ) -> proof_assistant_pb2.VerifyProofResponse: return self.stub.VerifyProof(request) def RegisterTheorem(self, request: proof_assistant_pb2.RegisterTheoremRequest ) -> proof_assistant_pb2.RegisterTheoremResponse: return self.stub.RegisterTheorem(request)

true

f7201bd62f21c953c3e3878bc9213acf8336f68d

1,120

py

Python

src/data_utils/image_dataset.py

lindsey98/dml_cross_entropy

4312cb295e972abda7b0e2bdadecf1965c5d7ed5

[ "BSD-3-Clause" ]

null

src/data_utils/image_dataset.py

lindsey98/dml_cross_entropy

4312cb295e972abda7b0e2bdadecf1965c5d7ed5

[ "BSD-3-Clause" ]

null

src/data_utils/image_dataset.py

lindsey98/dml_cross_entropy

4312cb295e972abda7b0e2bdadecf1965c5d7ed5

[ "BSD-3-Clause" ]

null

from PIL import Image from torch.utils.data import Dataset from src.data_utils.utils import load_data class ImageDataset(Dataset): def __init__(self, samples: list, transform, preload: bool = False, num_workers=None): self.transform = transform self.samples = samples self.targets = [label for _, label in self.samples] self.preloaded = False if preload: image_paths = [image_path for image_path, _ in self.samples] self.images = load_data(image_paths, num_workers=num_workers) self.preloaded = True print(self.__class__.__name__ + ' loaded with {} images'.format(len(self.images.keys()))) def __len__(self): return len(self.samples) def __getitem__(self, index): image_path, label = self.samples[index] if self.preloaded: image = self.images[image_path].convert('RGB') else: image = Image.open(image_path).convert('RGB') if self.transform is not None: image = self.transform(image) return image, label, index

31.111111

101

0.63125

from PIL import Image from torch.utils.data import Dataset from src.data_utils.utils import load_data class ImageDataset(Dataset): def __init__(self, samples: list, transform, preload: bool = False, num_workers=None): self.transform = transform self.samples = samples self.targets = [label for _, label in self.samples] self.preloaded = False if preload: image_paths = [image_path for image_path, _ in self.samples] self.images = load_data(image_paths, num_workers=num_workers) self.preloaded = True print(self.__class__.__name__ + ' loaded with {} images'.format(len(self.images.keys()))) def __len__(self): return len(self.samples) def __getitem__(self, index): image_path, label = self.samples[index] if self.preloaded: image = self.images[image_path].convert('RGB') else: image = Image.open(image_path).convert('RGB') if self.transform is not None: image = self.transform(image) return image, label, index

true

f7201d90ee64fded6d4df4a84c0dcb782c4684f0

20,761

py

Python

Lib/site-packages/git/objects/commit.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

null

Lib/site-packages/git/objects/commit.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

7

2020-02-12T03:06:52.000Z

2021-06-10T19:33:14.000Z

Lib/site-packages/git/objects/commit.py

nemarugommula/ecommerce

60185e79655fbaf0fcad9e877a886fe9eb3c4451

[ "bzip2-1.0.6" ]

null

# commit.py # Copyright (C) 2008, 2009 Michael Trier (mtrier@gmail.com) and contributors # # This module is part of GitPython and is released under # the BSD License: http://www.opensource.org/licenses/bsd-license.php from gitdb import IStream from git.util import ( hex_to_bin, Actor, Iterable, Stats, finalize_process ) from git.diff import Diffable from .tree import Tree from . import base from .util import ( Traversable, Serializable, parse_date, altz_to_utctz_str, parse_actor_and_date, from_timestamp, ) from git.compat import text_type from time import ( time, daylight, altzone, timezone, localtime ) import os from io import BytesIO import logging log = logging.getLogger('git.objects.commit') log.addHandler(logging.NullHandler()) __all__ = ('Commit', ) class Commit(base.Object, Iterable, Diffable, Traversable, Serializable): """Wraps a git Commit object. This class will act lazily on some of its attributes and will query the value on demand only if it involves calling the git binary.""" # ENVIRONMENT VARIABLES # read when creating new commits env_author_date = "GIT_AUTHOR_DATE" env_committer_date = "GIT_COMMITTER_DATE" # CONFIGURATION KEYS conf_encoding = 'i18n.commitencoding' # INVARIANTS default_encoding = "UTF-8" # object configuration type = "commit" __slots__ = ("tree", "author", "authored_date", "author_tz_offset", "committer", "committed_date", "committer_tz_offset", "message", "parents", "encoding", "gpgsig") _id_attribute_ = "hexsha" def __init__(self, repo, binsha, tree=None, author=None, authored_date=None, author_tz_offset=None, committer=None, committed_date=None, committer_tz_offset=None, message=None, parents=None, encoding=None, gpgsig=None): """Instantiate a new Commit. All keyword arguments taking None as default will be implicitly set on first query. :param binsha: 20 byte sha1 :param parents: tuple( Commit, ... ) is a tuple of commit ids or actual Commits :param tree: Tree Tree object :param author: Actor is the author Actor object :param authored_date: int_seconds_since_epoch is the authored DateTime - use time.gmtime() to convert it into a different format :param author_tz_offset: int_seconds_west_of_utc is the timezone that the authored_date is in :param committer: Actor is the committer string :param committed_date: int_seconds_since_epoch is the committed DateTime - use time.gmtime() to convert it into a different format :param committer_tz_offset: int_seconds_west_of_utc is the timezone that the committed_date is in :param message: string is the commit message :param encoding: string encoding of the message, defaults to UTF-8 :param parents: List or tuple of Commit objects which are our parent(s) in the commit dependency graph :return: git.Commit :note: Timezone information is in the same format and in the same sign as what time.altzone returns. The sign is inverted compared to git's UTC timezone.""" super(Commit, self).__init__(repo, binsha) if tree is not None: assert isinstance(tree, Tree), "Tree needs to be a Tree instance, was %s" % type(tree) if tree is not None: self.tree = tree if author is not None: self.author = author if authored_date is not None: self.authored_date = authored_date if author_tz_offset is not None: self.author_tz_offset = author_tz_offset if committer is not None: self.committer = committer if committed_date is not None: self.committed_date = committed_date if committer_tz_offset is not None: self.committer_tz_offset = committer_tz_offset if message is not None: self.message = message if parents is not None: self.parents = parents if encoding is not None: self.encoding = encoding if gpgsig is not None: self.gpgsig = gpgsig @classmethod def _get_intermediate_items(cls, commit): return commit.parents def _set_cache_(self, attr): if attr in Commit.__slots__: # read the data in a chunk, its faster - then provide a file wrapper _binsha, _typename, self.size, stream = self.repo.odb.stream(self.binsha) self._deserialize(BytesIO(stream.read())) else: super(Commit, self)._set_cache_(attr) # END handle attrs @property def authored_datetime(self): return from_timestamp(self.authored_date, self.author_tz_offset) @property def committed_datetime(self): return from_timestamp(self.committed_date, self.committer_tz_offset) @property def summary(self): """:return: First line of the commit message""" return self.message.split('\n', 1)[0] def count(self, paths='', **kwargs): """Count the number of commits reachable from this commit :param paths: is an optional path or a list of paths restricting the return value to commits actually containing the paths :param kwargs: Additional options to be passed to git-rev-list. They must not alter the output style of the command, or parsing will yield incorrect results :return: int defining the number of reachable commits""" # yes, it makes a difference whether empty paths are given or not in our case # as the empty paths version will ignore merge commits for some reason. if paths: return len(self.repo.git.rev_list(self.hexsha, '--', paths, **kwargs).splitlines()) return len(self.repo.git.rev_list(self.hexsha, **kwargs).splitlines()) @property def name_rev(self): """ :return: String describing the commits hex sha based on the closest Reference. Mostly useful for UI purposes""" return self.repo.git.name_rev(self) @classmethod def iter_items(cls, repo, rev, paths='', **kwargs): """Find all commits matching the given criteria. :param repo: is the Repo :param rev: revision specifier, see git-rev-parse for viable options :param paths: is an optional path or list of paths, if set only Commits that include the path or paths will be considered :param kwargs: optional keyword arguments to git rev-list where ``max_count`` is the maximum number of commits to fetch ``skip`` is the number of commits to skip ``since`` all commits since i.e. '1970-01-01' :return: iterator yielding Commit items""" if 'pretty' in kwargs: raise ValueError("--pretty cannot be used as parsing expects single sha's only") # END handle pretty # use -- in any case, to prevent possibility of ambiguous arguments # see https://github.com/gitpython-developers/GitPython/issues/264 args = ['--'] if paths: args.extend((paths, )) # END if paths proc = repo.git.rev_list(rev, args, as_process=True, **kwargs) return cls._iter_from_process_or_stream(repo, proc) def iter_parents(self, paths='', **kwargs): """Iterate _all_ parents of this commit. :param paths: Optional path or list of paths limiting the Commits to those that contain at least one of the paths :param kwargs: All arguments allowed by git-rev-list :return: Iterator yielding Commit objects which are parents of self """ # skip ourselves skip = kwargs.get("skip", 1) if skip == 0: # skip ourselves skip = 1 kwargs['skip'] = skip return self.iter_items(self.repo, self, paths, **kwargs) @property def stats(self): """Create a git stat from changes between this commit and its first parent or from all changes done if this is the very first commit. :return: git.Stats""" if not self.parents: text = self.repo.git.diff_tree(self.hexsha, '--', numstat=True, root=True) text2 = "" for line in text.splitlines()[1:]: (insertions, deletions, filename) = line.split("\t") text2 += "%s\t%s\t%s\n" % (insertions, deletions, filename) text = text2 else: text = self.repo.git.diff(self.parents[0].hexsha, self.hexsha, '--', numstat=True) return Stats._list_from_string(self.repo, text) @classmethod def _iter_from_process_or_stream(cls, repo, proc_or_stream): """Parse out commit information into a list of Commit objects We expect one-line per commit, and parse the actual commit information directly from our lighting fast object database :param proc: git-rev-list process instance - one sha per line :return: iterator returning Commit objects""" stream = proc_or_stream if not hasattr(stream, 'readline'): stream = proc_or_stream.stdout readline = stream.readline while True: line = readline() if not line: break hexsha = line.strip() if len(hexsha) > 40: # split additional information, as returned by bisect for instance hexsha, _ = line.split(None, 1) # END handle extra info assert len(hexsha) == 40, "Invalid line: %s" % hexsha yield Commit(repo, hex_to_bin(hexsha)) # END for each line in stream # TODO: Review this - it seems process handling got a bit out of control # due to many developers trying to fix the open file handles issue if hasattr(proc_or_stream, 'wait'): finalize_process(proc_or_stream) @classmethod def create_from_tree(cls, repo, tree, message, parent_commits=None, head=False, author=None, committer=None, author_date=None, commit_date=None): """Commit the given tree, creating a commit object. :param repo: Repo object the commit should be part of :param tree: Tree object or hex or bin sha the tree of the new commit :param message: Commit message. It may be an empty string if no message is provided. It will be converted to a string in any case. :param parent_commits: Optional Commit objects to use as parents for the new commit. If empty list, the commit will have no parents at all and become a root commit. If None , the current head commit will be the parent of the new commit object :param head: If True, the HEAD will be advanced to the new commit automatically. Else the HEAD will remain pointing on the previous commit. This could lead to undesired results when diffing files. :param author: The name of the author, optional. If unset, the repository configuration is used to obtain this value. :param committer: The name of the committer, optional. If unset, the repository configuration is used to obtain this value. :param author_date: The timestamp for the author field :param commit_date: The timestamp for the committer field :return: Commit object representing the new commit :note: Additional information about the committer and Author are taken from the environment or from the git configuration, see git-commit-tree for more information""" if parent_commits is None: try: parent_commits = [repo.head.commit] except ValueError: # empty repositories have no head commit parent_commits = [] # END handle parent commits else: for p in parent_commits: if not isinstance(p, cls): raise ValueError("Parent commit '%r' must be of type %s" % (p, cls)) # end check parent commit types # END if parent commits are unset # retrieve all additional information, create a commit object, and # serialize it # Generally: # * Environment variables override configuration values # * Sensible defaults are set according to the git documentation # COMMITER AND AUTHOR INFO cr = repo.config_reader() env = os.environ committer = committer or Actor.committer(cr) author = author or Actor.author(cr) # PARSE THE DATES unix_time = int(time()) is_dst = daylight and localtime().tm_isdst > 0 offset = altzone if is_dst else timezone author_date_str = env.get(cls.env_author_date, '') if author_date: author_time, author_offset = parse_date(author_date) elif author_date_str: author_time, author_offset = parse_date(author_date_str) else: author_time, author_offset = unix_time, offset # END set author time committer_date_str = env.get(cls.env_committer_date, '') if commit_date: committer_time, committer_offset = parse_date(commit_date) elif committer_date_str: committer_time, committer_offset = parse_date(committer_date_str) else: committer_time, committer_offset = unix_time, offset # END set committer time # assume utf8 encoding enc_section, enc_option = cls.conf_encoding.split('.') conf_encoding = cr.get_value(enc_section, enc_option, cls.default_encoding) # if the tree is no object, make sure we create one - otherwise # the created commit object is invalid if isinstance(tree, str): tree = repo.tree(tree) # END tree conversion # CREATE NEW COMMIT new_commit = cls(repo, cls.NULL_BIN_SHA, tree, author, author_time, author_offset, committer, committer_time, committer_offset, message, parent_commits, conf_encoding) stream = BytesIO() new_commit._serialize(stream) streamlen = stream.tell() stream.seek(0) istream = repo.odb.store(IStream(cls.type, streamlen, stream)) new_commit.binsha = istream.binsha if head: # need late import here, importing git at the very beginning throws # as well ... import git.refs try: repo.head.set_commit(new_commit, logmsg=message) except ValueError: # head is not yet set to the ref our HEAD points to # Happens on first commit master = git.refs.Head.create(repo, repo.head.ref, new_commit, logmsg="commit (initial): %s" % message) repo.head.set_reference(master, logmsg='commit: Switching to %s' % master) # END handle empty repositories # END advance head handling return new_commit #{ Serializable Implementation def _serialize(self, stream): write = stream.write write(("tree %s\n" % self.tree).encode('ascii')) for p in self.parents: write(("parent %s\n" % p).encode('ascii')) a = self.author aname = a.name c = self.committer fmt = "%s %s <%s> %s %s\n" write((fmt % ("author", aname, a.email, self.authored_date, altz_to_utctz_str(self.author_tz_offset))).encode(self.encoding)) # encode committer aname = c.name write((fmt % ("committer", aname, c.email, self.committed_date, altz_to_utctz_str(self.committer_tz_offset))).encode(self.encoding)) if self.encoding != self.default_encoding: write(("encoding %s\n" % self.encoding).encode('ascii')) try: if self.__getattribute__('gpgsig') is not None: write(b"gpgsig") for sigline in self.gpgsig.rstrip("\n").split("\n"): write((" " + sigline + "\n").encode('ascii')) except AttributeError: pass write(b"\n") # write plain bytes, be sure its encoded according to our encoding if isinstance(self.message, text_type): write(self.message.encode(self.encoding)) else: write(self.message) # END handle encoding return self def _deserialize(self, stream): """:param from_rev_list: if true, the stream format is coming from the rev-list command Otherwise it is assumed to be a plain data stream from our object""" readline = stream.readline self.tree = Tree(self.repo, hex_to_bin(readline().split()[1]), Tree.tree_id << 12, '') self.parents = [] next_line = None while True: parent_line = readline() if not parent_line.startswith(b'parent'): next_line = parent_line break # END abort reading parents self.parents.append(type(self)(self.repo, hex_to_bin(parent_line.split()[-1].decode('ascii')))) # END for each parent line self.parents = tuple(self.parents) # we don't know actual author encoding before we have parsed it, so keep the lines around author_line = next_line committer_line = readline() # we might run into one or more mergetag blocks, skip those for now next_line = readline() while next_line.startswith(b'mergetag '): next_line = readline() while next_line.startswith(b' '): next_line = readline() # end skip mergetags # now we can have the encoding line, or an empty line followed by the optional # message. self.encoding = self.default_encoding self.gpgsig = None # read headers enc = next_line buf = enc.strip() while buf: if buf[0:10] == b"encoding ": self.encoding = buf[buf.find(' ') + 1:].decode( self.encoding, 'ignore') elif buf[0:7] == b"gpgsig ": sig = buf[buf.find(b' ') + 1:] + b"\n" is_next_header = False while True: sigbuf = readline() if not sigbuf: break if sigbuf[0:1] != b" ": buf = sigbuf.strip() is_next_header = True break sig += sigbuf[1:] # end read all signature self.gpgsig = sig.rstrip(b"\n").decode(self.encoding, 'ignore') if is_next_header: continue buf = readline().strip() # decode the authors name try: self.author, self.authored_date, self.author_tz_offset = \ parse_actor_and_date(author_line.decode(self.encoding, 'replace')) except UnicodeDecodeError: log.error("Failed to decode author line '%s' using encoding %s", author_line, self.encoding, exc_info=True) try: self.committer, self.committed_date, self.committer_tz_offset = \ parse_actor_and_date(committer_line.decode(self.encoding, 'replace')) except UnicodeDecodeError: log.error("Failed to decode committer line '%s' using encoding %s", committer_line, self.encoding, exc_info=True) # END handle author's encoding # a stream from our data simply gives us the plain message # The end of our message stream is marked with a newline that we strip self.message = stream.read() try: self.message = self.message.decode(self.encoding, 'replace') except UnicodeDecodeError: log.error("Failed to decode message '%s' using encoding %s", self.message, self.encoding, exc_info=True) # END exception handling return self #} END serializable implementation

38.878277

119

0.607822

from gitdb import IStream from git.util import ( hex_to_bin, Actor, Iterable, Stats, finalize_process ) from git.diff import Diffable from .tree import Tree from . import base from .util import ( Traversable, Serializable, parse_date, altz_to_utctz_str, parse_actor_and_date, from_timestamp, ) from git.compat import text_type from time import ( time, daylight, altzone, timezone, localtime ) import os from io import BytesIO import logging log = logging.getLogger('git.objects.commit') log.addHandler(logging.NullHandler()) __all__ = ('Commit', ) class Commit(base.Object, Iterable, Diffable, Traversable, Serializable): env_author_date = "GIT_AUTHOR_DATE" env_committer_date = "GIT_COMMITTER_DATE" conf_encoding = 'i18n.commitencoding' default_encoding = "UTF-8" type = "commit" __slots__ = ("tree", "author", "authored_date", "author_tz_offset", "committer", "committed_date", "committer_tz_offset", "message", "parents", "encoding", "gpgsig") _id_attribute_ = "hexsha" def __init__(self, repo, binsha, tree=None, author=None, authored_date=None, author_tz_offset=None, committer=None, committed_date=None, committer_tz_offset=None, message=None, parents=None, encoding=None, gpgsig=None): super(Commit, self).__init__(repo, binsha) if tree is not None: assert isinstance(tree, Tree), "Tree needs to be a Tree instance, was %s" % type(tree) if tree is not None: self.tree = tree if author is not None: self.author = author if authored_date is not None: self.authored_date = authored_date if author_tz_offset is not None: self.author_tz_offset = author_tz_offset if committer is not None: self.committer = committer if committed_date is not None: self.committed_date = committed_date if committer_tz_offset is not None: self.committer_tz_offset = committer_tz_offset if message is not None: self.message = message if parents is not None: self.parents = parents if encoding is not None: self.encoding = encoding if gpgsig is not None: self.gpgsig = gpgsig @classmethod def _get_intermediate_items(cls, commit): return commit.parents def _set_cache_(self, attr): if attr in Commit.__slots__: _binsha, _typename, self.size, stream = self.repo.odb.stream(self.binsha) self._deserialize(BytesIO(stream.read())) else: super(Commit, self)._set_cache_(attr) @property def authored_datetime(self): return from_timestamp(self.authored_date, self.author_tz_offset) @property def committed_datetime(self): return from_timestamp(self.committed_date, self.committer_tz_offset) @property def summary(self): return self.message.split('\n', 1)[0] def count(self, paths='', **kwargs): if paths: return len(self.repo.git.rev_list(self.hexsha, '--', paths, **kwargs).splitlines()) return len(self.repo.git.rev_list(self.hexsha, **kwargs).splitlines()) @property def name_rev(self): return self.repo.git.name_rev(self) @classmethod def iter_items(cls, repo, rev, paths='', **kwargs): if 'pretty' in kwargs: raise ValueError("--pretty cannot be used as parsing expects single sha's only") # END handle pretty # use -- in any case, to prevent possibility of ambiguous arguments # see https://github.com/gitpython-developers/GitPython/issues/264 args = ['--'] if paths: args.extend((paths, )) # END if paths proc = repo.git.rev_list(rev, args, as_process=True, **kwargs) return cls._iter_from_process_or_stream(repo, proc) def iter_parents(self, paths='', **kwargs): # skip ourselves skip = kwargs.get("skip", 1) if skip == 0: # skip ourselves skip = 1 kwargs['skip'] = skip return self.iter_items(self.repo, self, paths, **kwargs) @property def stats(self): if not self.parents: text = self.repo.git.diff_tree(self.hexsha, '--', numstat=True, root=True) text2 = "" for line in text.splitlines()[1:]: (insertions, deletions, filename) = line.split("\t") text2 += "%s\t%s\t%s\n" % (insertions, deletions, filename) text = text2 else: text = self.repo.git.diff(self.parents[0].hexsha, self.hexsha, '--', numstat=True) return Stats._list_from_string(self.repo, text) @classmethod def _iter_from_process_or_stream(cls, repo, proc_or_stream): stream = proc_or_stream if not hasattr(stream, 'readline'): stream = proc_or_stream.stdout readline = stream.readline while True: line = readline() if not line: break hexsha = line.strip() if len(hexsha) > 40: # split additional information, as returned by bisect for instance hexsha, _ = line.split(None, 1) # END handle extra info assert len(hexsha) == 40, "Invalid line: %s" % hexsha yield Commit(repo, hex_to_bin(hexsha)) # END for each line in stream # TODO: Review this - it seems process handling got a bit out of control # due to many developers trying to fix the open file handles issue if hasattr(proc_or_stream, 'wait'): finalize_process(proc_or_stream) @classmethod def create_from_tree(cls, repo, tree, message, parent_commits=None, head=False, author=None, committer=None, author_date=None, commit_date=None): if parent_commits is None: try: parent_commits = [repo.head.commit] except ValueError: # empty repositories have no head commit parent_commits = [] # END handle parent commits else: for p in parent_commits: if not isinstance(p, cls): raise ValueError("Parent commit '%r' must be of type %s" % (p, cls)) # end check parent commit types # END if parent commits are unset # retrieve all additional information, create a commit object, and # serialize it # Generally: # * Environment variables override configuration values # * Sensible defaults are set according to the git documentation # COMMITER AND AUTHOR INFO cr = repo.config_reader() env = os.environ committer = committer or Actor.committer(cr) author = author or Actor.author(cr) # PARSE THE DATES unix_time = int(time()) is_dst = daylight and localtime().tm_isdst > 0 offset = altzone if is_dst else timezone author_date_str = env.get(cls.env_author_date, '') if author_date: author_time, author_offset = parse_date(author_date) elif author_date_str: author_time, author_offset = parse_date(author_date_str) else: author_time, author_offset = unix_time, offset # END set author time committer_date_str = env.get(cls.env_committer_date, '') if commit_date: committer_time, committer_offset = parse_date(commit_date) elif committer_date_str: committer_time, committer_offset = parse_date(committer_date_str) else: committer_time, committer_offset = unix_time, offset # END set committer time # assume utf8 encoding enc_section, enc_option = cls.conf_encoding.split('.') conf_encoding = cr.get_value(enc_section, enc_option, cls.default_encoding) # if the tree is no object, make sure we create one - otherwise # the created commit object is invalid if isinstance(tree, str): tree = repo.tree(tree) # END tree conversion # CREATE NEW COMMIT new_commit = cls(repo, cls.NULL_BIN_SHA, tree, author, author_time, author_offset, committer, committer_time, committer_offset, message, parent_commits, conf_encoding) stream = BytesIO() new_commit._serialize(stream) streamlen = stream.tell() stream.seek(0) istream = repo.odb.store(IStream(cls.type, streamlen, stream)) new_commit.binsha = istream.binsha if head: # need late import here, importing git at the very beginning throws # as well ... import git.refs try: repo.head.set_commit(new_commit, logmsg=message) except ValueError: # head is not yet set to the ref our HEAD points to # Happens on first commit master = git.refs.Head.create(repo, repo.head.ref, new_commit, logmsg="commit (initial): %s" % message) repo.head.set_reference(master, logmsg='commit: Switching to %s' % master) # END handle empty repositories # END advance head handling return new_commit #{ Serializable Implementation def _serialize(self, stream): write = stream.write write(("tree %s\n" % self.tree).encode('ascii')) for p in self.parents: write(("parent %s\n" % p).encode('ascii')) a = self.author aname = a.name c = self.committer fmt = "%s %s <%s> %s %s\n" write((fmt % ("author", aname, a.email, self.authored_date, altz_to_utctz_str(self.author_tz_offset))).encode(self.encoding)) # encode committer aname = c.name write((fmt % ("committer", aname, c.email, self.committed_date, altz_to_utctz_str(self.committer_tz_offset))).encode(self.encoding)) if self.encoding != self.default_encoding: write(("encoding %s\n" % self.encoding).encode('ascii')) try: if self.__getattribute__('gpgsig') is not None: write(b"gpgsig") for sigline in self.gpgsig.rstrip("\n").split("\n"): write((" " + sigline + "\n").encode('ascii')) except AttributeError: pass write(b"\n") # write plain bytes, be sure its encoded according to our encoding if isinstance(self.message, text_type): write(self.message.encode(self.encoding)) else: write(self.message) # END handle encoding return self def _deserialize(self, stream): readline = stream.readline self.tree = Tree(self.repo, hex_to_bin(readline().split()[1]), Tree.tree_id << 12, '') self.parents = [] next_line = None while True: parent_line = readline() if not parent_line.startswith(b'parent'): next_line = parent_line break # END abort reading parents self.parents.append(type(self)(self.repo, hex_to_bin(parent_line.split()[-1].decode('ascii')))) # END for each parent line self.parents = tuple(self.parents) # we don't know actual author encoding before we have parsed it, so keep the lines around author_line = next_line committer_line = readline() next_line = readline() while next_line.startswith(b'mergetag '): next_line = readline() while next_line.startswith(b' '): next_line = readline() self.encoding = self.default_encoding self.gpgsig = None enc = next_line buf = enc.strip() while buf: if buf[0:10] == b"encoding ": self.encoding = buf[buf.find(' ') + 1:].decode( self.encoding, 'ignore') elif buf[0:7] == b"gpgsig ": sig = buf[buf.find(b' ') + 1:] + b"\n" is_next_header = False while True: sigbuf = readline() if not sigbuf: break if sigbuf[0:1] != b" ": buf = sigbuf.strip() is_next_header = True break sig += sigbuf[1:] self.gpgsig = sig.rstrip(b"\n").decode(self.encoding, 'ignore') if is_next_header: continue buf = readline().strip() try: self.author, self.authored_date, self.author_tz_offset = \ parse_actor_and_date(author_line.decode(self.encoding, 'replace')) except UnicodeDecodeError: log.error("Failed to decode author line '%s' using encoding %s", author_line, self.encoding, exc_info=True) try: self.committer, self.committed_date, self.committer_tz_offset = \ parse_actor_and_date(committer_line.decode(self.encoding, 'replace')) except UnicodeDecodeError: log.error("Failed to decode committer line '%s' using encoding %s", committer_line, self.encoding, exc_info=True) # a stream from our data simply gives us the plain message # The end of our message stream is marked with a newline that we strip self.message = stream.read() try: self.message = self.message.decode(self.encoding, 'replace') except UnicodeDecodeError: log.error("Failed to decode message '%s' using encoding %s", self.message, self.encoding, exc_info=True) # END exception handling return self #} END serializable implementation

true

f7201e595590259f3f2e768dbecb2c84ac98021f

14,932

py

Python

run_validation.py

ASinanSaglam/atomizer_analysis

8dfc1230b2ad0c691885f8fd7119d6169cd7d1ed

[ "MIT" ]

null

run_validation.py

ASinanSaglam/atomizer_analysis

8dfc1230b2ad0c691885f8fd7119d6169cd7d1ed

[ "MIT" ]

null

run_validation.py

ASinanSaglam/atomizer_analysis

8dfc1230b2ad0c691885f8fd7119d6169cd7d1ed

[ "MIT" ]

null

# %matplotlib notebook import os, re, sys, urllib, requests, base64, IPython, io, pickle, glob sys.path.append("/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/manual") import itertools as itt import numpy as np import subprocess as sb import pandas as pd import matplotlib.pyplot as plt import matplotlib.image as mpimg import roadrunner, h5py from bs4 import BeautifulSoup as BS from IPython.display import Image, display from matplotlib import rcParams import analyzerTools as AT def run_test(analyzer, test_no, t_end=1000, atomize=False, db=None, meta=None): if(analyzer.run_single_test(test_no, t_end=100, atomize=atomize,meta=meta)): if meta: meta[test_no]["success"] = True print("run successful {}".format(test_no)) #if db is not None: # # Save results into a DataFrame # res = analyzer.all_results[test_no] # sbml, bngl, rmsd, valid_per, keys = res[0],res[1],res[2],res[3],res[4] # for key in keys: # # couldn't get the curation keys # if len(key) == 2: # skey, bkey = key # # got curation keys # elif len(key) == 3: # skey, bkey, ckey = key # else: # print("couldn't find keys") # IPython.embed() # sys.exit() # # setting up the database # db.at["{:010d}".format(test_no), "{}_sbml".format(skey)] = res[0][skey] # db.at["{:010d}".format(test_no), "{}_bngl".format(bkey)] = res[1][bkey] # analyzer.plot_results(test_no, legend=True, save_fig=True) # if(analyzer.run_old_test(test_no, t_end=100, atomize=atomize)): # print("run successful {}".format(test_no)) # analyzer.plot_old_results(test_no, legend=False, save_fig=True) else: if meta: meta[test_no]["success"] = False print("run failed {}".format(test_no)) def uniquefy_names(keys): unique_keys = [] if len(keys[0]) == 3: bkeys_d = {} skeys_d = {} ckeys_d = {} for key in keys: bkey, skey, ckey = key if bkey in bkeys_d.keys(): bkey_new = bkey + "_{}".format(bkeys_d[bkey]) bkeys_d[bkey] += 1 bkey = bkey_new else: bkeys_d[bkey] = 1 if skey in skeys_d.keys(): skey_new = skey + "_{}".format(skeys_d[skey]) skeys_d[skey] += 1 skey = skey_new else: skeys_d[skey] = 1 if ckey in ckeys_d.keys(): ckey_new = ckey + "_{}".format(ckeys_d[ckey]) ckeys_d[ckey] += 1 ckey = ckey_new else: ckeys_d[ckey] = 1 unique_keys.append( (bkey,skey,ckey) ) else: bkeys_d = {} skeys_d = {} for key in keys: bkey, skey = key if bkey in bkeys_d.keys(): bkey_new = bkey + "_{}".format(bkeys_d[bkey]) bkeys_d[bkey] += 1 bkey = bkey_new else: bkeys_d[bkey] = 1 if skey in skeys_d.keys(): skey_new = skey + "_{}".format(skeys_d[skey]) skeys_d[skey] += 1 skey = skey_new else: skeys_d[skey] = 1 unique_keys.append( (bkey,skey) ) return unique_keys def update_results(results, h5file): for key in results: if "{:010d}".format(key) in h5file: continue # create a model group res_grp = h5file.create_group("{:010d}".format(key)) # pull dataframes sres, bres, _, _, keys_used = results[key] # names if len(keys_used) == 0: continue if len(keys_used[0]) == 2: names_to_use = [keys_used[i][1] for i in range(len(keys_used))] skeyd = dict([(keys_used[i][1],keys_used[i][0]) for i in range(len(keys_used))]) bkeyd = dict([(keys_used[i][1],keys_used[i][1]) for i in range(len(keys_used))]) skn = list(map(lambda x: skeyd[x], names_to_use)) bkn = list(map(lambda x: bkeyd[x], names_to_use)) else: names_to_use = [keys_used[i][2] for i in range(len(keys_used))] skeyd = dict([(keys_used[i][2],keys_used[i][0]) for i in range(len(keys_used))]) bkeyd = dict([(keys_used[i][2],keys_used[i][1]) for i in range(len(keys_used))]) skn = list(map(lambda x: skeyd[x], names_to_use)) bkn = list(map(lambda x: bkeyd[x], names_to_use)) # make structured arrays sdtype = np.dtype({"names":names_to_use, "formats": ["<f8" for i in range(len(names_to_use))]}) bdtype = np.dtype({"names":names_to_use, "formats": ["<f8" for i in range(len(names_to_use))]}) # if len(names_to_use) != sres[skn].shape[1]: # # we have multiple datasets per name, drop one # for iname,name in enumerate(names_to_use): # if len(sres[name].shape) > 1: # # stupl = list(map(tuple, sres[skn].values)) btupl = list(map(tuple, bres[bkn].values)) sarr = np.array(stupl, dtype=sdtype) barr = np.array(btupl, dtype=bdtype) # add the data in, if it exists if sarr.shape[0] != 0: sg = res_grp.create_dataset("sbml_data", data=sarr) if barr.shape[0] != 0: bg = res_grp.create_dataset("bngl_data", data=barr) print("updated results") return True def save_meta(meta, fname="meta_data.pickle"): if os.path.isfile(fname): with open(fname, "rb") as f: m = pickle.load(f) for key in meta: m[key] = meta[key] with open(fname, "wb") as f: pickle.dump(m, f) else: with open(fname, "wb") as f: pickle.dump(meta, f) # All the paths we need # The BNG2.pl file for bionetgen runs bng_path = "/home/monoid/apps/BioNetGen-2.5.0/BNG2.pl" # This is the python file that can be called from the command line sbml_translator_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/sbmlTranslator.py" # if you give this the ATOMIZER ANALYZER 5000 will import atomizer and run internally # translator_package_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser" translator_package_path = None # This is neccesary for atomizer, has default naming conventions and a lot more # this path will be sym linked to everywhere you want to run translator under config_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/config" # the path to the folder that contains 5 zero padded folders for each test tests_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/curated" # Now we also add COPASI PATH!!_!_ copasi_path = "/home/monoid/apps/copasi/4.27/bin/CopasiSE" # change directory to where we want to run the tests os.chdir("/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/analyzerTools") # The analyzer setup ba = AT.BiomodelAnalyzer(bng_path, sbml_translator_path, config_path, tests_path, translator_import=translator_package_path, copasi_path=copasi_path) # Let's re-run everything tests = list(range(908,915)) known_issues = set([24,25,34,154,155,196,201,589,613,668,669,696,468, # Not implemented 643,644,645, # Complex "i" is used in function/parameter 63,245,248,305,556,575,578,542, # rule named used as parameter 342,429,457,547,570,627,637,638, # compartment used as parameter 527,562,592,593,596,723,250, # Actually broken, even in Copasi 304,324,330,331,341,343,345,349,367,371,374,377,381,533,548, 549,551,618,642,670,671,680,682,684,118,252,673,531,532,555, 561, # no reactions 306,307,308,309,310,311,388,390,391,393,409, 428,505,512,528,557,566,567,719,641,71,90,173, 253, # assignment rules used in reactions 610, # function defs for v16/v17 558,568,674,722,412,445,302,208,268,51,55,162,180,179,579, 691,465,466,238,312,538,603,604,605,215, # Uses time 635,636, # Uses not only time but also encoded strings for parameters 119, # single reaction, not really suitable for translation 47,483,484,486,487, # initial states should result in no reactions, 164,165,167,326,375,400,554,577,664,672,693,698, 234,237,286,450, # Uses piecewise definitions 396,398,507,522,705, 499,474, # SBML modeller is careless and uses species that should be params 607, # Function not defined properly/links to another function 319,206,39,145,353,385,392,463,608,470,472, # non-integer stoichiometry 161,182,239, # true multi-compartment model 271 # multi-compartment and the modeller has issues ]) # Need to figure out, mostly CVODE list_of_fails = set([246,336,378,383,384,387,438,9,107,123,183,192,269, 279,292,328,617,678,606, # new ones 616, # Legitimate bug, if species name is very simple AND rate constant # only depenent on the species concentration AND we end up generating # an observable with the same name as species name, then BNGL thinkg # we are giving obs name as the rate constant, leading to a bug 255, # Circular dependency in funcs? 401,402,403, # if func messes with func ordering 559, # can't load copasi result 64, # Due to website addition? also in too long set 232, # BNG takes too long? 172,176,177 # doesn't end up translating, takes a long time? ]) #too_long = set([64,574,426,70,217,247,503,469,471,473,506,451,595, # WAAAY TOO LONG - debug # 332,334, # ATOMIZER BREAKS THESE # 217,247,293,426,469 # too long when atomized # ]) too_long = set([64 ,172,176,177,212,217,235,247,293,385, 426,451,457,463,469,470,471,472,473,474, 496,497,503,505,506,574,595,835, 863, # transl too long 232,608, # BNG takes too long 63,70, # long but completes? 269 # due to long CVODE error ]) ################# NEW CHECKS ############## # A complete new set of checks to see the latest state of the tool as we are # writing the manuscript. new_checks = set([64,217,235,496, # too long 497,498, # skey ratio index out of range? 63, # fairly long but does complete 119,465,468, # no data? 247,269,469,470,471,472,473,474, 503,505,506,595,606,608,835,863 # long, didn't check if completes ]) ################# RUN FAILS ############### run_fails = set([9,24,25,34,51,55,107, 123,154,155,162,164,165,167,172,176,177,179,180,183,192, 201,208,215,232,234,237,238,245,246,248,250,255,268,279,286,292, 302,305,312,326,328,332,334,336,353,375,383,384,385,387,396,398, 400,401,402,403,412,426,429,438,445,450,451,457,463,466,483,484, 486,487,499,507,522,527,531,532,538,542,547,554,555,556,558,559, 561,562,574,575,577,578,579,589,592,593,599,600,602,607,610,617, 627,635,636,637,638,643,644,645,664,668,669,672,673,674,675,678, 687,688,692,693,696,698,705,722,723,730,731,748,749,757,759,760, 763,764,766,775,801,802,808,815,824,826,833,837,840,841,849,851, 858,859,876,879,880 # run_failed ]) ################# EVENTS ################# w_event = set([1,7,56,77,81,87,88,95,96,97,101,104,109, # models with events 111,117,120,121,122,124,125,126,127,128,129,130,131, # models with events 132,133,134,135,136,137,139,140,141,142,144,148,149, # models with events 152,153,158,186,187,188,189,193,194,195,196,227,235, # models with events 241,244,256,265,281,285,287,297,301,316,317,318,327, # models with events 337,338,339,340,342,344,404,408,422,436,437,439,479, # models with events 480,488,493,494,496,497,534,535,536,537,540,541,563, # models with events 570,571,597,598,601,612,613,620,621,628,632,634,650, # models with events 659,681,695,699,702,706,711,718,727,734,735,736,786, # models with events 789,791,794,806,814,816,817,818,820,822,825,829,834, # models with events 856,860,862,864,901]) # models with events ################# END CHECKS ############## all_issues = known_issues.union(w_event) all_issues = all_issues.union(list_of_fails) # Load in database # dbname = "validation.h5" # if os.path.isfile(dbname): # db = pd.read_hdf(dbname,key="validation") # else: # db = pd.DataFrame() # run tests # try: if os.path.isfile("results.h5"): os.remove("results.h5") # results_file = h5py.File("results.h5","a") results_file = h5py.File("results.h5","w") else: results_file = h5py.File("results.h5","w") meta_data = {} for test_no in tests: #if test_no in all_issues: # continue # if test_no in w_event or test_no in new_checks or test_no in run_fails: # if test_no in new_checks or test_no in run_fails: # continue if test_no in too_long: meta_data[test_no] = {"too_long":True} continue if (os.path.isfile("/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/curated/BIOMD{0:010d}.xml".format(test_no))): #run_test(ba, test_no, t_end=100, atomize=False, db=db) meta_data[test_no] = {"file":True, "too_long":False} run_test(ba, test_no, t_end=100, atomize=True, meta=meta_data) update_results(ba.all_results,results_file) else: meta_data[test_no] = {"file":False} print("number {} doesn't exist".format(test_no)) save_meta(meta_data) # with open("validation.pickle", 'wb') as f: # pickle.dump(ba.all_results, f) #except: # with open("validation.pickle", 'wb') as f: # pickle.dump(ba.all_results, f) # db.to_hdf(dbname,"validation")

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import os, re, sys, urllib, requests, base64, IPython, io, pickle, glob sys.path.append("/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/manual") import itertools as itt import numpy as np import subprocess as sb import pandas as pd import matplotlib.pyplot as plt import matplotlib.image as mpimg import roadrunner, h5py from bs4 import BeautifulSoup as BS from IPython.display import Image, display from matplotlib import rcParams import analyzerTools as AT def run_test(analyzer, test_no, t_end=1000, atomize=False, db=None, meta=None): if(analyzer.run_single_test(test_no, t_end=100, atomize=atomize,meta=meta)): if meta: meta[test_no]["success"] = True print("run successful {}".format(test_no)) 2: # skey, bkey = key # # got curation keys # elif len(key) == 3: # skey, bkey, ckey = key # else: # print("couldn't find keys") else: if meta: meta[test_no]["success"] = False print("run failed {}".format(test_no)) def uniquefy_names(keys): unique_keys = [] if len(keys[0]) == 3: bkeys_d = {} skeys_d = {} ckeys_d = {} for key in keys: bkey, skey, ckey = key if bkey in bkeys_d.keys(): bkey_new = bkey + "_{}".format(bkeys_d[bkey]) bkeys_d[bkey] += 1 bkey = bkey_new else: bkeys_d[bkey] = 1 if skey in skeys_d.keys(): skey_new = skey + "_{}".format(skeys_d[skey]) skeys_d[skey] += 1 skey = skey_new else: skeys_d[skey] = 1 if ckey in ckeys_d.keys(): ckey_new = ckey + "_{}".format(ckeys_d[ckey]) ckeys_d[ckey] += 1 ckey = ckey_new else: ckeys_d[ckey] = 1 unique_keys.append( (bkey,skey,ckey) ) else: bkeys_d = {} skeys_d = {} for key in keys: bkey, skey = key if bkey in bkeys_d.keys(): bkey_new = bkey + "_{}".format(bkeys_d[bkey]) bkeys_d[bkey] += 1 bkey = bkey_new else: bkeys_d[bkey] = 1 if skey in skeys_d.keys(): skey_new = skey + "_{}".format(skeys_d[skey]) skeys_d[skey] += 1 skey = skey_new else: skeys_d[skey] = 1 unique_keys.append( (bkey,skey) ) return unique_keys def update_results(results, h5file): for key in results: if "{:010d}".format(key) in h5file: continue res_grp = h5file.create_group("{:010d}".format(key)) sres, bres, _, _, keys_used = results[key] if len(keys_used) == 0: continue if len(keys_used[0]) == 2: names_to_use = [keys_used[i][1] for i in range(len(keys_used))] skeyd = dict([(keys_used[i][1],keys_used[i][0]) for i in range(len(keys_used))]) bkeyd = dict([(keys_used[i][1],keys_used[i][1]) for i in range(len(keys_used))]) skn = list(map(lambda x: skeyd[x], names_to_use)) bkn = list(map(lambda x: bkeyd[x], names_to_use)) else: names_to_use = [keys_used[i][2] for i in range(len(keys_used))] skeyd = dict([(keys_used[i][2],keys_used[i][0]) for i in range(len(keys_used))]) bkeyd = dict([(keys_used[i][2],keys_used[i][1]) for i in range(len(keys_used))]) skn = list(map(lambda x: skeyd[x], names_to_use)) bkn = list(map(lambda x: bkeyd[x], names_to_use)) sdtype = np.dtype({"names":names_to_use, "formats": ["<f8" for i in range(len(names_to_use))]}) bdtype = np.dtype({"names":names_to_use, "formats": ["<f8" for i in range(len(names_to_use))]}) (map(tuple, sres[skn].values)) btupl = list(map(tuple, bres[bkn].values)) sarr = np.array(stupl, dtype=sdtype) barr = np.array(btupl, dtype=bdtype) if sarr.shape[0] != 0: sg = res_grp.create_dataset("sbml_data", data=sarr) if barr.shape[0] != 0: bg = res_grp.create_dataset("bngl_data", data=barr) print("updated results") return True def save_meta(meta, fname="meta_data.pickle"): if os.path.isfile(fname): with open(fname, "rb") as f: m = pickle.load(f) for key in meta: m[key] = meta[key] with open(fname, "wb") as f: pickle.dump(m, f) else: with open(fname, "wb") as f: pickle.dump(meta, f) bng_path = "/home/monoid/apps/BioNetGen-2.5.0/BNG2.pl" sbml_translator_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/sbmlTranslator.py" translator_package_path = None config_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/config" tests_path = "/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/curated" copasi_path = "/home/monoid/apps/copasi/4.27/bin/CopasiSE" os.chdir("/home/monoid/Development/fresh_atomizer_checks/atomizer/SBMLparser/test/analyzerTools") ba = AT.BiomodelAnalyzer(bng_path, sbml_translator_path, config_path, tests_path, translator_import=translator_package_path, copasi_path=copasi_path) tests = list(range(908,915)) known_issues = set([24,25,34,154,155,196,201,589,613,668,669,696,468, # Not implemented 643,644,645, # Complex "i" is used in function/parameter 63,245,248,305,556,575,578,542, # rule named used as parameter 342,429,457,547,570,627,637,638, # compartment used as parameter 527,562,592,593,596,723,250, # Actually broken, even in Copasi 304,324,330,331,341,343,345,349,367,371,374,377,381,533,548, 549,551,618,642,670,671,680,682,684,118,252,673,531,532,555, 561, # no reactions 306,307,308,309,310,311,388,390,391,393,409, 428,505,512,528,557,566,567,719,641,71,90,173, 253, # assignment rules used in reactions 610, # function defs for v16/v17 558,568,674,722,412,445,302,208,268,51,55,162,180,179,579, 691,465,466,238,312,538,603,604,605,215, # Uses time 635,636, # Uses not only time but also encoded strings for parameters 119, # single reaction, not really suitable for translation 47,483,484,486,487, # initial states should result in no reactions, 164,165,167,326,375,400,554,577,664,672,693,698, 234,237,286,450, # Uses piecewise definitions 396,398,507,522,705, 499,474, # SBML modeller is careless and uses species that should be params 607, # Function not defined properly/links to another function 319,206,39,145,353,385,392,463,608,470,472, # non-integer stoichiometry 161,182,239, # true multi-compartment model 271 # multi-compartment and the modeller has issues ]) # Need to figure out, mostly CVODE list_of_fails = set([246,336,378,383,384,387,438,9,107,123,183,192,269, 279,292,328,617,678,606, # new ones 616, # Legitimate bug, if species name is very simple AND rate constant # only depenent on the species concentration AND we end up generating # an observable with the same name as species name, then BNGL thinkg # we are giving obs name as the rate constant, leading to a bug 255, # Circular dependency in funcs? 401,402,403, # if func messes with func ordering 559, # can't load copasi result 64, 232, 172,176,177 ]) #too_long = set([64,574,426,70,217,247,503,469,471,473,506,451,595, # WAAAY TOO LONG - debug # 332,334, # ATOMIZER BREAKS THESE # 217,247,293,426,469 # too long when atomized # ]) too_long = set([64 ,172,176,177,212,217,235,247,293,385, 426,451,457,463,469,470,471,472,473,474, 496,497,503,505,506,574,595,835, 863, # transl too long 232,608, # BNG takes too long 63,70, # long but completes? 269 # due to long CVODE error ]) ################# NEW CHECKS ############## # A complete new set of checks to see the latest state of the tool as we are # writing the manuscript. new_checks = set([64,217,235,496, # too long 497,498, # skey ratio index out of range? 63, # fairly long but does complete 119,465,468, # no data? 247,269,469,470,471,472,473,474, 503,505,506,595,606,608,835,863 # long, didn't check if completes ]) 731,748,749,757,759,760, 763,764,766,775,801,802,808,815,824,826,833,837,840,841,849,851, 858,859,876,879,880 ]) 856,860,862,864,901]) no, t_end=100, atomize=True, meta=meta_data) update_results(ba.all_results,results_file) else: meta_data[test_no] = {"file":False} print("number {} doesn't exist".format(test_no)) save_meta(meta_data) # with open("validation.pickle", 'wb') as f: # pickle.dump(ba.all_results, f) #except: # with open("validation.pickle", 'wb') as f: # pickle.dump(ba.all_results, f) # db.to_hdf(dbname,"validation")

true

f7201f03fb11fc26e5295dea810629ac3fa330da

5,030

py

Python

cedar/forms.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

null

cedar/forms.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

11

2020-03-24T15:29:46.000Z

2022-03-11T23:14:48.000Z

cedar/forms.py

stewardshiptools/stewardshiptools

ee5d27e7b0d5d4947f34ad02bdf63a06ad0a5c3e

[ "MIT" ]

null

from os.path import join from email.mime.image import MIMEImage from django.conf import settings from django.forms import ModelForm, ValidationError, ChoiceField from django.forms.models import BaseInlineFormSet from django.forms.models import inlineformset_factory from django.contrib.auth.forms import ReadOnlyPasswordHashWidget, ReadOnlyPasswordHashField, PasswordResetForm from django.contrib.auth.tokens import default_token_generator from django.core.mail import EmailMultiAlternatives from django.template import loader from crm.models import Person from crm.forms import PersonSettingsForm from django.contrib.auth.models import User from security.forms import SecurityLevelModelFormMixin from security.models import SecurityLevel class UserAdminForm(SecurityLevelModelFormMixin, ModelForm): """ Override the user admin form so that we can force firstname, lastname to be required --- needed for pushing changes over to crm.Person. """ password = ReadOnlyPasswordHashField(label=("Password"), help_text=("Raw passwords are not stored, so there is no way to see " "this user's password, but you can change the password " "using <a href=\"password/\">this form</a>.")) # Need to minimally declare security_level here so that the user admin can see it. # The mixin will take care of details. security_level = ChoiceField() def __init__(self, *args, **kwargs): super(UserAdminForm, self).__init__(*args, **kwargs) self.fields['first_name'].required = True self.fields['last_name'].required = True self.fields['email'].required = True # self.fields['password'].widget = ReadOnlyPasswordHashWidget() def get_security_level_default(self): level_range = [x[0] for x in SecurityLevel.level_choices] return max(level_range) # Default users to the lowest security level. class Meta: model = User fields = '__all__' class UserSettingsForm(ModelForm): """ This is the form used by the user menu to update user profile settings. hides user password (for now). """ def __init__(self, *args, **kwargs): super(UserSettingsForm, self).__init__(*args, **kwargs) self.fields['first_name'].required = True self.fields['last_name'].required = True self.fields['email'].required = True class Meta: model = User fields = ('first_name', 'last_name', 'email',) class CedarPasswordResetForm(PasswordResetForm): def send_mail(self, subject_template_name, email_template_name, context, from_email, to_email, html_email_template_name=None): """ Sends a django.core.mail.EmailMultiAlternatives to `to_email`. """ subject = loader.render_to_string(subject_template_name, context) # Email subject *must not* contain newlines subject = ''.join(subject.splitlines()) body = loader.render_to_string(email_template_name, context) email_message = EmailMultiAlternatives(subject, body, from_email, [to_email]) if html_email_template_name is not None: html_email = loader.render_to_string(html_email_template_name, context) email_message.attach_alternative(html_email, 'text/html') email_message.mixed_subtype = 'related' with open(join(settings.STATIC_ROOT, 'css/cedarbox_icon_gry.png'), 'rb') as fp: logo_img = MIMEImage(fp.read()) logo_img.add_header('Content-ID', '<{}>'.format('cedarbox_icon_gry.png')) email_message.attach(logo_img) email_message.send() def save(self, domain_override=None, subject_template_name='registration/password_reset_subject.txt', email_template_name='registration/password_reset_email.html', use_https=False, token_generator=default_token_generator, from_email=None, request=None, html_email_template_name=None): # If domain_override hasn't been provided. Let's override it ourself using the request. if domain_override is None and request is not None: domain_override = request.META['HTTP_HOST'] return super(CedarPasswordResetForm, self).save( domain_override=domain_override, subject_template_name=subject_template_name, email_template_name=email_template_name, use_https=use_https, token_generator=token_generator, from_email=from_email, request=request, html_email_template_name=html_email_template_name ) UserSettingsFormset = inlineformset_factory(User, Person, form=PersonSettingsForm, extra=1 )

43.362069

110

0.663022

from os.path import join from email.mime.image import MIMEImage from django.conf import settings from django.forms import ModelForm, ValidationError, ChoiceField from django.forms.models import BaseInlineFormSet from django.forms.models import inlineformset_factory from django.contrib.auth.forms import ReadOnlyPasswordHashWidget, ReadOnlyPasswordHashField, PasswordResetForm from django.contrib.auth.tokens import default_token_generator from django.core.mail import EmailMultiAlternatives from django.template import loader from crm.models import Person from crm.forms import PersonSettingsForm from django.contrib.auth.models import User from security.forms import SecurityLevelModelFormMixin from security.models import SecurityLevel class UserAdminForm(SecurityLevelModelFormMixin, ModelForm): password = ReadOnlyPasswordHashField(label=("Password"), help_text=("Raw passwords are not stored, so there is no way to see " "this user's password, but you can change the password " "using <a href=\"password/\">this form</a>.")) # Need to minimally declare security_level here so that the user admin can see it. # The mixin will take care of details. security_level = ChoiceField() def __init__(self, *args, **kwargs): super(UserAdminForm, self).__init__(*args, **kwargs) self.fields['first_name'].required = True self.fields['last_name'].required = True self.fields['email'].required = True # self.fields['password'].widget = ReadOnlyPasswordHashWidget() def get_security_level_default(self): level_range = [x[0] for x in SecurityLevel.level_choices] return max(level_range) # Default users to the lowest security level. class Meta: model = User fields = '__all__' class UserSettingsForm(ModelForm): def __init__(self, *args, **kwargs): super(UserSettingsForm, self).__init__(*args, **kwargs) self.fields['first_name'].required = True self.fields['last_name'].required = True self.fields['email'].required = True class Meta: model = User fields = ('first_name', 'last_name', 'email',) class CedarPasswordResetForm(PasswordResetForm): def send_mail(self, subject_template_name, email_template_name, context, from_email, to_email, html_email_template_name=None): subject = loader.render_to_string(subject_template_name, context) # Email subject *must not* contain newlines subject = ''.join(subject.splitlines()) body = loader.render_to_string(email_template_name, context) email_message = EmailMultiAlternatives(subject, body, from_email, [to_email]) if html_email_template_name is not None: html_email = loader.render_to_string(html_email_template_name, context) email_message.attach_alternative(html_email, 'text/html') email_message.mixed_subtype = 'related' with open(join(settings.STATIC_ROOT, 'css/cedarbox_icon_gry.png'), 'rb') as fp: logo_img = MIMEImage(fp.read()) logo_img.add_header('Content-ID', '<{}>'.format('cedarbox_icon_gry.png')) email_message.attach(logo_img) email_message.send() def save(self, domain_override=None, subject_template_name='registration/password_reset_subject.txt', email_template_name='registration/password_reset_email.html', use_https=False, token_generator=default_token_generator, from_email=None, request=None, html_email_template_name=None): # If domain_override hasn't been provided. Let's override it ourself using the request. if domain_override is None and request is not None: domain_override = request.META['HTTP_HOST'] return super(CedarPasswordResetForm, self).save( domain_override=domain_override, subject_template_name=subject_template_name, email_template_name=email_template_name, use_https=use_https, token_generator=token_generator, from_email=from_email, request=request, html_email_template_name=html_email_template_name ) UserSettingsFormset = inlineformset_factory(User, Person, form=PersonSettingsForm, extra=1 )

true