0:\n random.shuffle(indexed_pairs)\n for seq_num in range(args.random):\n seq = indexed_pairs[seq_num]\n print('-'*80)\n input_str = ' '.join(\n ''.join([source_vocab.idx2char[idx] for idx in seq[0] if idx\n not in [const.SOS_IDX, const.EOS_IDX]])\n .split(const.SEP))\n gold_str = ' '.join(\n ''.join([target_vocab.idx2char[idx] for idx in seq[1] if idx\n not in [const.SOS_IDX, const.EOS_IDX]])\n .split(const.SEP))\n predicted_idxx = decode(seq[0], args.itemize, encoder, decoder,\n dataset_params['max_seq_len'])\n pred_str = ' '.join(\n ''.join([target_vocab.idx2char[idx] for idx in predicted_idxx\n if idx not in [const.SOS_IDX, const.EOS_IDX]])\n .split(const.SEP))\n print('>', input_str)\n print('=', gold_str)\n print('<', pred_str)\n else:\n _evaluate(indexed_pairs, args.itemize, encoder, decoder,\n target_vocab.idx2char, dataset_params['max_seq_len'])","def init_model(model):\n model(tf.random.uniform((1, 512, 512, 3)))","def __init__(self, name): #, keep_axis=1):\n\n #:param int keep_axis: The axis which will be exempt from flattening (can only be one at most). If keep_axis is negative, we will count from the end.\n #self.keep_axis = keep_axis\n\n # generate the model in tensorflow\n self.name = name\n super().__init__(name=name)","def __init__(self, inputs, outputs,\n session=tf.get_default_session, name='function'):\n self.session, self.name = session, name\n self.inputs, self.outputs = inputs, outputs","def main():\n flags = PARSER.parse_args()\n\n if flags.to == 'savedmodel':\n to_savedmodel(input_shape=flags.input_shape,\n model_fn=unet_fn,\n src_dir=flags.checkpoint_dir,\n dst_dir='./saved_model',\n input_names=['IteratorGetNext'],\n output_names=['total_loss_ref'],\n use_amp=flags.use_amp,\n use_xla=flags.use_xla,\n compress=flags.compress)\n if flags.to == 'tensorrt':\n ds = Dataset(data_dir=flags.data_dir,\n batch_size=1,\n augment=False,\n gpu_id=0,\n num_gpus=1,\n seed=42)\n iterator = ds.test_fn(count=1).make_one_shot_iterator()\n features = iterator.get_next()\n\n sess = tf.Session()\n\n def input_data():\n return {'input_tensor:0': sess.run(features)}\n\n to_tensorrt(src_dir=flags.savedmodel_dir,\n dst_dir='./tf_trt_model',\n precision=flags.precision,\n feed_dict_fn=input_data,\n num_runs=1,\n output_tensor_names=['Softmax:0'],\n compress=flags.compress)\n if flags.to == 'onnx':\n to_onnx(src_dir=flags.savedmodel_dir,\n dst_dir='./onnx_model',\n compress=flags.compress)","def build_model(tparams, options):\n opt_ret = dict()\n use_noise = theano.shared(numpy.asarray(1., dtype=theano.config.floatX))\n try:\n trng = RandomStreams(1234, use_cuda=True)\n except:\n print \"Could not apply use_cuda==True in RandonStreams ...\"\n trng = RandomStreams(1234)\n\n xs = []\n xmasks = []\n\n langs = options['langs']\n for lang in langs:\n # description string: #words x #samples\n x_lang = tensor.matrix('x_%s'%lang, dtype='int64')\n mask_lang = tensor.matrix('mask_%s'%lang, dtype='float32')\n xs.append(x_lang)\n xmasks.append(mask_lang)\n\n xs_r = []\n xmasks_r = []\n if options['bidirectional_enc']:\n for i,lang in enumerate(langs):\n x_lang = xs[i]\n mask_lang = xmasks[i]\n # reverse\n x_lang_r = x_lang[::-1]\n mask_lang_r = mask_lang[::-1]\n\n xs_r.append(x_lang_r)\n xmasks_r.append(mask_lang_r)\n\n sents_all = []\n im = tensor.matrix('im', dtype='float32')\n n_samples = im.shape[0]\n\n for i,lang in enumerate(langs):\n x_lang = xs[i]\n mask_lang = xmasks[i]\n\n n_timesteps_lang = x_lang.shape[0]\n n_samples_lang = x_lang.shape[1]\n\n if options['use_dropout']:\n # dropout probs for the word embeddings\n retain_probability_emb = 1-options['dropout_embedding']\n # dropout probs for the RNN hidden states\n retain_probability_hidden = 1-options['dropout_hidden']\n # dropout probs for the source words\n retain_probability_source = 1-options['dropout_source']\n # hidden states\n rec_dropout = shared_dropout_layer((2, n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\n rec_dropout_r = shared_dropout_layer((2, n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\n # word embeddings\n emb_dropout = shared_dropout_layer((2, n_samples_lang, options['dim_word']), use_noise, trng, retain_probability_emb)\n emb_dropout_r = shared_dropout_layer((2, n_samples_lang, options['dim_word']), use_noise, trng, retain_probability_emb)\n # source words\n source_dropout = shared_dropout_layer((n_timesteps_lang, n_samples_lang, 1), use_noise, trng, retain_probability_source)\n source_dropout = tensor.tile(source_dropout, (1,1,options['dim_word']))\n else:\n # hidden states\n rec_dropout = theano.shared(numpy.array([1.]*2, dtype='float32'))\n rec_dropout_r = theano.shared(numpy.array([1.]*2, dtype='float32'))\n # word embeddings\n emb_dropout = theano.shared(numpy.array([1.]*2, dtype='float32'))\n emb_dropout_r = theano.shared(numpy.array([1.]*2, dtype='float32'))\n\n # Word embedding (for a particular language `lang`)\n # forward\n emb_lang = tparams['Wemb_%s'%lang][x_lang.flatten()]\n emb_lang = emb_lang.reshape([n_timesteps_lang, n_samples_lang, options['dim_word']])\n\n if options['use_dropout']:\n emb_lang *= source_dropout\n\n if options['bidirectional_enc']:\n x_lang_r = xs_r[i]\n mask_lang_r = xmasks_r[i]\n\n # backward lang encoder\n emb_lang_r = tparams['Wemb_%s'%lang][x_lang_r.flatten()]\n emb_lang_r = emb_lang_r.reshape([n_timesteps_lang, n_samples_lang, options['dim_word']])\n\n if options['use_dropout']:\n emb_lang_r *= source_dropout[::-1]\n\n # Encode sentence in language `lang`\n if options['encoder_%s'%lang] == 'bow':\n sents_lang = (emb_lang * mask_lang[:,:,None]).sum(0)\n else:\n # iteratively push input from first hidden layer until the last\n for i in range(int(options['n_enc_hidden_layers'])):\n layer_name_prefix='encoder_%s_%i'%(lang,i)\n # if first hidden layer use wembs, otherwise output of previous hidden layer\n layer_below=emb_lang if i==0 else layer_below[0]\n\n # do not apply dropout on word embeddings layer\n #if options['use_dropout'] and i>0:\n # layer_below = dropout_layer(layer_below, use_noise, trng, prob=options['dropout_prob'])\n\n layer_below=get_layer(options['encoder_%s'%lang])[1](tparams,\n layer_below, options, None, prefix=layer_name_prefix, mask=mask_lang,\n emb_dropout=emb_dropout, \n rec_dropout=rec_dropout)\n\n if i==int(options['n_enc_hidden_layers'])-1:\n # sentence embeddings (projections) are the output of the last hidden layer\n proj_lang = layer_below\n\n # apply forward and backward steps and concatenate both\n if options['bidirectional_enc']:\n # concatenate forward and backward pass RNNs\n # iteratively push input from first hidden layer until the last\n for i in range(int(options['n_enc_hidden_layers'])):\n layer_name_prefix='encoder_%s_r_%i'%(lang,i)\n # if first hidden layer use wembs, else output of prev hidden layer\n layer_below=emb_lang_r if i==0 else layer_below[0]\n\n # do not apply dropout on word embeddings layer\n #if options['use_dropout'] and i>0:\n # layer_below = dropout_layer(layer_below, use_noise, trng, prob=options['dropout_prob'])\n\n layer_below=get_layer(options['encoder_%s'%lang])[1](tparams,\n layer_below, options, None,\n prefix=layer_name_prefix,\n mask=mask_lang_r,\n emb_dropout=emb_dropout_r,\n rec_dropout=rec_dropout_r)\n\n if i==int(options['n_enc_hidden_layers'])-1:\n # sentence embeddings (projections) are the output of the last hidden layer\n proj_lang_r = layer_below\n\n # use the last state of forward + backward encoder rnns\n sents_lang = concatenate([proj_lang[0][-1], proj_lang_r[0][-1]], axis=proj_lang[0].ndim-2)\n else:\n sents_lang = proj_lang[0][-1]\n\n if options['use_dropout']:\n sents_lang *= shared_dropout_layer((n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\n\n # project sentences into multimodal space\n sents_mm = get_layer('ff')[1](tparams, sents_lang, options, prefix='ff_sentence_mm', activ='linear')\n\n if options['attention_type'] == 'dot':\n sents_mm = l2norm(sents_mm)\n\n if options['use_dropout']:\n sents_mm *= shared_dropout_layer((n_samples_lang, options['dim_multimodal']), use_noise, trng, retain_probability_hidden)\n\n sents_all.append(sents_mm)\n\n # Encode images\n images = get_layer('ff')[1](tparams, im, options, prefix='ff_image_mm', activ='linear')\n\n if options['attention_type'] == 'dot':\n images = l2norm(images)\n\n if options['use_dropout']:\n images *= shared_dropout_layer((n_samples, options['dim_multimodal']), use_noise, trng, retain_probability_hidden)\n\n # Compute loss\n lambda_img_sent = options['lambda_img_sent']\n lambda_sent_sent = options['lambda_sent_sent']\n if options['use_all_costs']:\n cost = contrastive_loss_all(tparams, options,\n images, sents_all, lambda_img_sent, lambda_sent_sent)\n else:\n cost = contrastive_loss(tparams, options,\n images, sents_all)\n\n # return flattened inputs\n inps = []\n inps.extend(xs)\n inps.extend(xmasks)\n inps.append(im)\n\n return trng, inps, cost","def create_model(self):\n\t\twith tf.name_scope(\"input\"):\n\t\t\tself.user_embedding = tf.get_variable(\"user_embed\", \n\t\t\t\t[self.user_size, self.embed_size], dtype=tf.float32)\n\t\t\tself.item_embedding = tf.get_variable(\"item_embed\", \n\t\t\t\t[self.item_size, self.embed_size], dtype=tf.float32)\n\t\t\tself.user_embed = tf.nn.embedding_lookup(\n\t\t\t\t\t\t\tself.user_embedding, self.user)\n\t\t\tself.item_embed = tf.nn.embedding_lookup(\n\t\t\t\t\t\t\tself.user_embedding, self.item)\n\t\twith tf.name_scope(\"fusion\"):\n\t\t\tself.user_fusion_add = self.user_embed + self.user_feature\n\t\t\tself.item_fusion_add = self.item_embed + self.item_feature\n\n\t\t\tself.user_fusion = tf.layers.dense(inputs=self.user_fusion_add,\n\t\t\t\t\t\t\t\tunits=self.embed_size,\n\t\t\t\t\t\t\t\tactivation=self.activation_func,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='user_fusion')\n\t\t\tself.item_fusion = tf.layers.dense(inputs=self.item_fusion_add,\n\t\t\t\t\t\t\t\tunits=self.embed_size,\n\t\t\t\t\t\t\t\tactivation=self.activation_func,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='item_fusion')\n\n\t\twith tf.name_scope(\"attention\"):\n\t\t\tself.feature_all = tf.concat([\n\t\t\t\t\t\tself.user_fusion, self.item_fusion], -1)\n\t\t\tself.att_layer1 = tf.layers.dense(inputs=self.feature_all,\n\t\t\t\t\t\t\t\tunits=1,\n\t\t\t\t\t\t\t\tactivation=self.activation_func,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='att_layer1')\n\t\t\tself.att_layer2 = tf.layers.dense(inputs=self.att_layer1,\n\t\t\t\t\t\t\t\tunits=self.embed_size,\n\t\t\t\t\t\t\t\tactivation=self.activation_func,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='att_layer2')\n\t\t\tself.att_weights = tf.nn.softmax(self.att_layer2, \n\t\t\t\t\t\t\t\taxis=-1, name='att_softmax')\n\n\t\twith tf.name_scope(\"prediction\"):\n\t\t\tself.interact = self.att_weights*self.user_fusion*self.item_fusion\n\t\t\tself.interact1 = tf.layers.dense(inputs=self.interact,\n\t\t\t\t\t\t\t\tunits=self.embed_size,\n\t\t\t\t\t\t\t\tactivation=self.activation_func,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='interact1')\n\t\t\tself.interact1 = tf.nn.dropout(self.interact1, self.dropout)\n\t\t\tself.prediction = tf.layers.dense(inputs=self.interact,\n\t\t\t\t\t\t\t\tunits=1,\n\t\t\t\t\t\t\t\tactivation=None,\n\t\t\t\t\t\t\t\tkernel_regularizer=self.regularizer,\n\t\t\t\t\t\t\t\tname='prediction')\n\t\t\tself.prediction = tf.reshape(self.prediction, [-1])","def __call__(self, params: Mapping[str, Any],\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\n ...","def model_fn(features,labels,mode,params):\n tf.logging.info('*** Features ***')\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s ,shape = %s\" % (name,features[name].shape))\n\n input_ids = features['input_ids']\n input_mask = features['input_mask']\n segment_ids = features['segment_ids']\n label_ids = features['label_ids']\n if 'is_real_example' in features:\n is_real_example = tf.cast(features['is_real_example'],dtype=tf.float32)\n else:\n is_real_example = tf.ones(tf.shape(label_ids),dtype=tf.float32)\n\n is_training = (mode == tf_estimator.estimator.ModeKeys.TRAIN)\n\n (total_loss,per_example_loss,probabilities,predictions) = \\\n create_model(albert_config,is_training,input_ids,input_mask,\n segment_ids,label_ids,num_labels,\n use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n if init_checkpoint:\n (assignment_map,initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)\n tf.train.init_from_checkpoint(init_checkpoint,assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf_estimator.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer(total_loss,\n learning_rate,\n num_train_steps,\n num_warmup_steps,use_tpu=False)\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n )\n elif mode == tf_estimator.estimator.ModeKeys.EVAL:\n def metric_fn(per_example_loss,label_ids,logits,is_real_example):\n accuracy = tf.metrics.accuracy(\n labels=label_ids,predictions=predictions,\n weights=is_real_example\n )\n loss = tf.metrics.mean(\n values=per_example_loss,weights=is_real_example\n )\n return {\n 'eval_accuracy':accuracy,\n 'eval_loss':loss,\n }\n\n eval_metrics = metric_fn(per_example_loss,label_ids,predictions,is_real_example)\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metric_ops=eval_metrics\n )\n else:\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n predictions={\n 'probabilities':probabilities,\n 'predictions':predictions,\n },\n )\n\n return output_spec","def build_model(cls, args, task):\n\n # make sure all arguments are present in older models\n base_architecture(args)\n\n if getattr(args, \"max_source_positions\", None) is None:\n args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS\n if getattr(args, \"max_target_positions\", None) is None:\n args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS\n\n args.ddp_rank = distributed_utils.get_data_parallel_rank()\n\n src_dict, tgt_dict = task.source_dictionary, task.target_dictionary\n\n if args.share_all_embeddings:\n if src_dict != tgt_dict:\n raise ValueError(\"--share-all-embeddings requires a joined dictionary\")\n if args.encoder_embed_dim != args.decoder_embed_dim:\n raise ValueError(\n \"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim\"\n )\n if args.decoder_embed_path and (\n args.decoder_embed_path != args.encoder_embed_path\n ):\n raise ValueError(\n \"--share-all-embeddings not compatible with --decoder-embed-path\"\n )\n encoder_embed_tokens = cls.build_embedding(\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\n )\n decoder_embed_tokens = encoder_embed_tokens\n args.share_decoder_input_output_embed = True\n else:\n encoder_embed_tokens = cls.build_embedding(\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\n )\n decoder_embed_tokens = cls.build_embedding(\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\n )\n if getattr(args, \"offload_activations\", False):\n args.checkpoint_activations = True # offloading implies checkpointing\n\n encoder_embed_positions = (\n PositionalEmbedding(\n args.max_source_positions,\n args.encoder_embed_dim,\n src_dict.pad(),\n learned=args.encoder_learned_pos,\n )\n if not args.no_token_positional_embeddings\n else None\n )\n\n decoder_embed_positions = (\n PositionalEmbedding(\n args.max_target_positions,\n args.decoder_embed_dim,\n tgt_dict.pad(),\n learned=args.decoder_learned_pos,\n )\n if not args.no_token_positional_embeddings\n else None\n )\n\n if args.share_decoder_input_output_embed:\n output_projection = torch.nn.Linear(\n decoder_embed_tokens.weight.shape[1],\n decoder_embed_tokens.weight.shape[0],\n bias=False,\n )\n output_projection.weight = decoder_embed_tokens.weight\n else:\n output_projection = torch.nn.Linear(\n args.decoder_embed_dim, len(tgt_dict), bias=False\n )\n torch.nn.init.normal_(\n output_projection.weight, mean=0, std=args.decoder_embed_dim**-0.5\n )\n\n encoder = cls.build_encoder(\n args,\n encoder_embed_tokens,\n encoder_embed_positions,\n src_dict,\n )\n decoder = cls.build_decoder(\n args,\n decoder_embed_tokens,\n decoder_embed_positions,\n output_projection,\n tgt_dict,\n )\n\n if not args.share_all_embeddings:\n min_params_to_wrap = getattr(\n args, \"min_params_to_wrap\", DEFAULT_MIN_PARAMS_TO_WRAP\n )\n # fsdp_wrap is a no-op when --ddp-backend != fully_sharded\n encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)\n decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)\n return cls(args, encoder, decoder)","def train(self, *args, **kwargs):\n raise NotImplementedError","def create_model(scene_dim_x, scene_dim_y, scene_dim_z):\n input_placeholder = tf.placeholder(\n tf.float32,\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x, 2],\n name='pl_scan')\n target_placeholder = tf.placeholder(\n tf.float32,\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x, 2],\n name='pl_target')\n target_lo_placeholder = tf.placeholder(\n tf.float32,\n shape=[1, scene_dim_z // 2, scene_dim_y // 2, scene_dim_x // 2, 2],\n name='pl_target_lo')\n target_sem_placeholder = tf.placeholder(\n tf.uint8,\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x],\n name='pl_target_sem')\n target_sem_lo_placeholder = tf.placeholder(\n tf.uint8,\n shape=[1, scene_dim_z // 2, scene_dim_y // 2, scene_dim_x // 2],\n name='pl_target_sem_lo')\n # No previous level input if at base level.\n if FLAGS.is_base_level:\n target_scan_low_resolution = None\n target_semantics_low_resolution = None\n else:\n target_scan_low_resolution = target_lo_placeholder\n target_semantics_low_resolution = target_sem_lo_placeholder\n logits = model.model(\n input_scan=input_placeholder,\n target_scan_low_resolution=target_scan_low_resolution,\n target_scan=target_placeholder,\n target_semantics_low_resolution=target_semantics_low_resolution,\n target_semantics=target_sem_placeholder,\n num_quant_levels=FLAGS.num_quant_levels,\n predict_semantics=FLAGS.predict_semantics,\n use_p_norm=FLAGS.p_norm > 0)\n return (input_placeholder, target_placeholder, target_lo_placeholder,\n target_sem_placeholder, target_sem_lo_placeholder, logits)","def build_tf_graph(self):\n raise NotImplementedError","def main():\n\n args = define_and_process_args()\n print('\\n', 'ARGUMENTS', '\\n\\n', args, '\\n')\n\n log_dir = get_log_dir(args)\n print('\\n', 'LOG DIRECTORY', '\\n\\n', log_dir, '\\n')\n\n standardized_data_path = os.path.join(args.data_dir, args.data_filename)\n if not os.path.exists(standardized_data_path):\n message = '%s does not exist.' % standardized_data_path\n raise ValueError(message)\n\n dataset = data.Dataset(standardized_data_path)\n train_raw_seqs, test_raw_seqs = dataset.get_splits(args.test_users)\n train_triplets = [data.prepare_raw_seq(seq) for seq in train_raw_seqs]\n test_triplets = [data.prepare_raw_seq(seq) for seq in test_raw_seqs]\n\n train_input_seqs, train_reset_seqs, train_label_seqs = zip(*train_triplets)\n test_input_seqs, test_reset_seqs, test_label_seqs = zip(*test_triplets)\n\n Model = eval('models.' + args.model_type + 'Model')\n input_size = dataset.input_size\n target_size = dataset.num_classes\n\n # This is just to satisfy a low-CPU requirement on our cluster\n # when using GPUs.\n if 'CUDA_VISIBLE_DEVICES' in os.environ:\n config = tf.ConfigProto(intra_op_parallelism_threads=2,\n inter_op_parallelism_threads=2)\n else:\n config = None\n\n with tf.Session(config=config) as sess:\n model = Model(input_size, target_size, args.num_layers,\n args.hidden_layer_size, args.init_scale,\n args.dropout_keep_prob)\n optimizer = optimizers.Optimizer(\n model.loss, args.num_train_sweeps, args.initial_learning_rate,\n args.num_initial_sweeps, args.num_sweeps_per_decay,\n args.decay_factor, args.max_global_grad_norm)\n train(sess, model, optimizer, log_dir, args.batch_size,\n args.num_sweeps_per_summary, args.num_sweeps_per_save,\n train_input_seqs, train_reset_seqs, train_label_seqs,\n test_input_seqs, test_reset_seqs, test_label_seqs)","def __init__(\n self,\n model_dir,\n model_filename=\"model.pdmodel\",\n params_filename=\"model.pdiparams\",\n precision=\"fp32\",\n use_trt=False,\n use_mkldnn=False,\n batch_size=1,\n device=\"CPU\",\n min_subgraph_size=3,\n use_dynamic_shape=False,\n cpu_threads=1,\n ):\n self.rerun_flag = False\n if device != \"GPU\" and use_trt:\n raise ValueError(\n \"Predict by TensorRT mode: {}, expect device=='GPU', but device == {}\".format(precision, device)\n )\n config = Config(os.path.join(model_dir, model_filename), os.path.join(model_dir, params_filename))\n if device == \"GPU\":\n # initial GPU memory(M), device ID\n config.enable_use_gpu(200, 0)\n # optimize graph and fuse op\n config.switch_ir_optim(True)\n else:\n config.disable_gpu()\n config.set_cpu_math_library_num_threads(cpu_threads)\n config.switch_ir_optim()\n if use_mkldnn:\n config.enable_mkldnn()\n if precision == \"int8\":\n config.enable_mkldnn_int8({\"conv2d\", \"depthwise_conv2d\", \"pool2d\", \"transpose2\", \"elementwise_mul\"})\n if precision == \"bf16\":\n config.enable_mkldnn_bfloat16()\n\n if precision == \"bf16\":\n config.enable_mkldnn_bfloat16()\n\n if use_trt:\n if precision == \"bf16\":\n print(\"paddle trt does not support bf16, switching to fp16.\")\n precision = \"fp16\"\n\n precision_map = {\n \"int8\": Config.Precision.Int8,\n \"fp32\": Config.Precision.Float32,\n \"fp16\": Config.Precision.Half,\n }\n assert precision in precision_map.keys()\n\n if use_dynamic_shape:\n dynamic_shape_file = os.path.join(model_dir, \"dynamic_shape.txt\")\n if os.path.exists(dynamic_shape_file):\n config.enable_tuned_tensorrt_dynamic_shape()\n print(\"trt set dynamic shape done!\")\n else:\n # In order to avoid memory overflow when collecting dynamic shapes, it is changed to use CPU.\n config.disable_gpu()\n config.set_cpu_math_library_num_threads(10)\n config.collect_shape_range_info(dynamic_shape_file)\n print(\"Start collect dynamic shape...\")\n self.rerun_flag = True\n\n if not self.rerun_flag:\n config.enable_tensorrt_engine(\n workspace_size=1 << 30,\n max_batch_size=batch_size,\n min_subgraph_size=min_subgraph_size,\n precision_mode=precision_map[precision],\n use_static=True,\n use_calib_mode=False,\n )\n\n # enable shared memory\n config.enable_memory_optim()\n self.predictor = create_predictor(config)\n self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()]\n self.output_handles = [self.predictor.get_output_handle(name) for name in self.predictor.get_output_names()]\n print(\"[Paddle Inference Backend] Completed PaddleInferenceEngine init ...\")","def main():\n\n args = get_arguments()\n\n w, h = map(int, args.input_size.split(','))\n\n config_path = os.path.join(os.path.dirname(args.restore_from),'opts.yaml')\n with open(config_path, 'r') as stream:\n config = yaml.load(stream)\n\n args.model = config['model']\n print('ModelType:%s'%args.model)\n print('NormType:%s'%config['norm_style'])\n gpu0 = args.gpu\n batchsize = args.batchsize\n\n model_name = os.path.basename( os.path.dirname(args.restore_from) )\n #args.save += model_name\n\n if not os.path.exists(args.save):\n os.makedirs(args.save)\n confidence_path = os.path.join(args.save, 'submit/confidence')\n label_path = os.path.join(args.save, 'submit/labelTrainIds')\n label_invalid_path = os.path.join(args.save, 'submit/labelTrainIds_invalid')\n for path in [confidence_path, label_path, label_invalid_path]:\n if not os.path.exists(path):\n os.makedirs(path)\n\n if args.model == 'DeepLab':\n model = DeeplabMulti(num_classes=args.num_classes, use_se = config['use_se'], train_bn = False, norm_style = config['norm_style'])\n elif args.model == 'Oracle':\n model = Res_Deeplab(num_classes=args.num_classes)\n if args.restore_from == RESTORE_FROM:\n args.restore_from = RESTORE_FROM_ORC\n elif args.model == 'DeeplabVGG':\n model = DeeplabVGG(num_classes=args.num_classes)\n if args.restore_from == RESTORE_FROM:\n args.restore_from = RESTORE_FROM_VGG\n\n if args.restore_from[:4] == 'http' :\n saved_state_dict = model_zoo.load_url(args.restore_from)\n else:\n saved_state_dict = torch.load(args.restore_from)\n\n try:\n model.load_state_dict(saved_state_dict)\n except:\n model = torch.nn.DataParallel(model)\n model.load_state_dict(saved_state_dict)\n model.eval()\n model.cuda(gpu0)\n\n testloader = data.DataLoader(DarkZurichDataSet(args.data_dir, args.data_list, crop_size=(h, w), resize_size=(w, h), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),\n batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)\n\n scale = 1.25\n testloader2 = data.DataLoader(DarkZurichDataSet(args.data_dir, args.data_list, crop_size=(round(h*scale), round(w*scale) ), resize_size=( round(w*scale), round(h*scale)), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),\n batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)\n\n\n if version.parse(torch.__version__) >= version.parse('0.4.0'):\n interp = nn.Upsample(size=(1080, 1920), mode='bilinear', align_corners=True)\n else:\n interp = nn.Upsample(size=(1080, 1920), mode='bilinear')\n\n sm = torch.nn.Softmax(dim = 1)\n log_sm = torch.nn.LogSoftmax(dim = 1)\n kl_distance = nn.KLDivLoss( reduction = 'none')\n prior = np.load('./utils/prior_all.npy').transpose((2,0,1))[np.newaxis, :, :, :]\n prior = torch.from_numpy(prior)\n for index, img_data in enumerate(zip(testloader, testloader2) ):\n batch, batch2 = img_data\n image, _, name = batch\n image2, _, name2 = batch2\n\n inputs = image.cuda()\n inputs2 = image2.cuda()\n print('\\r>>>>Extracting feature...%04d/%04d'%(index*batchsize, args.batchsize*len(testloader)), end='')\n if args.model == 'DeepLab':\n with torch.no_grad():\n output1, output2 = model(inputs)\n output_batch = interp(sm(0.5* output1 + output2))\n\n heatmap_batch = torch.sum(kl_distance(log_sm(output1), sm(output2)), dim=1)\n\n output1, output2 = model(fliplr(inputs))\n output1, output2 = fliplr(output1), fliplr(output2)\n output_batch += interp(sm(0.5 * output1 + output2))\n del output1, output2, inputs\n\n output1, output2 = model(inputs2)\n output_batch += interp(sm(0.5* output1 + output2))\n output1, output2 = model(fliplr(inputs2))\n output1, output2 = fliplr(output1), fliplr(output2)\n output_batch += interp(sm(0.5 * output1 + output2))\n del output1, output2, inputs2\n ratio = 0.95\n output_batch = output_batch.cpu() / 4\n # output_batch = output_batch *(ratio + (1 - ratio) * prior)\n output_batch = output_batch.data.numpy()\n heatmap_batch = heatmap_batch.cpu().data.numpy()\n elif args.model == 'DeeplabVGG' or args.model == 'Oracle':\n output_batch = model(Variable(image).cuda())\n output_batch = interp(output_batch).cpu().data.numpy()\n\n output_batch = output_batch.transpose(0,2,3,1)\n score_batch = np.max(output_batch, axis=3)\n output_batch = np.asarray(np.argmax(output_batch, axis=3), dtype=np.uint8)\n\n threshold = 0.3274\n for i in range(output_batch.shape[0]):\n output_single = output_batch[i,:,:]\n output_col = colorize_mask(output_single)\n output = Image.fromarray(output_single)\n\n name_tmp = name[i].split('/')[-1]\n dir_name = name[i].split('/')[-2]\n save_path = args.save + '/' + dir_name\n if not os.path.isdir(save_path):\n os.mkdir(save_path)\n output.save('%s/%s' % (save_path, name_tmp))\n print('%s/%s' % (save_path, name_tmp))\n output_col.save('%s/%s_color.png' % (save_path, name_tmp.split('.')[0]))\n\n # heatmap_tmp = heatmap_batch[i,:,:]/np.max(heatmap_batch[i,:,:])\n # fig = plt.figure()\n # plt.axis('off')\n # heatmap = plt.imshow(heatmap_tmp, cmap='viridis')\n # fig.colorbar(heatmap)\n # fig.savefig('%s/%s_heatmap.png' % (save_path, name_tmp.split('.')[0]))\n\n if args.set == 'test' or args.set == 'val':\n # label\n output.save('%s/%s' % (label_path, name_tmp))\n # label invalid\n output_single[score_batch[i, :, :] < threshold] = 255\n output = Image.fromarray(output_single)\n output.save('%s/%s' % (label_invalid_path, name_tmp))\n # conficence\n\n confidence = score_batch[i, :, :] * 65535\n confidence = np.asarray(confidence, dtype=np.uint16)\n print(confidence.min(), confidence.max())\n iio.imwrite('%s/%s' % (confidence_path, name_tmp), confidence)\n\n return args.save","def _ProjectImpl(self, tensor_names: List[Text]) -> \"TFXIO\":","def convert_to_model(self, *args):","def run_model (arguments):\n if arguments.train is not None:\n # Train a new model, optionally with a certain number of epochs\n predictor = None\n if len(arguments.train) > 0:\n predictor = train(n_epochs=arguments.train[0])\n else:\n predictor = train()\n # Afterwards save it\n now = datetime.now(timezone.utc)\n predictor.to_disk(fname=f\"model_parameters_{now.strftime('%Y%m%d%H%M%S')}\")\n elif arguments.export_embeddings:\n # Load the saved predictor ...\n predictor = Predictor.from_file()\n # ... and then dump the models to disk.\n predictor.subj.export_embeddings(\"subject\")\n predictor.obj.export_embeddings(\"object\")\n print(\"Models are saved to output directory for loading with http://projector.tensorflow.org/.\")\n elif arguments.console:\n # Opens a console for prediction without training\n predictor = Predictor.from_file()\n tinker(predictor)","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n (total_loss, per_example_loss, logits, probabilities) = model_function.create(\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\n num_labels, use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu,\n scope=(\"loss\" if model_function.freeze else None))\n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n elif mode == tf.estimator.ModeKeys.EVAL:\n\n if model_function.task_type == TaskType.CLASSIFICATION:\n\n def metric_fn(per_example_loss, label_ids, logits):\n predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)\n accuracy = tf.metrics.accuracy(label_ids, predictions)\n loss = tf.metrics.mean(per_example_loss)\n return {\n \"eval_accuracy\": accuracy,\n \"eval_loss\": loss,\n }\n elif model_function.task_type == TaskType.REGRESSION:\n\n def metric_fn(per_example_loss, label_ids, logits):\n ground_truth = tf.log1p(tf.clip_by_value(tf.cast(label_ids, tf.float32), 1e-8, 1e+30))\n predictions = tf.log1p(tf.clip_by_value(logits, 1e-8, 1e+30))\n return {\n \"eval_loss\": tf.metrics.mean(per_example_loss),\n \"another_loss\": tf.metrics.mean_squared_error(ground_truth, predictions)\n }\n else:\n raise NotImplementedError()\n\n eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metrics=eval_metrics,\n scaffold_fn=scaffold_fn)\n else:\n predictions = {\n \"result\": probabilities\n }\n print(probabilities.shape)\n print(type(probabilities))\n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)\n return output_spec","def main(args):\n with tf.Graph().as_default():\n with tf.Session() as sess:\n # prepare validate datasets\n train_x, train_y, test_x, test_y = load_dataset()\n\n # Load the modelc\n load_model(args.model)\n\n # Get input and output tensors, ignore phase_train_placeholder\n # for it have default value.\n inputs_placeholder = tf.get_default_graph().get_tensor_by_name(\"input:0\")\n\n feature_maps = tf.get_default_graph().\\\n get_tensor_by_name('MobileFaceNet/MobileFaceNet/'\n 'Conv2d_4_InvResBlock_5/Conv/Conv2D:0')\n\n feature_maps_r = tf.reshape(feature_maps, [-1, 3, 14, 14, 256])\n anchor_feature_maps = feature_maps_r[:, 0, :, :, :]\n pos_feature_maps = feature_maps_r[:, 1, :, :, :]\n neg_feature_maps = feature_maps_r[:, 2, :, :, :]\n\n anchor_labels = tf.placeholder(tf.int32, [None, 3], name='anchor_labels')\n neg_labels = tf.placeholder(tf.int32, [None, 3], name='neg_labels')\n test_labels = tf.placeholder(tf.int32, [None, 3], name='test_labels')\n learning_rate = tf.placeholder(tf.float32, name='learning_rate')\n\n anchor_logits, anchor_feature = network(feature_maps=anchor_feature_maps, keep_prob=0.5)\n pos_logits, pos_feature = network(feature_maps=pos_feature_maps, keep_prob=0.5)\n neg_logits, neg_feature = network(feature_maps=neg_feature_maps, keep_prob=0.5)\n test_logits, _ = network(feature_maps=feature_maps, keep_prob=1.0,\n is_training=False, reuse=True)\n train_accuracy = calculate_accuracy(logit=anchor_logits, label=anchor_labels,\n name='train_accuracy')\n test_accuracy = calculate_accuracy(logit=test_logits, label=test_labels,\n name='test_accuracy')\n\n with tf.name_scope(\"retrain_loss\"):\n pos_pair_loss = tf.losses.mean_squared_error(anchor_feature, pos_feature)\n temp_neg_pair_loss = tf.losses.mean_squared_error(anchor_feature, neg_feature)\n neg_pair_loss = tf.maximum(0.0, 2.0 - temp_neg_pair_loss)\n\n anchor_loss = cross_entropy_loss(anchor_logits, anchor_labels)\n pos_loss = cross_entropy_loss(pos_logits, anchor_labels)\n neg_loss = cross_entropy_loss(neg_logits, neg_labels)\n\n loss = anchor_loss + pos_loss + neg_loss + pos_pair_loss + neg_pair_loss\n\n with tf.name_scope(\"retrain_op\"): # not shown in the book\n optimizer = tf.train.GradientDescentOptimizer(learning_rate) # not shown\n train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,\n scope=\"retrain_net\")\n train_op = optimizer.minimize(loss, var_list=train_vars)\n\n uninitialized_vars = []\n for var in tf.all_variables():\n try:\n sess.run(var)\n except tf.errors.FailedPreconditionError:\n uninitialized_vars.append(var)\n\n init_new_vars_op = tf.initialize_variables(uninitialized_vars)\n sess.run(init_new_vars_op)\n\n model_path = './checkpoint/SR_model/'\n new_saver = tf.train.Saver()\n\n batch_size = 7\n n_epochs = 45\n train_iteration = len(train_x) // batch_size\n test_iteration = len(test_x) // batch_size\n\n # loop for epoch\n best_test_accuracy = 0\n epoch_lr = 0.0005\n for epoch in range(0, n_epochs):\n if epoch == int(epoch * 0.33) or epoch == int(epoch * 0.66):\n epoch_lr = epoch_lr * 0.1\n\n # get batch data\n for idx in range(0, train_iteration):\n batch_x = train_x[idx * batch_size:(idx + 1) * batch_size]\n batch_y = train_y[idx * batch_size:(idx + 1) * batch_size]\n\n concat_data = convert_train_data_batch(batch_x)\n anchor_label_r, neg_label_r = convert_train_label_batch(batch_y)\n\n train_feed_dict = {\n inputs_placeholder: concat_data,\n anchor_labels: anchor_label_r,\n neg_labels: neg_label_r,\n learning_rate: epoch_lr\n }\n\n _, train_accuracy_v, anchor_loss_v, pos_loss_v, neg_loss_v, pos_pair_loss_v, \\\n neg_pair_loss_v = sess.run([train_op, train_accuracy,\n anchor_loss, pos_loss, neg_loss, pos_pair_loss,\n neg_pair_loss], feed_dict=train_feed_dict)\n\n if idx % 10 == 0:\n # display training status\n print('Epoch: [%2d][%4d/%4d] Anchor Loss %.4f Pos Loss %.4f Neg Loss %.4f '\n 'Pos Pair Loss %.4f Neg Pair Loss %.4f Prec %.4f\\t'\n % (epoch, idx, train_iteration, anchor_loss_v, pos_loss_v, neg_loss_v,\n pos_pair_loss_v, neg_pair_loss_v, train_accuracy_v))\n\n total_test_accuracy = 0\n for idx in range(test_iteration):\n batch_x = test_x[idx * batch_size:(idx + 1) * batch_size]\n batch_y = test_y[idx * batch_size:(idx + 1) * batch_size]\n\n batch_x_r = convert_test_data_batch(batch_x)\n batch_y_r = convert_test_label_batch(batch_y)\n\n test_feed_dict = {\n inputs_placeholder: batch_x_r,\n test_labels: batch_y_r\n }\n\n test_accuracy_v = sess.run(test_accuracy, feed_dict=test_feed_dict)\n\n total_test_accuracy += test_accuracy_v\n total_test_accuracy /= test_iteration\n\n # display training status\n print(\"Epoch: [%2d/%2d]\\ttest_accuracy: %.2f\" \\\n % (epoch, n_epochs, total_test_accuracy))\n\n # save model\n if best_test_accuracy < total_test_accuracy:\n best_test_accuracy = total_test_accuracy\n new_saver.save(sess, os.path.join(model_path, 'SR_model_%2.4f.ckpt'\n % best_test_accuracy))","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n unique_ids = features[\"unique_ids\"]\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n (subject_logits, property_logits, value_logits) = self.create_model(\n bert_config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n params=params,\n use_one_hot_embeddings=use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n seq_length = modeling.get_shape_list(input_ids)[1]\n\n def compute_loss(logits, positions, depth):\n one_hot_positions = tf.one_hot(\n positions, depth=depth, dtype=tf.float32)\n log_probs = tf.nn.log_softmax(logits, axis=-1)\n loss = -tf.reduce_mean(\n tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\n return loss\n\n # subject, property, value로 나오도록\n subject_label = features[\"subject\"]\n property_label = features[\"property\"]\n value_label = features[\"value\"]\n res_length = params[\"res_length\"]\n ont_length = params[\"ont_length\"]\n\n subject_loss = compute_loss(subject_logits, subject_label, res_length)\n property_loss = compute_loss(property_logits, property_label, ont_length)\n value_loss = compute_loss(value_logits, value_label, res_length)\n\n total_loss = (subject_loss + property_loss + value_loss) / 3.0\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n elif mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {\n \"unique_ids\": unique_ids,\n \"subject_logits\": subject_logits,\n \"property_logits\": property_logits,\n \"value_logits\": value_logits,\n }\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)\n else:\n raise ValueError(\n \"Only TRAIN and PREDICT modes are supported: %s\" % (mode))\n\n return output_spec","def build(self):\n\n self.W = self.init([self.n_atom_input_feat, self.n_output])\n self.b = model_ops.zeros(shape=[\n self.n_output,\n ])\n\n self.trainable_weights = self.W + self.b","def main():\n args = get_arguments()\n\n # Create network.\n net = DeepLabLFOVModel()\n\n # Which variables to load.\n trainable = tf.trainable_variables()\n\n \n # Set up TF session and initialize variables. \n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n sess = tf.Session(config=config)\n init = tf.initialize_all_variables()\n \n sess.run(init)\n \n # Load weights.\n saver = tf.train.Saver(var_list=trainable)\n load(saver, sess, args.model_weights)\n\n path_to_save_parmas = '../pretrained_params/'\n net.save_convs_params(sess, path_to_save_parmas)\n #writer = tf.summary.FileWriter('./deeplab_graph/', sess.graph)","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n (total_loss, per_example_loss, log_probs) = create_model(\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\n num_labels, use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n\n scaffold_fn = None\n initialized_variable_names = []\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\n\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n\n elif mode == tf.estimator.ModeKeys.PREDICT:\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n mode=mode,\n predictions={\n \"log_probs\": log_probs,\n \"label_ids\": label_ids,\n },\n scaffold_fn=scaffold_fn)\n\n else:\n raise ValueError(\n \"Only TRAIN and PREDICT modes are supported: %s\" % (mode))\n\n return output_spec","def build(model_name):\n return pretrain.factory.create(model_name)","def build_model(cls, args, task):\n global PAD_IDX, EOS_IDX\n # make sure all arguments are present in older models\n w2v_lm_architecture2(args)\n\n if not hasattr(args, \"max_source_positions\"):\n args.max_source_positions = 2048\n if not hasattr(args, \"max_target_positions\"):\n args.max_target_positions = 2048\n\n tgt_dict = task.target_dictionary\n PAD_IDX = tgt_dict.pad()\n EOS_IDX = tgt_dict.eos()\n\n encoder = cls.build_encoder(args)\n assigner = cls.build_assigner(args, encoder.d)\n lm = cls.build_lm(args, task)\n\n return cls(args, encoder, assigner, lm)","def make_model():\n m = model_class(*argv[2:-1])\n modelobj[\"model\"] = m","def create(self) -> tf.keras.models.Model:\n raise NotImplementedError()","def __init__(self, model_name, logger=None, gpu_ids=None):\n super().__init__(model_name, 'generator', logger, gpu_ids)","def _tf2_ ( self , *args ) :\n ##\n if not hasattr ( self , '_wo2' ) : self._wo2 = _WO2_ ( self )\n if not self._wo2 : self._wo2 = _WO2_ ( self )\n ## \n _wo = self._wo2\n fun = ROOT.TF2 ( funID () , _wo , *args )\n fun.SetNpx ( 100 ) \n fun.SetNpy ( 100 ) \n #\n return fun","def __init__(self , model_file_name ):\n logging.set_verbosity(logging.ERROR)\n with TheTFGraph.as_default():\n with TheTFSession.as_default():\n self.model = keras.models.load_model( model_file_name + \".hdf5\" , compile=False )\n JSON = json.load( open(model_file_name + \".json\" ) )\n self.all_sites = list(JSON['all_sites'])\n self.all_errors = list(JSON['all_errors'])\n self.all_actions = list(JSON['all_actions'])\n self.IsBinary = bool(JSON['IsBinary'])\n self.TiersOnly = bool(JSON['TiersOnly'])\n self.Task = Task({} , \"TaskLoader\" , self)\n self.Name = model_file_name.split('/')[-1]\n self.ModelID = int( JSON['model'] )\n self.InputTrainingDataID = int( JSON['trainingdata'])\n\n self.Prediction = Prediction.Prediction( self.ModelID , self.InputTrainingDataID )","def model_fn(features,labels,mode,params):\n input_q = features[\"input_q\"] # query feature vector\n input_K = features[\"input_K\"] # Key set Matrix\n input_v = features[\"input_v\"] # image visual feature vector\n input_labels = features[\"input_labels\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n model = modeling.AMT(\n config = config,\n is_trainging = is_training, \n scope = \"AMT\",\n input_q = input_q,\n input_K = input_K,\n input_v = input_v\n )\n loss = model.loss\n q_doc_rank = model.get_predict()\n output_spec = None\n scaffold_fn = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer()\n output_spec = tf.estimator.EstimatorSpec(\n mode = mode,\n loss = loss,\n train_op = train_op,\n scaffold_fn = scaffold_fn)\n elif mode == tf.estimator.ModeKeys.EVAL:\n def metric_fn():\n return 0\n else:\n output_spec = tf.estimator.EstimatorSpec(\n mode = mode,\n predictions = q_doc_rank,\n scaffold_fn = scaffold_fn)\n return output_spec","def __init__(self,\n model,\n weights,\n singa_ops,\n keep_initializers_as_inputs=True):\n super(SingaRep, self).__init__()\n self.model = model\n self.tensor_map = weights\n self.keep_initializers_as_inputs = keep_initializers_as_inputs\n # this each item of singa_ops is: ('name', 'op', 'handle', 'forward')\n # the name is a string, op is OnnxNode,\n # handle is Singa handle to store the tensor into singa operator\n # the forward is singa autograd operator\n self.singa_ops = singa_ops","def initialize_model(\n *,\n model_def: nn.Module,\n input_spec: Sequence[Union[Tuple[Tuple[int, ...], jnp.dtype],\n Tuple[int, ...], None]],\n config: ml_collections.ConfigDict,\n rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],\n) -> Tuple[PyTree, PyTree, int, Optional[float]]:\n batch_size = (config.batch_size //\n jax.device_count()) if config.get('batch_size') else None\n dummy_input = []\n for spec in input_spec:\n if spec is not None:\n in_st = debug_utils.input_spec_to_jax_shape_dtype_struct(\n spec, batch_size=batch_size)\n dummy_input.append(jnp.zeros(in_st.shape, in_st.dtype))\n else:\n dummy_input.append(None)\n\n # We want all parameters to be created in host RAM, not on any device, they'll\n # be sent there later as needed, otherwise we already encountered two\n # situations where we allocate them twice.\n @functools.partial(jax.jit, backend='cpu')\n def _initialize_model(rngs):\n \"\"\"Initialization function to be jitted.\"\"\"\n init_model_state, init_params = model_def.init(\n rngs, *dummy_input, train=False, debug=False).pop('params')\n # Set bias in the head to low value, such that loss is small initially.\n if config.get('init_head_bias', None) is not None:\n init_params = flax.core.unfreeze(init_params)\n init_params['output_projection'] = optimizers.tree_map_with_names(\n lambda p: jnp.full_like(p, config.init_head_bias),\n init_params['output_projection'],\n match_name_fn=lambda name: 'bias' in name)\n init_params = flax.core.freeze(init_params)\n return init_params, init_model_state\n\n if not isinstance(rngs, dict):\n rngs = {'params': rngs}\n init_params, init_model_state = _initialize_model(rngs)\n # Pop out params rng:\n rngs.pop('params')\n\n # Count number of trainable parameters:\n num_trainable_params = debug_utils.log_param_shapes(init_params)\n\n # Count gflops:\n count_flops = config.get('count_flops',\n ml_collections.ConfigDict({'count_flops': True}))\n if count_flops:\n variables = {'params': init_params, **init_model_state}\n flops = debug_utils.compute_flops(\n flax_model_apply_fn=functools.partial(\n model_def.apply, variables, train=False, debug=False, rngs=rngs),\n input_spec=count_flops.get('input_spec', input_spec),\n fuse_multiply_add=count_flops.get('fuse_multiply_add', True))\n gflops = flops / (10**9)\n else:\n gflops = None\n\n return init_params, init_model_state, num_trainable_params, gflops","def build_model(self, constructor, args):\n dims = {'en': 300, 'es': 50}\n dists = {'en': 'Normal',\n 'es': 'Normal',}\n z_dim = args.model_args.get('z_dim', 64)\n h_dim = args.model_args.get('h_dim', 64)\n n_layers = args.model_args.get('n_layers', 3)\n gauss_out = (args.model != 'MultiDKS') \n encoders = {'en': models.common.DeepGaussianMLP(dims['en'], z_dim, h_dim, n_layers),\n 'es': models.common.DeepGaussianMLP(dims['es'], z_dim, h_dim, n_layers)}\n decoders = {'en': models.common.DeepGaussianMLP(z_dim, dims['en'], h_dim, n_layers),\n 'es': models.common.DeepGaussianMLP(z_dim, dims['es'], h_dim, n_layers)}\n custom_mods = [m for m in ['en', 'es'] if m in args.modalities]\n model = constructor(args.modalities,\n dims=(dims[m] for m in args.modalities),\n dists=[dists[m] for m in args.modalities],\n encoders={m: encoders[m] for m in custom_mods},\n decoders={m: decoders[m] for m in custom_mods},\n z_dim=z_dim, h_dim=h_dim,\n device=args.device, **args.model_args)\n return model","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n # tf.logging.info(\"*** Features ***\")\n # for name in sorted(features.keys()):\n # tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n is_real_example = None\n if \"is_real_example\" in features:\n is_real_example = tf.cast(features[\"is_real_example\"], dtype=tf.float32)\n else:\n is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)\n\n is_training = mode == tf.estimator.ModeKeys.TRAIN\n\n model = modeling.BertModel(\n config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n token_type_ids=segment_ids,\n use_one_hot_embeddings=use_one_hot_embeddings,\n )\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint:\n (\n assignment_map,\n initialized_variable_names,\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu\n )\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode, loss=total_loss, train_op=train_op, scaffold=scaffold_fn\n )\n elif mode == tf.estimator.ModeKeys.EVAL:\n\n def metric_fn(per_example_loss, label_ids, probabilities, is_real_example):\n\n logits_split = tf.split(probabilities, num_labels, axis=-1)\n label_ids_split = tf.split(label_ids, num_labels, axis=-1)\n # metrics change to auc of every class\n eval_dict = {}\n for j, logits in enumerate(logits_split):\n label_id_ = tf.cast(label_ids_split[j], dtype=tf.int32)\n current_auc, update_op_auc = tf.metrics.auc(label_id_, logits)\n eval_dict[str(j)] = (current_auc, update_op_auc)\n eval_dict[\"eval_loss\"] = tf.metrics.mean(values=per_example_loss)\n return eval_dict\n\n\n eval_metrics = metric_fn(\n per_example_loss, label_ids, probabilities, is_real_example\n )\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metric_ops=eval_metrics,\n scaffold=scaffold_fn,\n )\n else:\n out = {\n \"input_ids\": input_ids,\n \"label_ids\": label_ids,\n }\n all_layers = model.get_all_encoder_layers()\n for (i, layer_index) in enumerate(layer_indexes):\n out[\"layer_output_%d\" % i] = all_layers[layer_index]\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode, predictions=out, scaffold=scaffold_fn\n )\n return output_spec","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n input_mask = features[\"input_mask\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n is_prediction = (mode == tf.estimator.ModeKeys.PREDICT)\n\n (total_loss, per_example_loss, logits, probabilities) = create_model(\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\n num_labels, use_one_hot_embeddings, is_prediction)\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, False)\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n )\n elif mode == tf.estimator.ModeKeys.EVAL:\n predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)\n accuracy = tf.metrics.accuracy(\n labels=label_ids, predictions=predictions)\n loss = tf.metrics.mean(values=per_example_loss)\n eval_metrics = {\n \"eval_accuracy\": accuracy,\n \"eval_loss\": loss,\n }\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metric_ops=eval_metrics\n )\n else:\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n predictions={\"probabilities\": probabilities},\n export_outputs={'predict': tf.estimator.export.PredictOutput(outputs=probabilities)}\n )\n return output_spec","def new(cls, args, src_meta, trg_meta, waitk_lagging, name=None):\n # build source and target modality\n src_modality, trg_modality = cls.build_modalities(args, src_meta, trg_meta)\n encoder_params, decoder_params = {}, {}\n for f in cls.class_or_method_args():\n if f.name in args:\n if f.name.startswith(\"encoder.\"):\n encoder_params[f.name[8:]] = args[f.name]\n elif f.name.startswith(\"decoder.\"):\n decoder_params[f.name[8:]] = args[f.name]\n # build encoder and decoder\n encoder = build_encoder({\n \"encoder.class\": \"TransformerEncoder\",\n \"encoder.params\": encoder_params})\n decoder = build_decoder({\n \"decoder.class\": \"TransformerDecoder\",\n \"decoder.params\": decoder_params})\n model = cls(args, src_meta, trg_meta, src_modality, trg_modality,\n encoder, decoder, name=name)\n model.wait_k = waitk_lagging\n _ = model({\"src\": tf.convert_to_tensor([[1, 2, 3]], tf.int64),\n \"src_padding\": tf.convert_to_tensor([[0, 0., 0]], tf.float32),\n \"trg_input\": tf.convert_to_tensor([[1, 2, 3]], tf.int64)})\n return model","def main(args):\n # Use CUDA\n use_cuda = args.use_cuda and torch.cuda.is_available()\n device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n # Fix random seed\n torch.manual_seed(args.seed)\n\n # Generate token-to-index and index-to-token mapping\n tok2id, id2tok = data_loader.build_or_load_vocab(\n args.train, overwrite=False)\n\n print(\"*\" * 5)\n print(args)\n\n # Create DataLoader() objects\n params = {\n \"batch_size\": args.batch_size,\n \"collate_fn\": data_loader.collate_fn,\n \"shuffle\": args.shuffle,\n \"num_workers\": args.num_workers,\n }\n # train_dataset = data_loader.SNLIDataSet(args.train, tok2id)\n # train_loader = torch.utils.data.DataLoader(train_dataset, **params)\n val_dataset = data_loader.SNLIDataSet(args.val, tok2id)\n val_loader = torch.utils.data.DataLoader(val_dataset, **params)\n\n # Initialize model\n if args.model == \"rnn\": # RNN model\n model = RNN(\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\n emb_dim=const.EMB_DIM, # Embedding dimensions\n hidden_dim=args.hidden_dim, # Hidden dimensions\n dropout_prob=args.dropout_prob, # Dropout probability\n padding_idx=const.PAD_IDX, # Padding token index\n num_classes=const.NUM_CLASSES, # Number of class labels\n id2tok=id2tok, # Vocabulary\n ).to(device)\n # Load model weights from disk\n model.load_state_dict(torch.load(const.MODELS + \"rnn.pt\"))\n model.eval()\n elif args.model == \"cnn\": # CNN model\n model = CNN(\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\n emb_dim=const.EMB_DIM, # Embedding dimensions\n hidden_dim=args.hidden_dim, # Hidden dimensions\n kernel_size=args.kernel_size, # Kernel size\n dropout_prob=args.dropout_prob, # Dropout probability\n padding_idx=const.PAD_IDX, # Padding token index\n num_classes=const.NUM_CLASSES, # Number of class labels\n id2tok=id2tok, # Vocabulary\n ).to(device)\n # Load model weights from disk\n model.load_state_dict(torch.load(const.MODELS + \"cnn.pt\"))\n model.eval()\n else:\n print(\"Invalid model specification, exiting\")\n exit()\n\n # Criterion\n criterion = torch.nn.CrossEntropyLoss()\n # Model parameters\n params = [p for p in model.parameters() if p.requires_grad]\n\n # Inspect correct/incorrect predictions\n if args.inspect:\n right, wrong = eval_model(val_loader, model, device, criterion,\n inspect=True)\n print(\"\\nValidation premises with correct predictions:\\n\")\n for i, item in enumerate(right):\n text = \" \".join([id2tok[idx] for idx in item if idx > 0])\n print(\"#{}\\n {}\".format(i + 1, text))\n print(\"\\nValidation premises with incorrect predictions:\\n\")\n for i, item in enumerate(wrong):\n text = \" \".join([id2tok[idx] for idx in item if idx > 0])\n print(\"#{}\\n {}\".format(i + 1, text))\n return\n\n # Validation\n val_acc, _ = eval_model(val_loader, model, device, criterion)\n print(\"\\n Validation accuracy: {}\".format(val_acc))\n\n print(\"*\" * 5 + \"\\n\")","def __init__(self,args,train=True,reuse=None,model=None):\n self.max_seq_len = args.max_seq_len\n self.vocab_size = args.vocab_size\n self.hidden_size = args.hidden_size\n\n initialize = model is None # whether to initialize variables\n\n # evice = \"/cpu:0\" if args.cpu else \"\"\n self.graph = tf.Graph() if model is None else model.graph\n self.session = tf.Session(graph=self.graph) \\\n if model is None else model.session\n\n with self.graph.as_default(),\\\n tf.variable_scope(\"LanguageModel\") as vs:\n self._seq = tf.placeholder(\n tf.int64,[None,self.max_seq_len])\n self._len = tf.placeholder(\n tf.int64,[None,])\n\n cell = tf.nn.rnn_cell.BasicLSTMCell(\n self.hidden_size,state_is_tuple=True)\n\n # Running RNN through sequence\n logit, _ = self.rnn_with_embedding(\n cell,None,self._seq, self._len,reuse=reuse)\n\n logit_list = tf.unpack(tf.transpose(logit,[1,0,2]))\n seq_list = tf.unpack(tf.transpose(self._seq,[1,0]))\n seq_list = seq_list[1:]\n\n xent = self.softmax_xent_loss_sequence(\n logit_list,seq_list,self._len,self.max_seq_len)\n\n self._cost = xent\n\n if train:\n log(vs.name+\"/Adding optimizer\")\n with tf.variable_scope(\"AdamOptimizer\"):\n optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)\n self._train_op = optimizer.minimize(self._cost)\n\n if initialize:\n log(vs.name+\"/Initializing variables\")\n self.session.run(tf.initialize_all_variables())\n\n log(\"Done with constructor.\")","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n # tf.logging.info(\"*** Features ***\")\n # for name in sorted(features.keys()):\n # tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n (total_loss, per_example_loss, logits, probabilities) = create_model(\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\n num_labels, use_one_hot_embeddings)\n\n # output_spec = tf.contrib.tpu.TPUEstimatorSpec(\n # mode=tf.estimator.ModeKeys.PREDICT,\n # predictions=probabilities)\n output_spec = tf.estimator.EstimatorSpec(\n mode=tf.estimator.ModeKeys.PREDICT,\n predictions=probabilities\n )\n return output_spec","def do_new(self, args):\n model_name = questionary.text(\"Target name:\").ask()\n model_name = model_name.replace(\" \", \"\")\n\n available_frameworks = list(CFState.get_instance().loaded_frameworks.keys())\n framework_choice = questionary.select(\"Which framework?\", choices=available_frameworks).ask()\n\n if \"textattack\" in framework_choice:\n framework = \"TextTarget\"\n elif \"art\" in framework_choice:\n framework = \"ArtTarget\"\n else:\n raise ValueError(\"invalid framework\")\n\n if framework == \"TextTarget\":\n model_data_type = \"text\"\n elif framework == \"ArtTarget\":\n model_data_type = questionary.select(\"What data type?\", choices=[\"numpy\", \"image\"]).ask()\n else:\n raise ValueError(\"invalid framework\")\n\n if model_name not in os.listdir(config.targets_path):\n try:\n os.mkdir(f\"{config.targets_path}/{model_name}\")\n open(f\"{config.targets_path}/{model_name}/__init__.py\", \"w\").close()\n with open(f\"{config.targets_path}/{model_name}/{model_name}.py\", \"w\") as f:\n f.write(\n f\"\"\"\n\n# Generated by counterfit #\n\nfrom counterfit.core.targets import {framework}\n\nclass {model_name.capitalize()}({framework}):\n model_name = \"{model_name.lower()}\"\n model_data_type = \"{model_data_type}\"\n model_endpoint = \"\"\n model_input_shape = ()\n model_output_classes = []\n X = []\n\n def __init__(self):\n self.X = []\n\n def __call__(self, x):\n return x\n\"\"\"\n )\n\n CFState.get_instance().import_targets()\n except Exception as e:\n\n self.pwarning(f\"\\n [!] Failed to write target file: {e}.\\n\")\n\n else:\n self.pwarning(f\"\\n [!] {model_name} already exists. Choose a new name.\\n\")","def build_model(self) -> nn.Module:\n pass","def _tf1_ ( self , *args ) :\n #\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\n if not self._wo1 : self._wo1 = _WO1_ ( self )\n ## \n _wo = self._wo1 \n fun = ROOT.TF1 ( funID() , _wo , *args )\n fun.SetNpx ( 500 ) \n #\n return fun","def model_fn_builder(num_labels, learning_rate, num_train_steps, num_warmup_steps):\n def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n label_ids = features[\"label_ids\"]\n\n is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\n\n # TRAIN and EVAL\n if not is_predicting:\n\n (loss, predicted_labels, log_probs) = create_model(\n is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n\n train_op = create_optimizer(\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)\n\n # Calculate evaluation metrics.\n def metric_fn(label_ids, predicted_labels):\n accuracy = tf.compat.v1.metrics.accuracy(label_ids, predicted_labels)\n #f1_score = tf.contrib.metrics.f1_score(\n # label_ids,\n # predicted_labels)\n #auc = tf.metrics.auc(\n # label_ids,\n # predicted_labels)\n #recall = tf.metrics.recall(\n # label_ids,\n # predicted_labels)\n #precision = tf.metrics.precision(\n # label_ids,\n # predicted_labels)\n #true_pos = tf.metrics.true_positives(\n # label_ids,\n # predicted_labels)\n #true_neg = tf.metrics.true_negatives(\n # label_ids,\n # predicted_labels)\n #false_pos = tf.metrics.false_positives(\n # label_ids,\n # predicted_labels)\n #false_neg = tf.metrics.false_negatives(\n # label_ids,\n # predicted_labels)\n return {\n \"eval_accuracy\": accuracy,\n # \"f1_score\": f1_score,\n #\"auc\": auc,\n # \"precision\": precision,\n # \"recall\": recall,\n # \"true_positives\": true_pos,\n # \"true_negatives\": true_neg,\n # \"false_positives\": false_pos,\n # \"false_negatives\": false_neg\n }\n\n eval_metrics = metric_fn(label_ids, predicted_labels)\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n return tf.estimator.EstimatorSpec(mode=mode,\n loss=loss,\n train_op=train_op)\n else:\n return tf.estimator.EstimatorSpec(mode=mode,\n loss=loss,\n eval_metric_ops=eval_metrics)\n else:\n (predicted_labels, log_probs) = create_model(\n is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\n\n predictions = {\n 'probabilities': log_probs,\n 'labels': predicted_labels\n }\n return tf.estimator.EstimatorSpec(mode, predictions=predictions)\n\n # Return the actual model function in the closure\n return model_fn","def construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 16404\",name='inp_slice')\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\n gt_y = lbann.Identity(inp_slice,name='gt_y')\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\n\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\n\n z_dim = 20 #Latent space dim\n\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\"20\")\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\n\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\"1 -1\"))\n\n layers = list(lbann.traverse_layer_graph(input))\n # Setup objective function\n weights = set()\n src_layers = []\n dst_layers = []\n for l in layers:\n if(l.weights and \"disc0\" in l.name and \"instance1\" in l.name):\n src_layers.append(l.name)\n #freeze weights in disc2\n if(l.weights and \"disc1\" in l.name):\n dst_layers.append(l.name)\n for idx in range(len(l.weights)):\n l.weights[idx].optimizer = lbann.NoOptimizer()\n weights.update(l.weights)\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\n # Initialize check metric callback\n metrics = [lbann.Metric(img_loss, name='recon_error')]\n #pred_y = macc_models.MACCWAE.pred_y_name\n callbacks = [lbann.CallbackPrint(),\n lbann.CallbackTimer(),\n lbann.CallbackSaveModel(dir=args.dump_models),\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\n destination_layers=list2str(dst_layers),\n batch_interval=2)]\n\n if(args.ltfb_batch_interval > 0) :\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\n low_score_wins=True,\n exchange_hyperparameters=True))\n\n # Construct model\n return lbann.Model(args.num_epochs,\n serialize_io=True,\n weights=weights,\n layers=layers,\n metrics=metrics,\n objective_function=obj,\n callbacks=callbacks)","def build_model_mobilenet(num_classes):","def _create_model(self):\n if torch.cuda.is_available():\n model = torch.jit.load(self.torch_jit).cuda()\n else:\n model = torch.jit.load(self.torch_jit)\n model.eval()\n return model","def __init__(self, model):\r\n self._tensorflow_session = model._tensorflow_session\r\n self._model = model","def build_model(cls, args, task):\n # from fairseq.tasks.multilingual_translation import MultilingualTranslationTask\n # assert isinstance(task, MultilingualTranslationTask)\n\n # make sure all arguments are present in older models\n base_architecture(args)\n\n if args.share_encoders:\n args.share_encoder_embeddings = True\n\n ### nat model\n # build shared embeddings (if applicable)\n src_dict, tgt_dict = task.source_dictionary, task.target_dictionary\n if args.share_all_embeddings:\n if src_dict != tgt_dict:\n raise ValueError(\"--share-all-embeddings requires a joined dictionary\")\n if args.encoder_embed_dim != args.decoder_embed_dim:\n raise ValueError(\n \"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim\"\n )\n if args.decoder_embed_path and (\n args.decoder_embed_path != args.encoder_embed_path\n ):\n raise ValueError(\n \"--share-all-embeddings not compatible with --decoder-embed-path\"\n )\n encoder_embed_tokens = TransformerModel.build_embedding(\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\n )\n decoder_embed_tokens = encoder_embed_tokens\n args.share_decoder_input_output_embed = True\n else:\n encoder_embed_tokens = TransformerModel.build_embedding(\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\n )\n decoder_embed_tokens = TransformerModel.build_embedding(\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\n )\n\n\n student_cls = ARCH_MODEL_REGISTRY[args.student_arch]\n encoder = student_cls.build_encoder(args, src_dict, encoder_embed_tokens)\n decoder = student_cls.build_decoder(args, tgt_dict, decoder_embed_tokens)\n student = student_cls(args,encoder,decoder)\n\n teacher_cls = ARCH_MODEL_REGISTRY[args.teacher_arch]\n if not issubclass(teacher_cls, NATransformerModel):\n teacher_cls = PatchedTransformerModel\n\n teacher_encoder = teacher_cls.build_encoder(\n args, src_dict,\n encoder_embed_tokens if args.share_encoder_embeddings else TransformerModel.build_embedding(\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\n )\n )\n teacher_decoder = teacher_cls.build_decoder(\n args, tgt_dict,\n decoder_embed_tokens if args.share_decoder_embeddings else TransformerModel.build_embedding(\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\n )\n )\n teacher = teacher_cls(args,teacher_encoder,teacher_decoder)\n\n return cls(args, student, teacher)","def __init__(self, args, task_name, input_fields, conversion_name, cache=None, train_types_only=False):\n self.input_fields = input_fields\n self.task_name = task_name\n self.conversion_name = conversion_name\n self.conversion_dict = None\n self.name_to_index_dict = None\n self.cache = None\n self.ints_to_strings = {}\n self.label_vocab = {'[PAD]':0, '-':0, '_':0}\n self.train_types_only = train_types_only\n self.train_type_vocab = set()","def _train_model(self):\n raise NotImplementedError()","def get_model(point_cloud, feature_cloud, color_cloud, s1, s2, s3, s4, g1, g2, g3, g4, c1, c2, c3, c4, t1, t2, t3, t4, is_training, num_class, use_color=0, bn_decay=None):\n batch_size = point_cloud.get_shape()[0].value\n num_point = point_cloud.get_shape()[1].value\n l0_xyz = point_cloud\n l0_points = None\n\n if use_color == 0:\n l0_points = None\n else:\n l0_points = feature_cloud\n if use_color == 2:\n l0_cloud = TextureConv(color_cloud, is_training, bn_decay)\n l0_points = tf.concat([l0_points,l0_cloud],axis=-1)\n\n # Layer 1\n l1_xyz, l1_points = texture_geodesic_conv(s1, g1, c1, t1, l0_xyz, l0_points, npoint=1024, radius=0.1, conv_radius=0.1, conv_mlp = None, nsample=32, mlp=[32, 32, 64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_color=use_color)\n l2_xyz, l2_points = texture_geodesic_conv(s2, g2, c2, t2, l1_xyz, l1_points, npoint=256, radius=0.2, conv_radius=0.2, conv_mlp = None, nsample=32, mlp=[64, 64, 128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2', use_color=use_color)\n l3_xyz, l3_points = texture_geodesic_conv(s3, g3, c3, t3, l2_xyz, l2_points, npoint=64, radius=0.4, conv_radius=0.4, conv_mlp = None, nsample=32, mlp=[128, 128, 256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3', use_color=use_color)\n l4_xyz, l4_points = texture_geodesic_conv(s4, g4, c4, t4, l3_xyz, l3_points, npoint=16, radius=0.8, conv_radius=0.8, conv_mlp = None, nsample=32, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4', use_color=use_color)\n\n # Feature Propagation layers\n l3_points = texture_geodesic_tconv(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay, scope='fa_layer1')\n l2_points = texture_geodesic_tconv(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer2')\n l1_points = texture_geodesic_tconv(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer3')\n l0_points = texture_geodesic_tconv(l0_xyz, l1_xyz, l0_points, l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer4')\n\n # FC layers\n net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)\n net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')\n net = tf_util.conv1d(net, num_class, 1, padding='VALID', activation_fn=None, scope='fc2')\n\n return net","def __init__(self, input_seq_batch, seq_length_batch, image_feat_batch,\n num_vocab, num_choices, module_names, dropout_keep_prob,\n scope='model', reuse=None):\n\n with tf.variable_scope(scope, reuse=reuse):\n self.T_ctrl = cfg.MODEL.T_CTRL\n\n # Input unit\n lstm_seq, q_encoding, embed_seq = input_unit.build_input_unit(\n input_seq_batch, seq_length_batch, num_vocab)\n kb_batch = input_unit.build_kb_batch(image_feat_batch)\n\n # Controller and NMN\n num_module = len(module_names)\n self.controller = controller.Controller(\n lstm_seq, q_encoding, embed_seq, seq_length_batch, num_module)\n self.c_list = self.controller.c_list\n self.module_logits = self.controller.module_logits\n self.module_probs = self.controller.module_probs\n self.module_prob_list = self.controller.module_prob_list\n self.nmn = nmn.NMN(\n kb_batch, self.c_list, module_names, self.module_prob_list)\n\n # Output unit\n if cfg.MODEL.BUILD_VQA:\n self.vqa_scores = output_unit.build_output_unit_vqa(\n q_encoding, self.nmn.mem_last, num_choices,\n dropout_keep_prob=dropout_keep_prob)\n if cfg.MODEL.BUILD_LOC:\n loc_scores, bbox_offset, bbox_offset_fcn = \\\n output_unit.build_output_unit_loc(\n q_encoding, kb_batch, self.nmn.att_last)\n self.loc_scores = loc_scores\n self.bbox_offset = bbox_offset\n self.bbox_offset_fcn = bbox_offset_fcn\n\n # Reconstruction loss\n if cfg.MODEL.REC.USE_REC_LOSS:\n rec_inputs = (self.module_logits if cfg.MODEL.REC.USE_LOGITS\n else self.module_probs)\n if cfg.MODEL.REC.USE_TXT_ATT:\n rec_inputs = tf.concat(\n [rec_inputs, tf.stack(self.c_list)], axis=-1)\n self.rec_loss = output_unit.build_output_unit_rec(\n rec_inputs, input_seq_batch, embed_seq, seq_length_batch,\n num_vocab)\n else:\n self.rec_loss = tf.convert_to_tensor(0.)\n\n self.params = [\n v for v in tf.trainable_variables() if scope in v.op.name]\n self.l2_reg = tf.add_n(\n [tf.nn.l2_loss(v) for v in self.params\n if v.op.name.endswith('weights')])\n\n # tensors for visualization\n self.vis_outputs = {\n 'txt_att': # [N, T, S]\n tf.transpose( # [S, N, T] -> [N, T, S]\n tf.concat(self.controller.cv_list, axis=2), (1, 2, 0)),\n 'att_stack': # [N, T, H, W, L]\n tf.stack(self.nmn.att_stack_list, axis=1),\n 'stack_ptr': # [N, T, L]\n tf.stack(self.nmn.stack_ptr_list, axis=1),\n 'module_prob': # [N, T, D]\n tf.stack(self.module_prob_list, axis=1)}\n if cfg.MODEL.BUILD_VQA:\n self.vis_outputs['vqa_scores'] = self.vqa_scores\n if cfg.MODEL.BUILD_LOC:\n self.vis_outputs['loc_scores'] = self.loc_scores\n self.vis_outputs['bbox_offset'] = self.bbox_offset","def model_fn_builder(albert_config,num_labels,init_checkpoint,learning_rate,\n num_train_steps,num_warmup_steps,\n use_one_hot_embeddings,optimizer='adamw'):\n\n def model_fn(features,labels,mode,params):\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n tf.logging.info('*** Features ***')\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s ,shape = %s\" % (name,features[name].shape))\n\n input_ids = features['input_ids']\n input_mask = features['input_mask']\n segment_ids = features['segment_ids']\n label_ids = features['label_ids']\n if 'is_real_example' in features:\n is_real_example = tf.cast(features['is_real_example'],dtype=tf.float32)\n else:\n is_real_example = tf.ones(tf.shape(label_ids),dtype=tf.float32)\n\n is_training = (mode == tf_estimator.estimator.ModeKeys.TRAIN)\n\n (total_loss,per_example_loss,probabilities,predictions) = \\\n create_model(albert_config,is_training,input_ids,input_mask,\n segment_ids,label_ids,num_labels,\n use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n if init_checkpoint:\n (assignment_map,initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)\n tf.train.init_from_checkpoint(init_checkpoint,assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf_estimator.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer(total_loss,\n learning_rate,\n num_train_steps,\n num_warmup_steps,use_tpu=False)\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op,\n )\n elif mode == tf_estimator.estimator.ModeKeys.EVAL:\n def metric_fn(per_example_loss,label_ids,logits,is_real_example):\n accuracy = tf.metrics.accuracy(\n labels=label_ids,predictions=predictions,\n weights=is_real_example\n )\n loss = tf.metrics.mean(\n values=per_example_loss,weights=is_real_example\n )\n return {\n 'eval_accuracy':accuracy,\n 'eval_loss':loss,\n }\n\n eval_metrics = metric_fn(per_example_loss,label_ids,predictions,is_real_example)\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metric_ops=eval_metrics\n )\n else:\n output_spec = tf_estimator.estimator.EstimatorSpec(\n mode=mode,\n predictions={\n 'probabilities':probabilities,\n 'predictions':predictions,\n },\n )\n\n return output_spec\n\n return model_fn","def model_fn_builder(adj_mat, w2n, n2w, bert_config, init_checkpoint, learning_rate,\n num_train_steps, num_warmup_steps, use_tpu,\n use_one_hot_embeddings):\n\n def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n \"\"\"The `model_fn` for TPUEstimator.\"\"\"\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n # INFO:tensorflow: name = input_ids, shape = (?, 180)\n # INFO:tensorflow: name = input_mask, shape = (?, 180)\n # INFO:tensorflow: name = is_real_example, shape = (?,)\n # INFO:tensorflow: name = label_ids, shape = (?,)\n # INFO:tensorflow: name = masked_lm_ids, shape = (?, 180)\n # INFO:tensorflow: name = masked_lm_positions, shape = (?, 180)\n # INFO:tensorflow: name = masked_lm_weights, shape = (?, 180)\n # INFO:tensorflow: name = segment_ids, shape = (?, 180)\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n masked_lm_positions = features[\"masked_lm_positions\"]\n masked_lm_ids = features[\"masked_lm_ids\"]\n masked_lm_weights = features[\"masked_lm_weights\"]\n #next_sentence_labels = features[\"next_sentence_labels\"]\n \n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n model = modeling.BertModel(\n config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n token_type_ids=segment_ids,\n use_one_hot_embeddings=use_one_hot_embeddings)\n \n gcn_embedding = build_gcn_output(adj_mat, w2n, n2w, model.get_embedding_table(), bert_config, is_training)\n \n (masked_lm_loss,\n masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(\n bert_config, model.get_sequence_output(), gcn_embedding,\n masked_lm_positions, masked_lm_ids, masked_lm_weights)\n\n\n masked_lm_loss = tf.identity(masked_lm_loss, name=\"masked_lm_loss\")\n\n\n total_loss = masked_lm_loss\n\n total_loss = tf.identity(total_loss, name='total_loss')\n\n tvars = tf.trainable_variables()\n\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint and (not FLAGS.use_horovod or hvd.rank() == 0):\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n if not FLAGS.use_horovod or hvd.rank() == 0:\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer(\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, FLAGS.use_horovod)\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n train_op=train_op)\n return output_spec\n elif mode == tf.estimator.ModeKeys.PREDICT:\n\n #def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\n # masked_lm_weights):#, next_sentence_example_loss,\n #next_sentence_log_probs, next_sentence_labels):\n \"\"\"Computes the loss and accuracy of the model.\"\"\"\n #masked_lm_log_probs = tf.reshape(masked_lm_log_probs,\n # [-1, masked_lm_log_probs.shape[-1]])\n masked_lm_predictions = tf.argmax(\n masked_lm_log_probs, axis=-1, output_type=tf.int32)\n # values=next_sentence_example_loss)\n\n predictions = {\n \"input_ids\": tf.reshape(input_ids, [-1]),\n \"predictions\": masked_lm_log_probs\n }\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n predictions=predictions)\n #eval_metric_ops=eval_metrics)\n return output_spec\n else:\n def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\n masked_lm_weights):\n \"\"\"Computes the loss and accuracy of the model.\"\"\"\n masked_lm_log_probs = tf.reshape(masked_lm_log_probs,\n [-1, masked_lm_log_probs.shape[-1]])\n masked_lm_predictions = tf.argmax(\n masked_lm_log_probs, axis=-1, output_type=tf.int32)\n masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])\n masked_lm_ids = tf.reshape(masked_lm_ids, [-1])\n masked_lm_weights = tf.reshape(masked_lm_weights, [-1])\n masked_lm_accuracy = tf.metrics.accuracy(\n labels=masked_lm_ids,\n predictions=masked_lm_predictions,\n weights=masked_lm_weights)\n masked_lm_mean_loss = tf.metrics.mean(\n values=masked_lm_example_loss, weights=masked_lm_weights)\n\n return {\n \"masked_lm_accuracy\": masked_lm_accuracy,\n \"masked_lm_loss\": masked_lm_mean_loss,\n }\n\n eval_metrics = metric_fn(\n masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\n masked_lm_weights)\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=total_loss,\n eval_metric_ops=eval_metrics)\n\n return output_spec\n\n return model_fn","def build_model(self, **kwargs):\n raise NotImplementedError()","def _main():\n\n # setup paths\n json_model_path = osp.join(FLAGS.input_dir, FLAGS.json_model_fname)\n weights_path = osp.join(FLAGS.input_dir, FLAGS.weights_fname)\n save_path = osp.splitext(json_model_path)[0][:-6] + \"graph_w\" + str(weights_path.split(\"_\")[-1][:-3]) + \".pb\"\n print(\"Loading Model: \" + json_model_path)\n print(\"Loading Weights: \" + weights_path)\n\n # Set keras to test phase\n k.set_learning_phase(0)\n\n # Load json and weights, then compile model\n with open(json_model_path, 'r') as json_file:\n loaded_model_json = json_file.read()\n model = model_from_json(loaded_model_json)\n model.load_weights(weights_path)\n model.compile(loss='mse', optimizer='sgd')\n\n # Freeze graph\n frozen_graph = freeze_session(k.get_session(), output_names=[out.op.name for out in model.outputs])\n\n # Write graph to protobuf file\n tf.train.write_graph(frozen_graph, \"model\", save_path, as_text=False)\n print(\"Written Graph to: \" + save_path)","def train_model(self, *args, **kwargs):\n raise NotImplementedError","def __init__(self, emotion_model=os.path.join(execution_path,os.path.join(\"models\", \"emotion_model.hdf5\")),\n cascade_file: str = None,\n scale_factor: float = 1.3,\n offsets: tuple = (20, 40),\n compile: bool = False): \n self.__scale_factor = scale_factor\n self.__offsets = offsets\n self.__labels={\n 0: 'angry',\n 1: 'disgust',\n 2: 'fear',\n 3: 'happy',\n 4: 'sad',\n 5: 'surprise',\n 6: 'neutral'\n }\n \n cascade_file=os.path.join(execution_path ,os.path.join(\"models\",\"haarcascade_frontalface_default.xml\")) \n \n self.__face_detector = cv2.CascadeClassifier(cascade_file)\n\n # Local Keras model\n self.deployment = False\n config = tf.ConfigProto(log_device_placement=False)\n config.gpu_options.allow_growth = True\n self.__emotion_classifier = load_model(emotion_model, compile=compile)\n self.__emotion_classifier._make_predict_function()\n self.__emotion_target_size = self.__emotion_classifier.input_shape[\n 1:3]","def model_fn(features, mode, params): # pylint: disable=unused-argument\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"input_type_ids\"]\n # label_ids = features[\"label_ids\"]\n vocab = vocab_list\n vocab_size = len(vocab_list)\n\n is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\n\n\n tvars = tf.trainable_variables()\n initialized_variable_names = {}\n scaffold_fn = None\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n if use_tpu:\n\n def tpu_scaffold():\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n return tf.train.Scaffold()\n\n scaffold_fn = tpu_scaffold\n else:\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n\n\n \n # TRAIN\n if not is_predicting:\n\n (loss, predictions, log_probs) = create_model(\n is_predicting, input_ids, input_mask, segment_ids, vocab, vocab_size, bert_config, use_one_hot_embeddings)\n\n train_op = bert.optimization.create_optimizer(\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=FLAGS.use_tpu)\n\n # if mode == tf.estimator.ModeKeys.TRAIN:\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(mode=mode,\n loss=loss,\n train_op=train_op,\n scaffold_fn=scaffold_fn)\n ## else:\n # return tf.estimator.EstimatorSpec(mode=mode,\n # loss=loss,\n # eval_metric_ops=eval_metrics)\n else:\n (predictions, log_probs) = create_model(\n is_predicting, input_ids, input_mask, segment_ids, vocab, vocab_size, bert_config, use_one_hot_embeddings)\n\n predictions = {\n 'probabilities': log_probs,\n 'predictions': predictions\n }\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(mode, predictions=predictions, scaffold_fn=scaffold_fn)\n\n return output_spec if use_tpu else output_spec.as_estimator_spec()","def model_fn_builder(config):\n def model_fn(features,labels,mode,params):\n \"\"\"The model_fn for Estimator\"\"\"\n input_q = features[\"input_q\"] # query feature vector\n input_K = features[\"input_K\"] # Key set Matrix\n input_v = features[\"input_v\"] # image visual feature vector\n input_labels = features[\"input_labels\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n model = modeling.AMT(\n config = config,\n is_trainging = is_training, \n scope = \"AMT\",\n input_q = input_q,\n input_K = input_K,\n input_v = input_v\n )\n loss = model.loss\n q_doc_rank = model.get_predict()\n output_spec = None\n scaffold_fn = None\n if mode == tf.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer()\n output_spec = tf.estimator.EstimatorSpec(\n mode = mode,\n loss = loss,\n train_op = train_op,\n scaffold_fn = scaffold_fn)\n elif mode == tf.estimator.ModeKeys.EVAL:\n def metric_fn():\n return 0\n else:\n output_spec = tf.estimator.EstimatorSpec(\n mode = mode,\n predictions = q_doc_rank,\n scaffold_fn = scaffold_fn)\n return output_spec\n return model_fn","def __init__(self, dim_input=1, dim_output=1, test_num_updates=5):\n self.dim_input = dim_input\n self.dim_output = dim_output\n self.update_lr = FLAGS.update_lr\n self.meta_lr = tf.placeholder_with_default(FLAGS.meta_lr, ())\n self.auto_lr = tf.placeholder_with_default(FLAGS.auto_lr, ())\n \n self.classification = False\n self.test_num_updates = test_num_updates\n self.dim_auto = 2 #This should be able to be arbitrary\n if auto:\n self.real_input = 39 # This is square root of the total (its a kernel)\n #self.real_output = 40#self.dim_output\n self.real_output = 39*39 # This should be the complete dimension out. \n self.dim_input = 3*self.dim_auto #= 3*self.dim_auto \n self.dim_output = self.dim_auto\n #This is from each. \n #if auto: self.dim_input, self.dim_output = self.dim_auto, self.dim_auto #If auto, pass in/out the dimension of the latent (auto_\n if FLAGS.datasource == 'sinusoid':\n self.dim_hidden = [40, 40,40]\n self.loss_func = mse\n self.forward = self.forward_fc\n self.construct_weights = self.construct_fc_weights\n elif FLAGS.datasource == 'omniglot' or FLAGS.datasource == 'miniimagenet':\n self.loss_func = xent\n self.classification = True\n if FLAGS.conv:\n self.dim_hidden = FLAGS.num_filters\n self.forward = self.forward_conv\n self.construct_weights = self.construct_conv_weights\n else:\n self.dim_hidden = [256, 128, 64, 64]\n self.forward=self.forward_fc\n self.construct_weights = self.construct_fc_weights\n if FLAGS.datasource == 'miniimagenet':\n self.channels = 3\n else:\n self.channels = 1\n self.img_size = int(np.sqrt(self.dim_input/self.channels))\n else:\n raise ValueError('Unrecognized data source.')","def build_model(cls, args, task):\n # make sure that all args are properly defaulted (in case there are any new ones)\n base_architecture(args)\n\n decoder_embed_dict = None\n if args.decoder_embed_path:\n decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)\n utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)\n\n out_channels = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_channels, type=int)\n kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_kernel_sizes, type=int)\n strides = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_strides, type=int)\n logger.info('input feature dimension: {}, channels: {}'.format(task.feat_dim, task.feat_in_channels))\n assert task.feat_dim % task.feat_in_channels == 0\n conv_layers = ConvBNReLU(\n out_channels, kernel_sizes, strides, in_channels=task.feat_in_channels,\n ) if out_channels is not None else None\n\n fconv_encoder_input_size = task.feat_dim // task.feat_in_channels\n if conv_layers is not None:\n for stride in strides:\n if isinstance(stride, (list, tuple)):\n assert len(stride) > 0\n s = stride[1] if len(stride) > 1 else stride[0]\n else:\n assert isinstance(stride, int)\n s = stride\n fconv_encoder_input_size = (fconv_encoder_input_size + s - 1) // s\n fconv_encoder_input_size *= out_channels[-1]\n\n encoder = SpeechFConvEncoder(\n conv_layers_before=conv_layers,\n input_size=fconv_encoder_input_size,\n embed_dim=args.encoder_embed_dim,\n convolutions=eval(args.encoder_layers),\n dropout=args.dropout,\n )\n decoder = SpeechFConvDecoder(\n dictionary=task.target_dictionary,\n embed_dim=args.decoder_embed_dim,\n embed_dict=decoder_embed_dict,\n convolutions=eval(args.decoder_layers),\n out_embed_dim=args.decoder_out_embed_dim,\n attention=eval(args.decoder_attention),\n dropout=args.dropout,\n max_positions=args.max_target_positions,\n share_embed=args.share_input_output_embed,\n positional_embeddings=args.decoder_positional_embed,\n )\n return cls(encoder, decoder)","def create_model(self, model_input, vocab_size, num_frames, **unused_params):\n num_frames_t=num_frames\n num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)\n feature_size = model_input.get_shape().as_list()[2]\n iterations=5#150\n if FLAGS.is_train: \n iterations=120\n model_input = utils.SampleRandomFrames(model_input[:,15:,:], num_frames-15-15,\n iterations)\n # iterations=50\n # model_input=model_input[:,20:-30:5,:]\n model_input=model_input+tf.random_normal(shape=tf.shape(model_input), mean=0.0, stddev=1e-3, dtype=tf.float32)\n\n # print('model_input is', model_input)\n # print('vocab_size is',vocab_size)\n aggregated_model = getattr(video_level_models,\n FLAGS.video_level_classifier_model)\n\n video_attention = AttentionLayers(1024,iterations,256)#256\n audio_attention = AttentionLayers(128,iterations,256/4)#256/4\n\n model_input = slim.batch_norm(\n model_input,\n center=True,\n scale=True,\n is_training=True,\n scope=\"model_input_bn\")\n\n with tf.variable_scope(\"video_Attention\"):\n attention_video = video_attention.forward(model_input[:,:,0:1024]) \n # print('vlad_video is',vlad_video)\n with tf.variable_scope(\"audio_Attention\"):\n attention_audio = audio_attention.forward(model_input[:,:,1024:])\n\n pooled=tf.concat([attention_video,attention_audio],axis=1)\n #instance_att#tf.reduce_mean(pooledi,axis=1)\n\n print('pooled is',pooled)\n\n dr2 = tf.get_variable(\"dr2\",\n [feature_size,1024],\n initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))\n pooled=tf.matmul(pooled,dr2)\n\n pooled = slim.batch_norm(\n pooled,\n center=True,\n scale=True,\n is_training=True,\n scope=\"pooled_bn\")\n\n gating_weights = tf.get_variable(\"gating_weights_2\",\n [1024, 1024],\n initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(1024))) \n gates = tf.matmul(pooled, gating_weights) \n gates = slim.batch_norm(\n gates,\n center=True,\n scale=True,\n is_training=True,\n scope=\"gating_bn\")\n gates = tf.sigmoid(gates)\n pooled = tf.multiply(pooled,gates)\n\n return aggregated_model().create_model(\n model_input=pooled, vocab_size=vocab_size, **unused_params)","def __init__(self, input_tensor_spec):\n self._input_tensor_spec = input_tensor_spec\n super().__init__()","def build_model(cls, args, task):\n # print(\"In build_model !!!\")\n default_architecture(args)\n assert args.load_hf_bert_from != ''\n encoder = HuggingFaceBertEncoder(args, task.dictionary)\n\n return cls(args, encoder, task)","def model_fn_builder():\n \n def model_fn(features, labels, mode, params):\n # features name and shape\n _info('*** Features ****')\n for name in sorted(features.keys()):\n tf.logging.info(' name = {}, shape = {}'.format(name, features[name].shape))\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n # get data\n input_x = features['input_x']\n input_mask = features['input_mask']\n if is_training:\n input_y = features['input_y']\n seq_length = features['seq_length']\n else:\n input_y = None\n seq_length = None\n\n # build encoder\n model = BertEncoder(\n config=cg.BertEncoderConfig,\n is_training=is_training,\n input_ids=input_x,\n input_mask=input_mask)\n embedding_table = model.get_embedding_table()\n encoder_output = tf.reduce_sum(model.get_sequence_output(), axis=1)\n\n # build decoder\n decoder_model = Decoder(\n config=cg.DecoderConfig,\n is_training=is_training,\n encoder_state=encoder_output,\n embedding_table=embedding_table,\n decoder_intput_data=input_y,\n seq_length_decoder_input_data=seq_length)\n logits, sample_id, ppl_seq, ppl = decoder_model.get_decoder_output()\n\n if mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {'sample_id': sample_id, 'ppls': ppl_seq}\n output_spec = tf.estimator.EstimatorSpec(mode, predictions=predictions)\n else:\n if mode == tf.estimator.ModeKeys.TRAIN:\n max_time = ft.get_shape_list(labels, expected_rank=2)[1]\n target_weights = tf.sequence_mask(seq_length, max_time, dtype=logits.dtype)\n batch_size = tf.cast(ft.get_shape_list(labels, expected_rank=2)[0], tf.float32)\n\n loss = tf.reduce_sum(\n tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits) * target_weights) / batch_size\n\n learning_rate = tf.train.polynomial_decay(cg.learning_rate,\n tf.train.get_or_create_global_step(),\n cg.train_steps / 100,\n end_learning_rate=1e-4,\n power=1.0,\n cycle=False)\n\n lr = tf.maximum(tf.constant(cg.lr_limit), learning_rate)\n optimizer = tf.train.AdamOptimizer(lr, name='optimizer')\n tvars = tf.trainable_variables()\n gradients = tf.gradients(loss, tvars, colocate_gradients_with_ops=cg.colocate_gradients_with_ops)\n clipped_gradients, _ = tf.clip_by_global_norm(gradients, 5.0)\n train_op = optimizer.apply_gradients(zip(clipped_gradients, tvars), global_step=tf.train.get_global_step())\n\n\n # this is excellent, because it could display the result each step, i.e., each step equals to batch_size.\n # the output_spec, display the result every save checkpoints step.\n logging_hook = tf.train.LoggingTensorHook({'loss' : loss, 'ppl': ppl, 'lr': lr}, every_n_iter=cg.print_info_interval)\n\n output_spec = tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[logging_hook])\n elif mode == tf.estimator.ModeKeys.EVAL:\n # TODO\n raise NotImplementedError\n \n return output_spec\n \n return model_fn","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n\n bert_config = modeling.BertConfig.from_json_file(bert_config_file)\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n (total_loss, per_example_loss, log_probs) = create_model(\n bert_config, is_training, input_ids, input_mask, segment_ids,\n num_labels, False)\n\n tvars = tf.trainable_variables()\n\n initialized_variable_names = []\n print('****TRYING TO LOAD FROM INIT CHECKPOINT %s****' % init_checkpoint)\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n print(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n print(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n predictions={\n \"log_probs\": log_probs\n })\n\n return output_spec","def main(unused_argv):\n model_params = sketch_rnn_model.get_default_hparams()\n if FLAGS.hparams:\n model_params.parse(FLAGS.hparams)\n trainer(model_params)","def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\n\n tf.logging.info(\"*** Features ***\")\n for name in sorted(features.keys()):\n tf.logging.info(\" name = %s, shape = %s\" % (name, features[name].shape))\n\n unique_ids = features[\"unique_ids\"]\n input_ids = features[\"input_ids\"]\n input_mask = features[\"input_mask\"]\n segment_ids = features[\"segment_ids\"]\n\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\n\n logits = create_model(\n bert_config=bert_config,\n is_training=is_training,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n use_one_hot_embeddings=use_one_hot_embeddings)\n\n tvars = tf.trainable_variables()\n\n initialized_variable_names = {}\n\n if init_checkpoint:\n (assignment_map, initialized_variable_names\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\n\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\n\n tf.logging.info(\"**** Trainable Variables ****\")\n for var in tvars:\n init_string = \"\"\n if var.name in initialized_variable_names:\n init_string = \", *INIT_FROM_CKPT*\"\n tf.logging.info(\" name = %s, shape = %s%s\", var.name, var.shape,\n init_string)\n\n output_spec = None\n if mode == tf.estimator.ModeKeys.PREDICT:\n predictions = {\n \"unique_ids\": unique_ids,\n \"prediction\": tf.argmax(logits, axis=-1),\n }\n output_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\n else:\n def compute_loss(logits, positions):\n one_hot_positions = tf.one_hot(\n positions, depth=3, dtype=tf.float32)\n log_probs = tf.nn.log_softmax(logits, axis=-1)\n loss = -tf.reduce_mean(\n tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\n return loss\n\n label = features[\"label\"]\n\n loss = compute_loss(logits, label)\n predicted_classes = tf.argmax(logits, axis=-1)\n accuracy = tf.metrics.accuracy(labels=label, predictions=predicted_classes, name='acc_op')\n\n # global global_acc_list\n # global_acc_list.append(accuracy)\n if mode == tf.estimator.ModeKeys.TRAIN:\n train_op = optimization.create_optimizer(\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\n\n metrics = {'accuracy': accuracy}\n output_spec = tf.estimator.EstimatorSpec(\n mode=mode,\n loss=loss,\n eval_metric_ops=metrics,\n train_op=train_op)\n elif mode == tf.estimator.ModeKeys.EVAL:\n metrics = {'accuracy': accuracy}\n output_spec = tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)\n else:\n raise ValueError(\n \"Only TRAIN and PREDICT modes are supported: %s\" % (mode))\n\n return output_spec","def create():\n with torch.set_grad_enabled(False):\n model = torch.hub.load(\n \"pytorch/vision:v0.6.0\", \"vgg11\", pretrained=True).eval()\n\n with_cuda = torch.cuda.is_available()\n if with_cuda:\n model.to(\"cuda\")\n else:\n logging.warn(\"Running on CPU, no CUDA detected.\")\n\n def call(features):\n images = features[\"image\"].numpy()\n # Normalize according to the documentation. Note that the pro-processing\n # will already have the range normalized to [0, 1].\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n images_normalized = (images - mean) / std\n # Reshape from [batch, h, w, c] -> [batch, c, h, w]\n images_normalized_bchw = np.transpose(\n images_normalized, [0, 3, 1, 2]).astype(np.float32).copy()\n with torch.no_grad():\n images_torch = torch.from_numpy(images_normalized_bchw)\n if with_cuda:\n images_torch = images_torch.to(\"cuda\")\n logits = model(images_torch)\n return torch.nn.functional.softmax(logits, dim=-1).cpu().numpy()\n\n preprocess_config = \"resize_small(256)|central_crop(224)|value_range(0,1)\"\n preprocess_fn = pipeline_builder.get_preprocess_fn(\n preprocess_config, remove_tpu_dtypes=False)\n return call, preprocess_fn","def __init__(self, pkl_path, joint_type='cocoplus', dtype=tf.float32):\n # -- Load SMPL params --\n with open(pkl_path, 'r') as f:\n dd = pickle.load(f) \n # Mean template vertices\n self.v_template = tf.Variable(\n undo_chumpy(dd['v_template']),\n name='v_template',\n dtype=dtype,\n trainable=False)\n # Size of mesh [Number of vertices, 3]\n self.size = [self.v_template.shape[0].value, 3]\n self.num_betas = dd['shapedirs'].shape[-1]\n # Shape blend shape basis: 6980 x 3 x 10\n # reshaped to 6980*30 x 10, transposed to 10x6980*3\n shapedir = np.reshape(\n undo_chumpy(dd['shapedirs']), [-1, self.num_betas]).T\n self.shapedirs = tf.Variable(\n shapedir, name='shapedirs', dtype=dtype, trainable=False)\n\n # Regressor for joint locations given shape - 6890 x 24\n self.J_regressor = tf.Variable(\n dd['J_regressor'].T.todense(),\n name=\"J_regressor\",\n dtype=dtype,\n trainable=False)\n\n # Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*30 x 207\n num_pose_basis = dd['posedirs'].shape[-1]\n # 207 x 20670\n posedirs = np.reshape(\n undo_chumpy(dd['posedirs']), [-1, num_pose_basis]).T\n self.posedirs = tf.Variable(\n posedirs, name='posedirs', dtype=dtype, trainable=False)\n\n # indices of parents for each joints\n self.parents = dd['kintree_table'][0].astype(np.int32)\n\n # LBS weights\n self.weights = tf.Variable(\n undo_chumpy(dd['weights']),\n name='lbs_weights',\n dtype=dtype,\n trainable=False)\n\n # This returns 19 keypoints: 6890 x 19\n self.joint_regressor = tf.Variable(\n dd['cocoplus_regressor'].T.todense(),\n name=\"cocoplus_regressor\",\n dtype=dtype,\n trainable=False)\n if joint_type == 'lsp': # 14 LSP joints!\n self.joint_regressor = self.joint_regressor[:, :14]\n\n if joint_type not in ['cocoplus', 'lsp']:\n print('BAD!! Unknown joint type: %s, it must be either \"cocoplus\" or \"lsp\"' % joint_type)\n import ipdb\n ipdb.set_trace()","def create_model(model_name, random_state, epoch, device, log_path, **hparams):\n model = eval(f'{model_name}')(\n **hparams, epoch=int(epoch), random_state=random_state, device=device,\n log_path=log_path\n )\n\n return model","def init_tensors(self, sample, *args):\n raise NotImplementedError"],"string":"[\n \"def create_model():\\r\\n parser = argparse.ArgumentParser()\\r\\n parser.add_argument('--DISC_LR', type=float, default=1e-4)\\r\\n parser.add_argument('--GEN_LR', type=float, default=1e-3)\\r\\n parser.add_argument('--GEN_BETA1', type=float, default=0.9)\\r\\n parser.add_argument('--GEN_BETA2', type=float, default=0.999)\\r\\n parser.add_argument('--IMAGE_SIZE', type=int, default=None)\\r\\n parser.add_argument('--QUES_SIZE', type=int, default=None)\\r\\n parser.add_argument('--QUES_EMBED_SIZE', type=int, default=2048)\\r\\n parser.add_argument('--WORD_EMBED_SIZE', type=int, default=512)\\r\\n parser.add_argument('--VOCAB_SIZE', type=int, default=1004)\\r\\n args, task_args = parser.parse_known_args()\\r\\n override_if_not_in_args('--max_steps', '1000', task_args)\\r\\n override_if_not_in_args('--batch_size', '64', task_args)\\r\\n override_if_not_in_args('--eval_set_size', '370', task_args)\\r\\n override_if_not_in_args('--eval_interval_secs', '2', task_args)\\r\\n override_if_not_in_args('--log_interval_secs', '2', task_args)\\r\\n override_if_not_in_args('--min_train_eval_rate', '2', task_args)\\r\\n\\r\\n return Model(args.DISC_LR, args.GEN_LR, args.GEN_BETA1, args.GEN_BETA2,\\r\\n args.IMAGE_SIZE, args.QUES_EMBED_SIZE, args.WORD_EMBED_SIZE,\\r\\n args.QUES_SIZE, args.VOCAB_SIZE), task_args\",\n \"def _make_model_v1():\\n tf.reset_default_graph()\\n\\n with tf.Session() as sess:\\n # Make two simple graphs, both of which will be served by TF\\n x = tf.placeholder('float', shape=(None, 3), name='Input')\\n z = tf.placeholder('float', shape=(), name='ScalarMultiple')\\n m = tf.Variable([1.0, 1.0, 1.0], name='Slopes')\\n y = m * x + 1\\n len_fun = tf.reduce_sum(y - x) # Returns the number of elements in the array\\n scale_mult = tf.multiply(z, x, name='scale_mult')\\n\\n # Initialize the variables\\n init = tf.global_variables_initializer()\\n sess.run(init)\\n\\n # Create the tool for saving the model to disk\\n builder = tf.saved_model.builder.SavedModelBuilder(tf_export_path)\\n\\n # Make descriptions for the inputs and outputs\\n x_desc = tf.saved_model.utils.build_tensor_info(x)\\n y_desc = tf.saved_model.utils.build_tensor_info(y)\\n z_desc = tf.saved_model.utils.build_tensor_info(z)\\n len_fun_desc = tf.saved_model.utils.build_tensor_info(len_fun)\\n scale_mult_desc = tf.saved_model.utils.build_tensor_info(scale_mult)\\n\\n # Make a signature for the functions to be served\\n func_sig = tf.saved_model.signature_def_utils.build_signature_def(\\n inputs={'x': x_desc},\\n outputs={'y': y_desc},\\n method_name='run'\\n )\\n len_sig = tf.saved_model.signature_def_utils.build_signature_def(\\n inputs={'x': x_desc},\\n outputs={'len': len_fun_desc},\\n method_name='length'\\n )\\n mult_sig = tf.saved_model.signature_def_utils.build_signature_def(\\n inputs={'x': x_desc, 'z': z_desc},\\n outputs={'scale_mult': scale_mult_desc},\\n method_name='scalar_multiply'\\n )\\n\\n # Add the functions and the state of the graph to the builder\\n builder.add_meta_graph_and_variables(\\n sess, [tf.saved_model.tag_constants.SERVING],\\n signature_def_map={\\n tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: func_sig,\\n 'length': len_sig,\\n 'scalar_multiply': mult_sig\\n })\\n\\n # Save the function\\n builder.save()\",\n \"def build_model(cls, args):\\n base_architecture(args) \\n return StyleGANGeneratorPretrain(args)\",\n \"def cli(sys_argv: List[str]):\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('--model_definition', type=str,\\n help='Path to json model definition')\\n\\n parser.add_argument('--model_state_path', type=str,\\n help='Path where to the trained parameters')\\n\\n parser.add_argument('--data_path', type=str, default=TEST_PATH,\\n help='path to the pickled dataframe on which prediction should be made')\\n\\n parser.add_argument('--numerical_preprocessor', type=str, default=NUMERICAL_PREPROCESSOR_SAVE_PATH,\\n help='Path of the saved numerical preprocessor')\\n\\n parser.add_argument('--categorical_preprocessor', type=str, default=CATEGORICAL_PREPROCESSOR_SAVE_PATH,\\n help='Path to the saved categorical preprocessor')\\n\\n parser.add_argument('--output_directory', type=str, default=RESULTS_DIR,\\n help='Path where to save the prediction of the experiment')\\n\\n args = parser.parse_args(sys_argv)\\n\\n # # ---------- parse config file ---------- # #\\n config: dict = json.load(open(args.model_definition, 'r'))\\n\\n model_class: str = config['model_class']\\n model_name: str = config['model_name']\\n numerical_input_features: List[str] = config['data']['numerical_input_features']\\n categorical_input_features: List[str] = config['data']['categorical_input_features']\\n output_features: List[str] = config['data']['output_features']\\n batch_size_test: int = config['data']['batch_size_test']\\n\\n device = torch.device(CUDA if torch.cuda.is_available() else CPU)\\n\\n # # ---------- parse model state ---------- # #\\n model_state = load_model_state(args.model_state_path, device)\\n\\n model_hyperparameters: dict = model_state['hyperparameters']\\n model_hyperparameters.update(config['model'])\\n model_hyperparameters['device']: torch.device = device\\n model_weights: dict = model_state['best_model_state_dict']\\n\\n # # ---------- initialize model ---------- # #\\n model = REGISTERED_MODELS[model_class](**model_hyperparameters).to(device)\\n model.load(model_weights)\\n\\n # # ---------- preprocess data for inference ---------- # #\\n test_loader = preprocess_for_inference(\\n args.data_path,\\n numerical_input_features,\\n categorical_input_features,\\n output_features,\\n args.numerical_preprocessor,\\n args.categorical_preprocessor,\\n batch_size_test=batch_size_test\\n )\\n\\n # # ---------- compute and save predictions ---------- # #\\n predictions = model.predict(test_loader)\\n\\n # save predictions\\n data_file_name = os.path.basename(args.data_path)\\n data_file_name = os.path.splitext(data_file_name)[0] # remove extension\\n model_path = '{}/predictions_{}_{}.pickle'.format(args.output_directory, model_name, data_file_name)\\n print(' [predict] Saving predictions at: `{}`'.format(model_path))\\n file_utils.save_to_pickle(\\n predictions,\\n path=model_path\\n )\\n print(' [predict] Done')\",\n \"def build_model_fn(self):\",\n \"def _main(args):\\n # model = keras.models.load_model(args.h5_file, custom_objects={'prelu': prelu})\\n with tf.keras.utils.custom_object_scope({'prelu': prelu}):\\n converter = tf.lite.TFLiteConverter.from_keras_model_file(args.h5_file)\\n tflite_file = converter.convert()\\n open(args.tflite_file, 'wb').write(tflite_file)\\n print('='*30)\\n print('tffile file save in {}.'.format(args.tflite_file))\",\n \"def model_creator(config):\\n return nn.Linear(1, 1)\",\n \"def model_create(ARGS):\\n\\n def retain(ARGS):\\n \\\"\\\"\\\"Create the model\\\"\\\"\\\"\\n\\n # Define the constant for model saving\\n reshape_size = ARGS.emb_size + ARGS.numeric_size\\n if ARGS.allow_negative:\\n embeddings_constraint = FreezePadding()\\n beta_activation = 'tanh'\\n output_constraint = None\\n else:\\n embeddings_constraint = FreezePadding_Non_Negative()\\n beta_activation = 'sigmoid'\\n output_constraint = non_neg()\\n\\n def reshape(data):\\n \\\"\\\"\\\"Reshape the context vectors to 3D vector\\\"\\\"\\\"\\n return K.reshape(x=data, shape=(K.shape(data)[0], 1, reshape_size))\\n\\n # Code Input\\n codes = L.Input((None, None), name='codes_input')\\n inputs_list = [codes]\\n # Calculate embedding for each code and sum them to a visit level\\n codes_embs_total = L.Embedding(ARGS.num_codes + 1,\\n ARGS.emb_size,\\n name='embedding'\\n # BUG: embeddings_constraint not supported\\n # https://github.com/tensorflow/tensorflow/issues/33755\\n # ,embeddings_constraint=embeddings_constraint\\n )(codes)\\n codes_embs = L.Lambda(lambda x: K.sum(x, axis=2))(codes_embs_total)\\n # Numeric input if needed\\n if ARGS.numeric_size > 0:\\n numerics = L.Input((None, ARGS.numeric_size), name='numeric_input')\\n inputs_list.append(numerics)\\n full_embs = L.concatenate([codes_embs, numerics], name='catInp')\\n else:\\n full_embs = codes_embs\\n\\n # Apply dropout on inputs\\n full_embs = L.Dropout(ARGS.dropout_input)(full_embs)\\n\\n # Time input if needed\\n if ARGS.use_time:\\n time = L.Input((None, 1), name='time_input')\\n inputs_list.append(time)\\n time_embs = L.concatenate([full_embs, time], name='catInp2')\\n else:\\n time_embs = full_embs\\n\\n # Setup Layers\\n # This implementation uses Bidirectional LSTM instead of reverse order\\n # (see https://github.com/mp2893/retain/issues/3 for more details)\\n\\n # If training on GPU and Tensorflow use CuDNNLSTM for much faster training\\n if glist:\\n alpha = L.Bidirectional(L.CuDNNLSTM(ARGS.recurrent_size, return_sequences=True),\\n name='alpha')\\n beta = L.Bidirectional(L.CuDNNLSTM(ARGS.recurrent_size, return_sequences=True),\\n name='beta')\\n else:\\n alpha = L.Bidirectional(L.LSTM(ARGS.recurrent_size,\\n return_sequences=True, implementation=2),\\n name='alpha')\\n beta = L.Bidirectional(L.LSTM(ARGS.recurrent_size,\\n return_sequences=True, implementation=2),\\n name='beta')\\n\\n alpha_dense = L.Dense(1, kernel_regularizer=l2(ARGS.l2))\\n beta_dense = L.Dense(ARGS.emb_size + ARGS.numeric_size,\\n activation=beta_activation, kernel_regularizer=l2(ARGS.l2))\\n\\n # Compute alpha, visit attention\\n alpha_out = alpha(time_embs)\\n alpha_out = L.TimeDistributed(alpha_dense, name='alpha_dense_0')(alpha_out)\\n alpha_out = L.Softmax(axis=1)(alpha_out)\\n # Compute beta, codes attention\\n beta_out = beta(time_embs)\\n beta_out = L.TimeDistributed(beta_dense, name='beta_dense_0')(beta_out)\\n # Compute context vector based on attentions and embeddings\\n c_t = L.Multiply()([alpha_out, beta_out, full_embs])\\n c_t = L.Lambda(lambda x: K.sum(x, axis=1))(c_t)\\n # Reshape to 3d vector for consistency between Many to Many and Many to One implementations\\n contexts = L.Lambda(reshape)(c_t)\\n\\n # Make a prediction\\n contexts = L.Dropout(ARGS.dropout_context)(contexts)\\n output_layer = L.Dense(1, activation='sigmoid', name='dOut',\\n kernel_regularizer=l2(ARGS.l2), kernel_constraint=output_constraint)\\n\\n # TimeDistributed is used for consistency\\n # between Many to Many and Many to One implementations\\n output = L.TimeDistributed(output_layer, name='time_distributed_out')(contexts)\\n # Define the model with appropriate inputs\\n model = Model(inputs=inputs_list, outputs=[output])\\n\\n return model\\n\\n # Set Tensorflow to grow GPU memory consumption instead of grabbing all of it at once\\n # If there are multiple GPUs set up a multi-gpu model\\n # Get available gpus , returns empty list if none\\n # glist = get_available_gpus()\\n glist = []\\n if len(glist) > 1:\\n with tf.device('/cpu:0'):\\n model = retain(ARGS)\\n model_final = make_parallel(model, glist)\\n else:\\n model_final = retain(ARGS)\\n\\n # Compile the model - adamax has produced best results in our experiments\\n model_final.compile(optimizer='adamax',\\n loss='binary_crossentropy',\\n #TODO: add AUPRC?\\n metrics=[Recall(), specificity,\\n SpecificityAtSensitivity(0.5,3),\\n SensitivityAtSpecificity(0.5, 3),\\n 'accuracy', AUC(), Precision()],\\n sample_weight_mode=\\\"temporal\\\")\\n return model_final\",\n \"def create_model():\\n\\n class Net(nn.Cell):\\n def construct(self, x, y):\\n return x\\n\\n net = Net()\\n model_simple = Model(net)\\n\\n return model_simple\",\n \"def create_model(self, input_shape, num_actions, mode, args, model_name='q_network'):\\n assert (mode in (\\\"linear\\\", \\\"duel\\\", \\\"dqn\\\"))\\n with tf.variable_scope(model_name):\\n input_data = Input(shape=input_shape, name=\\\"input\\\")\\n if mode == \\\"linear\\\":\\n # #version 4 elu:\\n # flatten_hidden = Flatten(name=\\\"flatten\\\")(input_data)\\n # FC_1 = Dense(512, activation='elu', name='FC1-elu')(flatten_hidden)\\n # FC_2 = Dense(512, activation='elu', name='FC2-elu')(FC_1)\\n # FC_3 = Dense(512, activation='elu', name='FC3-elu')(FC_2)\\n # FC_4 = Dense(512, activation='elu', name='FC4-elu')(FC_3)\\n # output = Dense(num_actions, activation='elu', name=\\\"output\\\")(FC_4)\\n\\n #version 4 elu:\\n flatten_hidden = Flatten(name=\\\"flatten\\\")(input_data)\\n FC_1 = Dense(1024, activation='elu', name='FC1-elu')(flatten_hidden)\\n FC_2 = Dense(1024, activation='elu', name='FC2-elu')(FC_1)\\n FC_3 = Dense(1024, activation='elu', name='FC3-elu')(FC_2)\\n FC_4 = Dense(512, activation='elu', name='FC4-elu')(FC_3)\\n output = Dense(num_actions, activation='elu', name=\\\"output\\\")(FC_4)\\n\\n else:\\n if not (args.recurrent):\\n # # # version 1:\\n # h1 = Convolution2D(32, (8, 8), strides=4, activation=\\\"relu\\\", name=\\\"conv1\\\")(input_data)\\n # h2 = Convolution2D(64, (4, 4), strides=2, activation=\\\"relu\\\", name=\\\"conv2\\\")(h1)\\n # h3 = Convolution2D(64, (3, 3), strides=1, activation=\\\"relu\\\", name=\\\"conv3\\\")(h2)\\n # context = Flatten(name=\\\"flatten\\\")(h3)\\n\\n # # version 2:\\n # conv1 = Convolution2D(1, (5, 5), strides=1, activation=\\\"elu\\\", name=\\\"conv1\\\")(input_data)\\n # flatten = Flatten(name=\\\"flatten\\\")(conv1)\\n # FC_2 = Dense(512, activation='elu', name='FC2-elu')(flatten)\\n # context = Dense(512, activation='elu', name='FC4-elu')(FC_2)\\n\\n # version 3:\\n conv1 = Convolution2D(32, (2, 2), strides=1, activation=\\\"relu\\\", name=\\\"conv1\\\")(input_data)\\n flatten = Flatten(name=\\\"flatten\\\")(conv1)\\n FC_2 = Dense(128, activation='relu', name='FC2-relu')(flatten)\\n FC_3 = Dense(128, activation='relu', name='FC3-relu')(FC_2)\\n context = Dense(128, activation='elu', name='FC4-elu')(FC_3)\\n\\n\\n\\n # else:\\n # print('>>>> Defining Recurrent Modules...')\\n # input_data_expanded = Reshape((input_shape[0], input_shape[1], input_shape[2], 1),\\n # input_shape=input_shape)(input_data)\\n # input_data_TimeDistributed = Permute((3, 1, 2, 4), input_shape=input_shape)(input_data_expanded)\\n # h1 = TimeDistributed(Convolution2D(32, (8, 8), strides=4, activation=\\\"relu\\\", name=\\\"conv1\\\"), \\\\\\n # input_shape=(args.num_frames, input_shape[0], input_shape[1], 1))(\\n # input_data_TimeDistributed)\\n # h2 = TimeDistributed(Convolution2D(64, (4, 4), strides=2, activation=\\\"relu\\\", name=\\\"conv2\\\"))(h1)\\n # h3 = TimeDistributed(Convolution2D(64, (2, 2), strides=1, activation=\\\"relu\\\", name=\\\"conv3\\\"))(h2)\\n # flatten_hidden = TimeDistributed(Flatten())(h3)\\n # hidden_input = TimeDistributed(Dense(512, activation='relu', name='flat_to_512'))(flatten_hidden)\\n # if not (args.a_t):\\n # context = LSTM(512, return_sequences=False, stateful=False, input_shape=(args.num_frames, 512))(\\n # hidden_input)\\n # else:\\n # if args.bidir:\\n # hidden_input = Bidirectional(\\n # LSTM(512, return_sequences=True, stateful=False, input_shape=(args.num_frames, 512)),\\n # merge_mode='sum')(hidden_input)\\n # all_outs = Bidirectional(\\n # LSTM(512, return_sequences=True, stateful=False, input_shape=(args.num_frames, 512)),\\n # merge_mode='sum')(hidden_input)\\n # else:\\n # all_outs = LSTM(512, return_sequences=True, stateful=False,\\n # input_shape=(args.num_frames, 512))(hidden_input)\\n # # attention\\n # attention = TimeDistributed(Dense(1, activation='tanh'))(all_outs)\\n # # print(attention.shape)\\n # attention = Flatten()(attention)\\n # attention = Activation('softmax')(attention)\\n # attention = RepeatVector(512)(attention)\\n # attention = Permute([2, 1])(attention)\\n # sent_representation = merge([all_outs, attention], mode='mul')\\n # context = Lambda(lambda xin: K.sum(xin, axis=-2), output_shape=(512,))(sent_representation)\\n # # print(context.shape)\\n\\n if mode == \\\"dqn\\\":\\n h4 = Dense(512, activation='elu', name=\\\"fc\\\")(context)\\n output = Dense(num_actions, name=\\\"output\\\")(h4)\\n # elif mode == \\\"duel\\\":\\n # value_hidden = Dense(512, activation='relu', name='value_fc')(context)\\n # value = Dense(1, name=\\\"value\\\")(value_hidden)\\n # action_hidden = Dense(512, activation='relu', name='action_fc')(context)\\n # action = Dense(num_actions, name=\\\"action\\\")(action_hidden)\\n # action_mean = Lambda(lambda x: tf.reduce_mean(x, axis=1, keep_dims=True), name='action_mean')(\\n # action)\\n # output = Lambda(lambda x: x[0] + x[1] - x[2], name='output')([action, value, action_mean])\\n model = Model(inputs=input_data, outputs=output)\\n print(model.summary())\\n return model\",\n \"def __init__(self, model_name: str, label_file: str) -> None:\\n\\n # Append TFLITE extension to model_name if there's no extension\\n _, ext = os.path.splitext(model_name)\\n if not ext:\\n model_name += '.tflite'\\n\\n # Initialize the TFLite model.\\n interpreter = Interpreter(model_path=model_name, num_threads=4)\\n interpreter.allocate_tensors()\\n\\n self._input_index = interpreter.get_input_details()[0]['index']\\n self._output_index = interpreter.get_output_details()[0]['index']\\n self._interpreter = interpreter\\n\\n self.pose_class_names = self._load_labels(label_file)\",\n \"def create_model(self, **inputs):\\n raise NotImplementedError('This method has to be overwritten.')\",\n \"def evaluate(args):\\n dataset_param_filepath = os.path.join(args.model, 'dataset.params')\\n dataset_params = putils.load_params(dataset_param_filepath)\\n source_vocab_filepath = os.path.join(args.model, 'source.vocab')\\n source_vocab = Vocab(vocab_filepath=source_vocab_filepath)\\n target_vocab_filepath = os.path.join(args.model, 'target.vocab')\\n target_vocab = Vocab(vocab_filepath=target_vocab_filepath)\\n model_params_filepath = os.path.join(args.model, 'model.params')\\n model_params = putils.load_params(model_params_filepath)\\n checkpoint_filepath = os.path.join(args.model, 'checkpoint.tar')\\n if not torch.cuda.is_available() and model_params['cuda']:\\n logger.info('Loading a GPU-trained model on CPU')\\n checkpoint = torch.load(checkpoint_filepath,\\n map_location=const.DEVICE)\\n elif torch.cuda.is_available() and model_params['cuda']:\\n logger.info('Loading a GPU-trained model on GPU')\\n checkpoint = torch.load(checkpoint_filepath)\\n elif torch.cuda.is_available() and not model_params['cuda']:\\n logger.info('Loading a CPU-trained model on GPU')\\n checkpoint = torch.load(checkpoint_filepath,\\n map_location='cuda:0')\\n else:\\n logger.info('Loading a CPU-trained model on CPU')\\n checkpoint = torch.load(checkpoint_filepath)\\n encoder = Encoder(model_type=checkpoint['encoder']['model_type'],\\n input_size=checkpoint['encoder']['input_size'],\\n hidden_size=checkpoint['encoder']['hidden_size'],\\n num_layers=checkpoint['encoder']['num_layers'],\\n nonlinearity=checkpoint['encoder']['nonlinearity'],\\n bias=checkpoint['encoder']['bias'],\\n dropout=checkpoint['encoder']['dropout'],\\n bidirectional=checkpoint['encoder']['bidirectional'])\\n if checkpoint['with_attention']:\\n decoder = Attention(model_type=checkpoint['decoder']['model_type'],\\n hidden_size=checkpoint['decoder']['hidden_size'],\\n output_size=checkpoint['decoder']['output_size'],\\n max_seq_len=dataset_params['max_seq_len'],\\n num_layers=checkpoint['decoder']['num_layers'],\\n nonlinearity=checkpoint['decoder']['nonlinearity'],\\n bias=checkpoint['decoder']['bias'],\\n dropout=checkpoint['decoder']['dropout'],\\n bidirectional=checkpoint['decoder']['bidirectional'])\\n else:\\n decoder = Decoder(model_type=checkpoint['decoder']['model_type'],\\n hidden_size=checkpoint['decoder']['hidden_size'],\\n output_size=checkpoint['decoder']['output_size'],\\n num_layers=checkpoint['decoder']['num_layers'],\\n nonlinearity=checkpoint['decoder']['nonlinearity'],\\n bias=checkpoint['decoder']['bias'],\\n dropout=checkpoint['decoder']['dropout'],\\n bidirectional=checkpoint['decoder']['bidirectional'])\\n encoder.load_state_dict(checkpoint['encoder_state_dict'])\\n decoder.load_state_dict(checkpoint['decoder_state_dict'])\\n if torch.cuda.is_available():\\n encoder.to(const.DEVICE)\\n decoder.to(const.DEVICE)\\n encoder.eval()\\n decoder.eval()\\n indexes = putils.index_dataset(\\n args.data, source_vocab.item2idx, target_vocab.item2idx,\\n dataset_params['is_character_based'], dataset_params['max_seq_len'],\\n dataset_params['is_reversed'])\\n if args.random > 0:\\n random.shuffle(indexes)\\n for seq_num in range(args.random):\\n seq = indexes[seq_num]\\n print('-'*80)\\n print('>', ' '.join([source_vocab.idx2item[idx]\\n for idx in seq[0]]))\\n print('=', ' '.join([target_vocab.idx2item[idx]\\n for idx in seq[1]]))\\n # TODO: add support for OOV\\n predicted_idx, _ = _decode(seq[0], encoder, decoder,\\n checkpoint['with_attention'],\\n dataset_params['max_seq_len'])\\n print('<', ' '.join([target_vocab.idx2item[idx]\\n for idx in predicted_idx]))\\n else:\\n _evaluate(indexes, encoder, decoder, target_vocab, checkpoint,\\n dataset_params)\",\n \"def _make_model_v2():\\n class CustomModule(tf.Module):\\n\\n def __init__(self):\\n super().__init__()\\n self.m = tf.Variable([1.0, 1.0, 1.0], name='slope')\\n\\n @tf.function\\n def __call__(self, x):\\n y = self.m * x + 1\\n return y\\n\\n @tf.function(input_signature=[tf.TensorSpec((None, 3), tf.float32)])\\n def length(self, x):\\n return tf.reduce_sum(self(x) - x, name='length')\\n\\n @tf.function(input_signature=[tf.TensorSpec([], tf.float32),\\n tf.TensorSpec((None, 3), tf.float32)])\\n def scalar_multiply(self, z, x):\\n return tf.multiply(z, x, name='scale_mult')\\n\\n module = CustomModule()\\n\\n # Make a concrete version of __call__\\n call = module.__call__.get_concrete_function(tf.TensorSpec((None, 3)))\\n\\n tf.saved_model.save(\\n module, tf_export_path, signatures={\\n tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY: call,\\n 'length': module.length,\\n 'scalar_multiply': module.scalar_multiply\\n }\\n )\",\n \"def create_model(args, pretrained_embedding: np.ndarray, model_name='deep_q_network', trainable=True):\\n\\n state = Input(shape=(args.n_features,))\\n model = None\\n\\n n, m = pretrained_embedding.shape\\n print('shape', pretrained_embedding.shape)\\n embedded = Embedding(n, m, embeddings_initializer=keras.initializers.constant(pretrained_embedding))(state)\\n\\n if model_name == \\\"deep_q_network\\\":\\n print(\\\"Building \\\" + model_name + \\\" ...\\\")\\n\\n # First convolutional layer\\n\\n x = Dense(args.hidden_size, activation=K.relu)(embedded)\\n x = Dropout(args.dropout)(x)\\n x = Flatten()(x)\\n y_pred = Dense(args.n_actions, trainable=trainable)(x)\\n\\n model = Model(inputs=state, outputs=y_pred)\\n\\n elif model_name == \\\"deep_q_network_double\\\":\\n print(\\\"Building \\\" + model_name + \\\" ...\\\")\\n\\n x = Dense(args.hidden_size, activation=K.relu)(embedded)\\n x = Dropout(args.dropout)(x)\\n x = Flatten()(x)\\n y_pred = Dense(args.n_actions, trainable=trainable)(x)\\n\\n model = Model(input=state, output=y_pred)\\n\\n elif model_name == \\\"deep_q_network_duel\\\":\\n print(\\\"Building \\\" + model_name + \\\" ...\\\")\\n\\n x = Dense(args.hidden_size, activation=K.relu)(embedded)\\n x = Dropout(args.dropout)(x)\\n x = Flatten()(x)\\n\\n y_pred = Dense(args.n_actions, trainable=trainable)(x)\\n\\n # value output\\n x_val = Dense(args.hidden_size, trainable=trainable)(x)\\n # x_val = Activation('relu')(x_val)\\n y_val = Dense(1, trainable=trainable)(x_val)\\n\\n # advantage output\\n x_advantage = Dense(args.hidden_size, trainable=trainable)(x)\\n # x_advantage = Activation('relu')(x_advantage)\\n y_advantage = Dense(args.n_actions, trainable=trainable)(x_advantage)\\n # mean advantage\\n y_advantage_mean = Lambda(lambda x: K.mean(x, axis=1, keepdims=True))(y_advantage)\\n\\n y_q = Lambda(lambda x: x[0] + x[1] - x[2])([y_val, y_advantage, y_advantage_mean])\\n\\n model = Model(input=state, output=y_q)\\n\\n else:\\n print(\\\"Model not supported\\\")\\n exit(1)\\n\\n return model\",\n \"def build_model():\",\n \"def tensor(*args, **kwargs):\\n return Tensor(*args, **kwargs)\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n '-v', '--verbose', action='store_true',\\n help='enable verbose output (for debugging)')\\n parser.add_argument(\\n '-c', '--category_num', type=int,\\n help='number of object categories (obsolete)')\\n parser.add_argument(\\n '-m', '--model', type=str, required=True,\\n help=('[yolov3-tiny|yolov3|yolov3-spp|yolov4-tiny|yolov4|'\\n 'yolov4-csp|yolov4x-mish|yolov4-p5]-[{dimension}], where '\\n '{dimension} could be either a single number (e.g. '\\n '288, 416, 608) or 2 numbers, WxH (e.g. 416x256)'))\\n parser.add_argument(\\n '--int8', action='store_true',\\n help='build INT8 TensorRT engine')\\n parser.add_argument(\\n '--dla_core', type=int, default=-1,\\n help='id of DLA core for inference (0 ~ N-1)')\\n args = parser.parse_args()\\n\\n engine = build_engine(\\n args.model, args.int8, args.dla_core, args.verbose)\\n if engine is None:\\n raise SystemExit('ERROR: failed to build the TensorRT engine!')\\n\\n engine_path = '%s.trt' % args.model\\n with open(engine_path, 'wb') as f:\\n f.write(engine.serialize())\\n print('Serialized the TensorRT engine to file: %s' % engine_path)\",\n \"def build(self, input_tensors, is_training, lengths=None, hparams=None):\",\n \"def __init__(\\n self,\\n train_X: Tensor,\\n train_Y: Tensor,\\n train_Yvar: Optional[Tensor],\\n task_feature: int,\\n output_tasks: Optional[List[int]] = None,\\n rank: Optional[int] = None,\\n outcome_transform: Optional[OutcomeTransform] = None,\\n input_transform: Optional[InputTransform] = None,\\n pyro_model: Optional[PyroModel] = None,\\n ) -> None:\\n if not (\\n train_X.ndim == train_Y.ndim == 2\\n and len(train_X) == len(train_Y)\\n and train_Y.shape[-1] == 1\\n ):\\n raise ValueError(\\n \\\"Expected train_X to have shape n x d and train_Y to have shape n x 1\\\"\\n )\\n if train_Yvar is not None and train_Y.shape != train_Yvar.shape:\\n raise ValueError(\\n \\\"Expected train_Yvar to be None or have the same shape as train_Y\\\"\\n )\\n with torch.no_grad():\\n transformed_X = self.transform_inputs(\\n X=train_X, input_transform=input_transform\\n )\\n if outcome_transform is not None:\\n train_Y, train_Yvar = outcome_transform(train_Y, train_Yvar)\\n if train_Yvar is not None: # Clamp after transforming\\n train_Yvar = train_Yvar.clamp(MIN_INFERRED_NOISE_LEVEL)\\n\\n super().__init__(\\n train_X=train_X,\\n train_Y=train_Y,\\n train_Yvar=train_Yvar,\\n task_feature=task_feature,\\n output_tasks=output_tasks,\\n )\\n self.to(train_X)\\n\\n self.mean_module = None\\n self.covar_module = None\\n self.likelihood = None\\n self.task_covar_module = None\\n self.register_buffer(\\\"latent_features\\\", None)\\n if pyro_model is None:\\n pyro_model = MultitaskSaasPyroModel()\\n pyro_model.set_inputs(\\n train_X=transformed_X,\\n train_Y=train_Y,\\n train_Yvar=train_Yvar,\\n task_feature=task_feature,\\n task_rank=rank,\\n )\\n self.pyro_model = pyro_model\\n if outcome_transform is not None:\\n self.outcome_transform = outcome_transform\\n if input_transform is not None:\\n self.input_transform = input_transform\",\n \"def create_ars_model(odim=10,adim=2,hdims=[128],\\n actv=tf.nn.relu,out_actv=tf.nn.tanh):\\n import tensorflow as tf\\n \\n def mlp(x,hdims=[256,256],actv=tf.nn.relu,out_actv=tf.nn.relu):\\n ki = tf.truncated_normal_initializer(stddev=0.1)\\n for hdim in hdims[:-1]:\\n x = tf.layers.dense(x,units=hdim,activation=actv,kernel_initializer=ki)\\n return tf.layers.dense(x,units=hdims[-1],\\n activation=out_actv,kernel_initializer=ki)\\n def placeholder(dim=None):\\n return tf.placeholder(dtype=tf.float32,shape=(None,dim) if dim else (None,))\\n def placeholders(*args):\\n \\\"\\\"\\\"\\n Usage: a_ph,b_ph,c_ph = placeholders(adim,bdim,None)\\n \\\"\\\"\\\"\\n return [placeholder(dim) for dim in args]\\n def get_vars(scope):\\n return [x for x in tf.compat.v1.global_variables() if scope in x.name]\\n \\n # Have own session\\n config = tf.ConfigProto()\\n config.gpu_options.allow_growth = True\\n sess = tf.Session(config=config)\\n \\n # Placeholders\\n o_ph = placeholder(odim)\\n \\n # Policy \\n with tf.variable_scope('main'):\\n mu = mlp(o_ph,hdims=hdims+[adim],actv=actv,out_actv=out_actv)\\n \\n # Params\\n main_vars = get_vars('main')\\n \\n model = {'o_ph':o_ph,'mu':mu,'main_vars':main_vars}\\n return model, sess\",\n \"def __init__(self, model_path, alpha):\\n\\n with tf.keras.utils.CustomObjectScope({'tf': tf}):\\n self.base_model = K.models.load_model(model_path)\\n self.base_model.save(base_model)\\n\\n A_input = tf.placeholder(tf.float32, (None, 96, 96, 3))\\n P_input = tf.placeholder(tf.float32, (None, 96, 96, 3))\\n N_input = tf.placeholder(tf.float32, (None, 96, 96, 3))\\n inputs = [A_inputs, P_inputs, N_inputs]\\n outputs_embedding = self.base_model(inputs)\\n \\\"\\\"\\\"\\n P = self.base_model(P_input)\\n N = self.base_model(N_input)\\n \\\"\\\"\\\"\\n tl = TripletLoss(alpha)\\n output = tl(outputs_embedding)\\n\\n training_model = K.models.Model(inputs, output)\\n training_model.compile(optimizer='Adam')\\n training_model.save('training_model')\",\n \"def build_model(cls, args, task):\\n\\n # make sure all arguments are present in older models\\n base_lm_architecture(args)\\n\\n if args.decoder_layers_to_keep:\\n args.decoder_layers = len(args.decoder_layers_to_keep.split(\\\",\\\"))\\n\\n if getattr(args, \\\"max_target_positions\\\", None) is None:\\n args.max_target_positions = getattr(\\n args, \\\"tokens_per_sample\\\", DEFAULT_MAX_TARGET_POSITIONS\\n )\\n\\n if args.character_embeddings:\\n embed_tokens = CharacterTokenEmbedder(\\n task.source_dictionary,\\n eval(args.character_filters),\\n args.character_embedding_dim,\\n args.decoder_embed_dim,\\n args.char_embedder_highway_layers,\\n )\\n elif args.adaptive_input:\\n embed_tokens = AdaptiveInput(\\n len(task.source_dictionary),\\n task.source_dictionary.pad(),\\n args.decoder_input_dim,\\n args.adaptive_input_factor,\\n args.decoder_embed_dim,\\n options.eval_str_list(args.adaptive_input_cutoff, type=int),\\n args.quant_noise_pq,\\n args.quant_noise_pq_block_size,\\n )\\n else:\\n embed_tokens = cls.build_embedding(\\n args, task.source_dictionary, args.decoder_input_dim\\n )\\n\\n if args.tie_adaptive_weights:\\n assert args.adaptive_input\\n assert args.adaptive_input_factor == args.adaptive_softmax_factor\\n assert (\\n args.adaptive_softmax_cutoff == args.adaptive_input_cutoff\\n ), \\\"{} != {}\\\".format(\\n args.adaptive_softmax_cutoff, args.adaptive_input_cutoff\\n )\\n assert args.decoder_input_dim == args.decoder_output_dim\\n\\n decoder = LinearTransformerDecoder(\\n args, task.target_dictionary, embed_tokens, no_encoder_attn=True\\n )\\n return cls(decoder)\",\n \"def build_model(cls, args, task):\\n raise NotImplementedError(\\\"Model must implement the build_model method\\\")\",\n \"def build_model():\\n with tf.name_scope('placeholders'):\\n real_data_int = tf.placeholder(tf.int32, shape=[None, picture_size])\\n x_true = 2 * ((tf.cast(real_data_int, tf.float32) / 255.) - .5)\\n z = tf.placeholder(tf.float32, [None, input_dim])\\n if use_JL:\\n JL = tf.placeholder(tf.float32, [d_last_layer_size, JL_dim])\\n P_non_normalized = tf.placeholder(tf.float32, [JL_dim, n_projections])\\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\\n else:\\n JL = None\\n P_non_normalized = tf.placeholder(tf.float32, [d_last_layer_size, n_projections])\\n P_non_normalized_SWD = tf.placeholder(tf.float32, [picture_size, n_projections])\\n\\n x_generated = generator(z, n_features_first=n_features_first_g,\\n n_features_reduction_factor=n_features_reduction_factor, min_features=64,\\n BN=BN, power=power, extra_layer=extra_layer_g,\\n init_method=init_method, n_features_image=n_features_image)\\n\\n d_pred_true, d_last_true = discriminator(x_true, reuse=False, n_features_last=n_features_last_d,\\n n_features_increase_factor=n_features_reduction_factor,\\n min_features=min_features, d_BN=d_BN, power=power,\\n n_features_image=n_features_image, init_method=init_method)\\n d_pred_gen, d_last_gen = discriminator(x_generated, reuse=True, n_features_last=n_features_last_d,\\n n_features_increase_factor=n_features_reduction_factor,\\n min_features=min_features, d_BN=d_BN, power=power,\\n n_features_image=n_features_image, init_method=init_method)\\n\\n # define generator loss (big part taken from SWG)\\n with tf.name_scope('g_loss'):\\n # apply the Johnson-Lindenstrauss map, if wanted, to the flattened array\\n if use_JL:\\n JL_true = tf.matmul(d_last_true, JL)\\n JL_gen = tf.matmul(d_last_gen, JL)\\n else:\\n JL_true = d_last_true\\n JL_gen = d_last_gen\\n\\n # next project the samples (images). After being transposed, we have tensors\\n # of the format: [[projected_image1_proj1, projected_image2_proj1, ...],\\n # [projected_image1_proj2, projected_image2_proj2, ...],...]\\n # Each row has the projections along one direction. This makes it easier for the sorting that follows.\\n # first normalize the random normal vectors to lie in the sphere\\n P = tf.nn.l2_normalize(P_non_normalized, axis=0)\\n\\n projected_true = tf.transpose(tf.matmul(JL_true, P))\\n projected_fake = tf.transpose(tf.matmul(JL_gen, P))\\n\\n sorted_true, true_indices = tf.nn.top_k(input=projected_true, k=batch_size)\\n sorted_fake, fake_indices = tf.nn.top_k(input=projected_fake, k=batch_size)\\n\\n # For faster gradient computation, we do not use sorted_fake to compute\\n # loss. Instead we re-order the sorted_true so that the samples from the\\n # true distribution go to the correct sample from the fake distribution.\\n\\n # It is less expensive (memory-wise) to rearrange arrays in TF.\\n # Flatten the sorted_true from dim [n_projections, batch_size].\\n flat_true = tf.reshape(sorted_true, [-1])\\n\\n # Modify the indices to reflect this transition to an array.\\n # new index = row + index\\n rows = np.asarray([batch_size * np.floor(i * 1.0 / batch_size) for i in range(n_projections * batch_size)])\\n rows = rows.astype(np.int32)\\n flat_idx = tf.reshape(fake_indices, [-1, 1]) + np.reshape(rows, [-1, 1])\\n\\n # The scatter operation takes care of reshaping to the rearranged matrix\\n shape = tf.constant([batch_size * n_projections])\\n rearranged_true = tf.reshape(tf.scatter_nd(flat_idx, flat_true, shape), [n_projections, batch_size])\\n\\n generator_loss = tf.reduce_mean(tf.square(projected_fake - rearranged_true))\\n\\n # get the sliced Wasserstein distance (SWD) (since SWD and JLSWD are not comparable)\\n with tf.name_scope('SWD'):\\n P_SWD = tf.nn.l2_normalize(P_non_normalized_SWD, axis=0)\\n\\n projected_true_SWD = tf.transpose(tf.matmul(x_true, P_SWD))\\n projected_fake_SWD = tf.transpose(tf.matmul(x_generated, P_SWD))\\n\\n sorted_true_SWD, true_indices_SWD = tf.nn.top_k(input=projected_true_SWD, k=batch_size)\\n sorted_fake_SWD, fake_indices_SWD = tf.nn.top_k(input=projected_fake_SWD, k=batch_size)\\n\\n flat_true_SWD = tf.reshape(sorted_true_SWD, [-1])\\n flat_idx_SWD = tf.reshape(fake_indices_SWD, [-1, 1]) + np.reshape(rows, [-1, 1])\\n\\n rearranged_true_SWD = tf.reshape(tf.scatter_nd(flat_idx_SWD, flat_true_SWD, shape),\\n [n_projections, batch_size])\\n\\n SWD = tf.reduce_mean(tf.square(projected_fake_SWD - rearranged_true_SWD))\\n\\n # define the discriminator loss\\n with tf.name_scope('d_loss'):\\n d_true_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(d_pred_true), logits=d_pred_true)\\n d_fake_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(d_pred_gen), logits=d_pred_gen)\\n discriminator_loss = tf.reduce_mean(d_true_loss + d_fake_loss)\\n\\n with tf.name_scope('g_optimizer'):\\n generator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')\\n g_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\\n g_train = g_optimizer.minimize(generator_loss, var_list=generator_vars)\\n\\n with tf.name_scope('d_optimizer'):\\n discriminator_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')\\n d_optimizer = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\\n d_train = d_optimizer.minimize(discriminator_loss, var_list=discriminator_vars)\\n\\n return real_data_int, z, x_generated, JL, P_non_normalized, P_non_normalized_SWD, SWD, g_train, d_train\",\n \"def evaluate(args):\\n dataset_param_filepath = os.path.join(args.model, 'dataset.params')\\n dataset_params = putils.load_params(dataset_param_filepath)\\n left_vocab_filepath = os.path.join(args.model, 'left.vocab')\\n left_vocab = Vocab(vocab_filepath=left_vocab_filepath)\\n right_vocab_filepath = os.path.join(args.model, 'right.vocab')\\n right_vocab = Vocab(vocab_filepath=right_vocab_filepath)\\n model_params_filepath = os.path.join(args.model, 'model.params')\\n model_params = putils.load_params(model_params_filepath)\\n checkpoint_filepath = os.path.join(args.model, 'checkpoint.tar')\\n if not torch.cuda.is_available() and model_params['cuda']:\\n logger.info('Loading a GPU-trained model on CPU')\\n checkpoint = torch.load(checkpoint_filepath,\\n map_location=const.DEVICE)\\n elif torch.cuda.is_available() and model_params['cuda']:\\n logger.info('Loading a GPU-trained model on GPU')\\n checkpoint = torch.load(checkpoint_filepath)\\n elif torch.cuda.is_available() and not model_params['cuda']:\\n logger.info('Loading a CPU-trained model on GPU')\\n checkpoint = torch.load(checkpoint_filepath,\\n map_location='cuda:0')\\n else:\\n logger.info('Loading a CPU-trained model on CPU')\\n checkpoint = torch.load(checkpoint_filepath)\\n if checkpoint['encoder']['model_type'] == 'transformer':\\n encoder = TEncoder(input_size=checkpoint['encoder']['input_size'],\\n hidden_size=checkpoint['encoder']['hidden_size'],\\n num_layers=checkpoint['encoder']['num_layers'],\\n dropout=checkpoint['encoder']['dropout'],\\n num_attention_heads=checkpoint['encoder']['num_attention_heads'])\\n else:\\n encoder = Encoder(model_type=checkpoint['encoder']['model_type'],\\n input_size=checkpoint['encoder']['input_size'],\\n hidden_size=checkpoint['encoder']['hidden_size'],\\n num_layers=checkpoint['encoder']['num_layers'],\\n nonlinearity=checkpoint['encoder']['nonlinearity'],\\n bias=checkpoint['encoder']['bias'],\\n dropout=checkpoint['encoder']['dropout'],\\n bidirectional=checkpoint['encoder']['bidirectional'])\\n if checkpoint['decoder']['model_type'] == 'transformer':\\n decoder = TDecoder(hidden_size=checkpoint['decoder']['hidden_size'],\\n output_size=checkpoint['decoder']['output_size'],\\n num_layers=checkpoint['decoder']['num_layers'],\\n dropout=checkpoint['decoder']['dropout'],\\n num_attention_heads=checkpoint['decoder']['num_attention_heads'])\\n elif checkpoint['decoder']['with_attention']:\\n decoder = Attention(hidden_size=checkpoint['decoder']['hidden_size'],\\n output_size=checkpoint['decoder']['output_size'],\\n max_seq_len=dataset_params['max_seq_len'],\\n num_layers=checkpoint['decoder']['num_layers'],\\n nonlinearity=checkpoint['decoder']['nonlinearity'],\\n bias=checkpoint['decoder']['bias'],\\n dropout=checkpoint['decoder']['dropout'])\\n else:\\n decoder = Decoder(model_type=checkpoint['decoder']['model_type'],\\n hidden_size=checkpoint['decoder']['hidden_size'],\\n output_size=checkpoint['decoder']['output_size'],\\n num_layers=checkpoint['decoder']['num_layers'],\\n nonlinearity=checkpoint['decoder']['nonlinearity'],\\n bias=checkpoint['decoder']['bias'],\\n dropout=checkpoint['decoder']['dropout'])\\n encoder.load_state_dict(checkpoint['encoder_state_dict'])\\n decoder.load_state_dict(checkpoint['decoder_state_dict'])\\n if torch.cuda.is_available():\\n encoder.to(const.DEVICE)\\n decoder.to(const.DEVICE)\\n encoder.eval()\\n decoder.eval()\\n pairs = putils.convert_to_seq_pairs(args.data)\\n indexed_pairs = putils.index_pairs(pairs, left_vocab.char2idx,\\n right_vocab.char2idx)\\n if dataset_params['reverse']:\\n indexed_pairs = [(y, x) for x, y in indexed_pairs]\\n source_vocab = right_vocab\\n target_vocab = left_vocab\\n else:\\n source_vocab = left_vocab\\n target_vocab = right_vocab\\n if args.random > 0:\\n random.shuffle(indexed_pairs)\\n for seq_num in range(args.random):\\n seq = indexed_pairs[seq_num]\\n print('-'*80)\\n input_str = ' '.join(\\n ''.join([source_vocab.idx2char[idx] for idx in seq[0] if idx\\n not in [const.SOS_IDX, const.EOS_IDX]])\\n .split(const.SEP))\\n gold_str = ' '.join(\\n ''.join([target_vocab.idx2char[idx] for idx in seq[1] if idx\\n not in [const.SOS_IDX, const.EOS_IDX]])\\n .split(const.SEP))\\n predicted_idxx = decode(seq[0], args.itemize, encoder, decoder,\\n dataset_params['max_seq_len'])\\n pred_str = ' '.join(\\n ''.join([target_vocab.idx2char[idx] for idx in predicted_idxx\\n if idx not in [const.SOS_IDX, const.EOS_IDX]])\\n .split(const.SEP))\\n print('>', input_str)\\n print('=', gold_str)\\n print('<', pred_str)\\n else:\\n _evaluate(indexed_pairs, args.itemize, encoder, decoder,\\n target_vocab.idx2char, dataset_params['max_seq_len'])\",\n \"def init_model(model):\\n model(tf.random.uniform((1, 512, 512, 3)))\",\n \"def __init__(self, name): #, keep_axis=1):\\n\\n #:param int keep_axis: The axis which will be exempt from flattening (can only be one at most). If keep_axis is negative, we will count from the end.\\n #self.keep_axis = keep_axis\\n\\n # generate the model in tensorflow\\n self.name = name\\n super().__init__(name=name)\",\n \"def __init__(self, inputs, outputs,\\n session=tf.get_default_session, name='function'):\\n self.session, self.name = session, name\\n self.inputs, self.outputs = inputs, outputs\",\n \"def main():\\n flags = PARSER.parse_args()\\n\\n if flags.to == 'savedmodel':\\n to_savedmodel(input_shape=flags.input_shape,\\n model_fn=unet_fn,\\n src_dir=flags.checkpoint_dir,\\n dst_dir='./saved_model',\\n input_names=['IteratorGetNext'],\\n output_names=['total_loss_ref'],\\n use_amp=flags.use_amp,\\n use_xla=flags.use_xla,\\n compress=flags.compress)\\n if flags.to == 'tensorrt':\\n ds = Dataset(data_dir=flags.data_dir,\\n batch_size=1,\\n augment=False,\\n gpu_id=0,\\n num_gpus=1,\\n seed=42)\\n iterator = ds.test_fn(count=1).make_one_shot_iterator()\\n features = iterator.get_next()\\n\\n sess = tf.Session()\\n\\n def input_data():\\n return {'input_tensor:0': sess.run(features)}\\n\\n to_tensorrt(src_dir=flags.savedmodel_dir,\\n dst_dir='./tf_trt_model',\\n precision=flags.precision,\\n feed_dict_fn=input_data,\\n num_runs=1,\\n output_tensor_names=['Softmax:0'],\\n compress=flags.compress)\\n if flags.to == 'onnx':\\n to_onnx(src_dir=flags.savedmodel_dir,\\n dst_dir='./onnx_model',\\n compress=flags.compress)\",\n \"def build_model(tparams, options):\\n opt_ret = dict()\\n use_noise = theano.shared(numpy.asarray(1., dtype=theano.config.floatX))\\n try:\\n trng = RandomStreams(1234, use_cuda=True)\\n except:\\n print \\\"Could not apply use_cuda==True in RandonStreams ...\\\"\\n trng = RandomStreams(1234)\\n\\n xs = []\\n xmasks = []\\n\\n langs = options['langs']\\n for lang in langs:\\n # description string: #words x #samples\\n x_lang = tensor.matrix('x_%s'%lang, dtype='int64')\\n mask_lang = tensor.matrix('mask_%s'%lang, dtype='float32')\\n xs.append(x_lang)\\n xmasks.append(mask_lang)\\n\\n xs_r = []\\n xmasks_r = []\\n if options['bidirectional_enc']:\\n for i,lang in enumerate(langs):\\n x_lang = xs[i]\\n mask_lang = xmasks[i]\\n # reverse\\n x_lang_r = x_lang[::-1]\\n mask_lang_r = mask_lang[::-1]\\n\\n xs_r.append(x_lang_r)\\n xmasks_r.append(mask_lang_r)\\n\\n sents_all = []\\n im = tensor.matrix('im', dtype='float32')\\n n_samples = im.shape[0]\\n\\n for i,lang in enumerate(langs):\\n x_lang = xs[i]\\n mask_lang = xmasks[i]\\n\\n n_timesteps_lang = x_lang.shape[0]\\n n_samples_lang = x_lang.shape[1]\\n\\n if options['use_dropout']:\\n # dropout probs for the word embeddings\\n retain_probability_emb = 1-options['dropout_embedding']\\n # dropout probs for the RNN hidden states\\n retain_probability_hidden = 1-options['dropout_hidden']\\n # dropout probs for the source words\\n retain_probability_source = 1-options['dropout_source']\\n # hidden states\\n rec_dropout = shared_dropout_layer((2, n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\\n rec_dropout_r = shared_dropout_layer((2, n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\\n # word embeddings\\n emb_dropout = shared_dropout_layer((2, n_samples_lang, options['dim_word']), use_noise, trng, retain_probability_emb)\\n emb_dropout_r = shared_dropout_layer((2, n_samples_lang, options['dim_word']), use_noise, trng, retain_probability_emb)\\n # source words\\n source_dropout = shared_dropout_layer((n_timesteps_lang, n_samples_lang, 1), use_noise, trng, retain_probability_source)\\n source_dropout = tensor.tile(source_dropout, (1,1,options['dim_word']))\\n else:\\n # hidden states\\n rec_dropout = theano.shared(numpy.array([1.]*2, dtype='float32'))\\n rec_dropout_r = theano.shared(numpy.array([1.]*2, dtype='float32'))\\n # word embeddings\\n emb_dropout = theano.shared(numpy.array([1.]*2, dtype='float32'))\\n emb_dropout_r = theano.shared(numpy.array([1.]*2, dtype='float32'))\\n\\n # Word embedding (for a particular language `lang`)\\n # forward\\n emb_lang = tparams['Wemb_%s'%lang][x_lang.flatten()]\\n emb_lang = emb_lang.reshape([n_timesteps_lang, n_samples_lang, options['dim_word']])\\n\\n if options['use_dropout']:\\n emb_lang *= source_dropout\\n\\n if options['bidirectional_enc']:\\n x_lang_r = xs_r[i]\\n mask_lang_r = xmasks_r[i]\\n\\n # backward lang encoder\\n emb_lang_r = tparams['Wemb_%s'%lang][x_lang_r.flatten()]\\n emb_lang_r = emb_lang_r.reshape([n_timesteps_lang, n_samples_lang, options['dim_word']])\\n\\n if options['use_dropout']:\\n emb_lang_r *= source_dropout[::-1]\\n\\n # Encode sentence in language `lang`\\n if options['encoder_%s'%lang] == 'bow':\\n sents_lang = (emb_lang * mask_lang[:,:,None]).sum(0)\\n else:\\n # iteratively push input from first hidden layer until the last\\n for i in range(int(options['n_enc_hidden_layers'])):\\n layer_name_prefix='encoder_%s_%i'%(lang,i)\\n # if first hidden layer use wembs, otherwise output of previous hidden layer\\n layer_below=emb_lang if i==0 else layer_below[0]\\n\\n # do not apply dropout on word embeddings layer\\n #if options['use_dropout'] and i>0:\\n # layer_below = dropout_layer(layer_below, use_noise, trng, prob=options['dropout_prob'])\\n\\n layer_below=get_layer(options['encoder_%s'%lang])[1](tparams,\\n layer_below, options, None, prefix=layer_name_prefix, mask=mask_lang,\\n emb_dropout=emb_dropout, \\n rec_dropout=rec_dropout)\\n\\n if i==int(options['n_enc_hidden_layers'])-1:\\n # sentence embeddings (projections) are the output of the last hidden layer\\n proj_lang = layer_below\\n\\n # apply forward and backward steps and concatenate both\\n if options['bidirectional_enc']:\\n # concatenate forward and backward pass RNNs\\n # iteratively push input from first hidden layer until the last\\n for i in range(int(options['n_enc_hidden_layers'])):\\n layer_name_prefix='encoder_%s_r_%i'%(lang,i)\\n # if first hidden layer use wembs, else output of prev hidden layer\\n layer_below=emb_lang_r if i==0 else layer_below[0]\\n\\n # do not apply dropout on word embeddings layer\\n #if options['use_dropout'] and i>0:\\n # layer_below = dropout_layer(layer_below, use_noise, trng, prob=options['dropout_prob'])\\n\\n layer_below=get_layer(options['encoder_%s'%lang])[1](tparams,\\n layer_below, options, None,\\n prefix=layer_name_prefix,\\n mask=mask_lang_r,\\n emb_dropout=emb_dropout_r,\\n rec_dropout=rec_dropout_r)\\n\\n if i==int(options['n_enc_hidden_layers'])-1:\\n # sentence embeddings (projections) are the output of the last hidden layer\\n proj_lang_r = layer_below\\n\\n # use the last state of forward + backward encoder rnns\\n sents_lang = concatenate([proj_lang[0][-1], proj_lang_r[0][-1]], axis=proj_lang[0].ndim-2)\\n else:\\n sents_lang = proj_lang[0][-1]\\n\\n if options['use_dropout']:\\n sents_lang *= shared_dropout_layer((n_samples_lang, options['dim']), use_noise, trng, retain_probability_hidden)\\n\\n # project sentences into multimodal space\\n sents_mm = get_layer('ff')[1](tparams, sents_lang, options, prefix='ff_sentence_mm', activ='linear')\\n\\n if options['attention_type'] == 'dot':\\n sents_mm = l2norm(sents_mm)\\n\\n if options['use_dropout']:\\n sents_mm *= shared_dropout_layer((n_samples_lang, options['dim_multimodal']), use_noise, trng, retain_probability_hidden)\\n\\n sents_all.append(sents_mm)\\n\\n # Encode images\\n images = get_layer('ff')[1](tparams, im, options, prefix='ff_image_mm', activ='linear')\\n\\n if options['attention_type'] == 'dot':\\n images = l2norm(images)\\n\\n if options['use_dropout']:\\n images *= shared_dropout_layer((n_samples, options['dim_multimodal']), use_noise, trng, retain_probability_hidden)\\n\\n # Compute loss\\n lambda_img_sent = options['lambda_img_sent']\\n lambda_sent_sent = options['lambda_sent_sent']\\n if options['use_all_costs']:\\n cost = contrastive_loss_all(tparams, options,\\n images, sents_all, lambda_img_sent, lambda_sent_sent)\\n else:\\n cost = contrastive_loss(tparams, options,\\n images, sents_all)\\n\\n # return flattened inputs\\n inps = []\\n inps.extend(xs)\\n inps.extend(xmasks)\\n inps.append(im)\\n\\n return trng, inps, cost\",\n \"def create_model(self):\\n\\t\\twith tf.name_scope(\\\"input\\\"):\\n\\t\\t\\tself.user_embedding = tf.get_variable(\\\"user_embed\\\", \\n\\t\\t\\t\\t[self.user_size, self.embed_size], dtype=tf.float32)\\n\\t\\t\\tself.item_embedding = tf.get_variable(\\\"item_embed\\\", \\n\\t\\t\\t\\t[self.item_size, self.embed_size], dtype=tf.float32)\\n\\t\\t\\tself.user_embed = tf.nn.embedding_lookup(\\n\\t\\t\\t\\t\\t\\t\\tself.user_embedding, self.user)\\n\\t\\t\\tself.item_embed = tf.nn.embedding_lookup(\\n\\t\\t\\t\\t\\t\\t\\tself.user_embedding, self.item)\\n\\t\\twith tf.name_scope(\\\"fusion\\\"):\\n\\t\\t\\tself.user_fusion_add = self.user_embed + self.user_feature\\n\\t\\t\\tself.item_fusion_add = self.item_embed + self.item_feature\\n\\n\\t\\t\\tself.user_fusion = tf.layers.dense(inputs=self.user_fusion_add,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=self.embed_size,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=self.activation_func,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='user_fusion')\\n\\t\\t\\tself.item_fusion = tf.layers.dense(inputs=self.item_fusion_add,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=self.embed_size,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=self.activation_func,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='item_fusion')\\n\\n\\t\\twith tf.name_scope(\\\"attention\\\"):\\n\\t\\t\\tself.feature_all = tf.concat([\\n\\t\\t\\t\\t\\t\\tself.user_fusion, self.item_fusion], -1)\\n\\t\\t\\tself.att_layer1 = tf.layers.dense(inputs=self.feature_all,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=1,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=self.activation_func,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='att_layer1')\\n\\t\\t\\tself.att_layer2 = tf.layers.dense(inputs=self.att_layer1,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=self.embed_size,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=self.activation_func,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='att_layer2')\\n\\t\\t\\tself.att_weights = tf.nn.softmax(self.att_layer2, \\n\\t\\t\\t\\t\\t\\t\\t\\taxis=-1, name='att_softmax')\\n\\n\\t\\twith tf.name_scope(\\\"prediction\\\"):\\n\\t\\t\\tself.interact = self.att_weights*self.user_fusion*self.item_fusion\\n\\t\\t\\tself.interact1 = tf.layers.dense(inputs=self.interact,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=self.embed_size,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=self.activation_func,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='interact1')\\n\\t\\t\\tself.interact1 = tf.nn.dropout(self.interact1, self.dropout)\\n\\t\\t\\tself.prediction = tf.layers.dense(inputs=self.interact,\\n\\t\\t\\t\\t\\t\\t\\t\\tunits=1,\\n\\t\\t\\t\\t\\t\\t\\t\\tactivation=None,\\n\\t\\t\\t\\t\\t\\t\\t\\tkernel_regularizer=self.regularizer,\\n\\t\\t\\t\\t\\t\\t\\t\\tname='prediction')\\n\\t\\t\\tself.prediction = tf.reshape(self.prediction, [-1])\",\n \"def __call__(self, params: Mapping[str, Any],\\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\\n ...\",\n \"def model_fn(features,labels,mode,params):\\n tf.logging.info('*** Features ***')\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s ,shape = %s\\\" % (name,features[name].shape))\\n\\n input_ids = features['input_ids']\\n input_mask = features['input_mask']\\n segment_ids = features['segment_ids']\\n label_ids = features['label_ids']\\n if 'is_real_example' in features:\\n is_real_example = tf.cast(features['is_real_example'],dtype=tf.float32)\\n else:\\n is_real_example = tf.ones(tf.shape(label_ids),dtype=tf.float32)\\n\\n is_training = (mode == tf_estimator.estimator.ModeKeys.TRAIN)\\n\\n (total_loss,per_example_loss,probabilities,predictions) = \\\\\\n create_model(albert_config,is_training,input_ids,input_mask,\\n segment_ids,label_ids,num_labels,\\n use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n if init_checkpoint:\\n (assignment_map,initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)\\n tf.train.init_from_checkpoint(init_checkpoint,assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf_estimator.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer(total_loss,\\n learning_rate,\\n num_train_steps,\\n num_warmup_steps,use_tpu=False)\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n )\\n elif mode == tf_estimator.estimator.ModeKeys.EVAL:\\n def metric_fn(per_example_loss,label_ids,logits,is_real_example):\\n accuracy = tf.metrics.accuracy(\\n labels=label_ids,predictions=predictions,\\n weights=is_real_example\\n )\\n loss = tf.metrics.mean(\\n values=per_example_loss,weights=is_real_example\\n )\\n return {\\n 'eval_accuracy':accuracy,\\n 'eval_loss':loss,\\n }\\n\\n eval_metrics = metric_fn(per_example_loss,label_ids,predictions,is_real_example)\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metric_ops=eval_metrics\\n )\\n else:\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n predictions={\\n 'probabilities':probabilities,\\n 'predictions':predictions,\\n },\\n )\\n\\n return output_spec\",\n \"def build_model(cls, args, task):\\n\\n # make sure all arguments are present in older models\\n base_architecture(args)\\n\\n if getattr(args, \\\"max_source_positions\\\", None) is None:\\n args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS\\n if getattr(args, \\\"max_target_positions\\\", None) is None:\\n args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS\\n\\n args.ddp_rank = distributed_utils.get_data_parallel_rank()\\n\\n src_dict, tgt_dict = task.source_dictionary, task.target_dictionary\\n\\n if args.share_all_embeddings:\\n if src_dict != tgt_dict:\\n raise ValueError(\\\"--share-all-embeddings requires a joined dictionary\\\")\\n if args.encoder_embed_dim != args.decoder_embed_dim:\\n raise ValueError(\\n \\\"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim\\\"\\n )\\n if args.decoder_embed_path and (\\n args.decoder_embed_path != args.encoder_embed_path\\n ):\\n raise ValueError(\\n \\\"--share-all-embeddings not compatible with --decoder-embed-path\\\"\\n )\\n encoder_embed_tokens = cls.build_embedding(\\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\\n )\\n decoder_embed_tokens = encoder_embed_tokens\\n args.share_decoder_input_output_embed = True\\n else:\\n encoder_embed_tokens = cls.build_embedding(\\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\\n )\\n decoder_embed_tokens = cls.build_embedding(\\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\\n )\\n if getattr(args, \\\"offload_activations\\\", False):\\n args.checkpoint_activations = True # offloading implies checkpointing\\n\\n encoder_embed_positions = (\\n PositionalEmbedding(\\n args.max_source_positions,\\n args.encoder_embed_dim,\\n src_dict.pad(),\\n learned=args.encoder_learned_pos,\\n )\\n if not args.no_token_positional_embeddings\\n else None\\n )\\n\\n decoder_embed_positions = (\\n PositionalEmbedding(\\n args.max_target_positions,\\n args.decoder_embed_dim,\\n tgt_dict.pad(),\\n learned=args.decoder_learned_pos,\\n )\\n if not args.no_token_positional_embeddings\\n else None\\n )\\n\\n if args.share_decoder_input_output_embed:\\n output_projection = torch.nn.Linear(\\n decoder_embed_tokens.weight.shape[1],\\n decoder_embed_tokens.weight.shape[0],\\n bias=False,\\n )\\n output_projection.weight = decoder_embed_tokens.weight\\n else:\\n output_projection = torch.nn.Linear(\\n args.decoder_embed_dim, len(tgt_dict), bias=False\\n )\\n torch.nn.init.normal_(\\n output_projection.weight, mean=0, std=args.decoder_embed_dim**-0.5\\n )\\n\\n encoder = cls.build_encoder(\\n args,\\n encoder_embed_tokens,\\n encoder_embed_positions,\\n src_dict,\\n )\\n decoder = cls.build_decoder(\\n args,\\n decoder_embed_tokens,\\n decoder_embed_positions,\\n output_projection,\\n tgt_dict,\\n )\\n\\n if not args.share_all_embeddings:\\n min_params_to_wrap = getattr(\\n args, \\\"min_params_to_wrap\\\", DEFAULT_MIN_PARAMS_TO_WRAP\\n )\\n # fsdp_wrap is a no-op when --ddp-backend != fully_sharded\\n encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)\\n decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)\\n return cls(args, encoder, decoder)\",\n \"def train(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def create_model(scene_dim_x, scene_dim_y, scene_dim_z):\\n input_placeholder = tf.placeholder(\\n tf.float32,\\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x, 2],\\n name='pl_scan')\\n target_placeholder = tf.placeholder(\\n tf.float32,\\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x, 2],\\n name='pl_target')\\n target_lo_placeholder = tf.placeholder(\\n tf.float32,\\n shape=[1, scene_dim_z // 2, scene_dim_y // 2, scene_dim_x // 2, 2],\\n name='pl_target_lo')\\n target_sem_placeholder = tf.placeholder(\\n tf.uint8,\\n shape=[1, scene_dim_z, scene_dim_y, scene_dim_x],\\n name='pl_target_sem')\\n target_sem_lo_placeholder = tf.placeholder(\\n tf.uint8,\\n shape=[1, scene_dim_z // 2, scene_dim_y // 2, scene_dim_x // 2],\\n name='pl_target_sem_lo')\\n # No previous level input if at base level.\\n if FLAGS.is_base_level:\\n target_scan_low_resolution = None\\n target_semantics_low_resolution = None\\n else:\\n target_scan_low_resolution = target_lo_placeholder\\n target_semantics_low_resolution = target_sem_lo_placeholder\\n logits = model.model(\\n input_scan=input_placeholder,\\n target_scan_low_resolution=target_scan_low_resolution,\\n target_scan=target_placeholder,\\n target_semantics_low_resolution=target_semantics_low_resolution,\\n target_semantics=target_sem_placeholder,\\n num_quant_levels=FLAGS.num_quant_levels,\\n predict_semantics=FLAGS.predict_semantics,\\n use_p_norm=FLAGS.p_norm > 0)\\n return (input_placeholder, target_placeholder, target_lo_placeholder,\\n target_sem_placeholder, target_sem_lo_placeholder, logits)\",\n \"def build_tf_graph(self):\\n raise NotImplementedError\",\n \"def main():\\n\\n args = define_and_process_args()\\n print('\\\\n', 'ARGUMENTS', '\\\\n\\\\n', args, '\\\\n')\\n\\n log_dir = get_log_dir(args)\\n print('\\\\n', 'LOG DIRECTORY', '\\\\n\\\\n', log_dir, '\\\\n')\\n\\n standardized_data_path = os.path.join(args.data_dir, args.data_filename)\\n if not os.path.exists(standardized_data_path):\\n message = '%s does not exist.' % standardized_data_path\\n raise ValueError(message)\\n\\n dataset = data.Dataset(standardized_data_path)\\n train_raw_seqs, test_raw_seqs = dataset.get_splits(args.test_users)\\n train_triplets = [data.prepare_raw_seq(seq) for seq in train_raw_seqs]\\n test_triplets = [data.prepare_raw_seq(seq) for seq in test_raw_seqs]\\n\\n train_input_seqs, train_reset_seqs, train_label_seqs = zip(*train_triplets)\\n test_input_seqs, test_reset_seqs, test_label_seqs = zip(*test_triplets)\\n\\n Model = eval('models.' + args.model_type + 'Model')\\n input_size = dataset.input_size\\n target_size = dataset.num_classes\\n\\n # This is just to satisfy a low-CPU requirement on our cluster\\n # when using GPUs.\\n if 'CUDA_VISIBLE_DEVICES' in os.environ:\\n config = tf.ConfigProto(intra_op_parallelism_threads=2,\\n inter_op_parallelism_threads=2)\\n else:\\n config = None\\n\\n with tf.Session(config=config) as sess:\\n model = Model(input_size, target_size, args.num_layers,\\n args.hidden_layer_size, args.init_scale,\\n args.dropout_keep_prob)\\n optimizer = optimizers.Optimizer(\\n model.loss, args.num_train_sweeps, args.initial_learning_rate,\\n args.num_initial_sweeps, args.num_sweeps_per_decay,\\n args.decay_factor, args.max_global_grad_norm)\\n train(sess, model, optimizer, log_dir, args.batch_size,\\n args.num_sweeps_per_summary, args.num_sweeps_per_save,\\n train_input_seqs, train_reset_seqs, train_label_seqs,\\n test_input_seqs, test_reset_seqs, test_label_seqs)\",\n \"def __init__(\\n self,\\n model_dir,\\n model_filename=\\\"model.pdmodel\\\",\\n params_filename=\\\"model.pdiparams\\\",\\n precision=\\\"fp32\\\",\\n use_trt=False,\\n use_mkldnn=False,\\n batch_size=1,\\n device=\\\"CPU\\\",\\n min_subgraph_size=3,\\n use_dynamic_shape=False,\\n cpu_threads=1,\\n ):\\n self.rerun_flag = False\\n if device != \\\"GPU\\\" and use_trt:\\n raise ValueError(\\n \\\"Predict by TensorRT mode: {}, expect device=='GPU', but device == {}\\\".format(precision, device)\\n )\\n config = Config(os.path.join(model_dir, model_filename), os.path.join(model_dir, params_filename))\\n if device == \\\"GPU\\\":\\n # initial GPU memory(M), device ID\\n config.enable_use_gpu(200, 0)\\n # optimize graph and fuse op\\n config.switch_ir_optim(True)\\n else:\\n config.disable_gpu()\\n config.set_cpu_math_library_num_threads(cpu_threads)\\n config.switch_ir_optim()\\n if use_mkldnn:\\n config.enable_mkldnn()\\n if precision == \\\"int8\\\":\\n config.enable_mkldnn_int8({\\\"conv2d\\\", \\\"depthwise_conv2d\\\", \\\"pool2d\\\", \\\"transpose2\\\", \\\"elementwise_mul\\\"})\\n if precision == \\\"bf16\\\":\\n config.enable_mkldnn_bfloat16()\\n\\n if precision == \\\"bf16\\\":\\n config.enable_mkldnn_bfloat16()\\n\\n if use_trt:\\n if precision == \\\"bf16\\\":\\n print(\\\"paddle trt does not support bf16, switching to fp16.\\\")\\n precision = \\\"fp16\\\"\\n\\n precision_map = {\\n \\\"int8\\\": Config.Precision.Int8,\\n \\\"fp32\\\": Config.Precision.Float32,\\n \\\"fp16\\\": Config.Precision.Half,\\n }\\n assert precision in precision_map.keys()\\n\\n if use_dynamic_shape:\\n dynamic_shape_file = os.path.join(model_dir, \\\"dynamic_shape.txt\\\")\\n if os.path.exists(dynamic_shape_file):\\n config.enable_tuned_tensorrt_dynamic_shape()\\n print(\\\"trt set dynamic shape done!\\\")\\n else:\\n # In order to avoid memory overflow when collecting dynamic shapes, it is changed to use CPU.\\n config.disable_gpu()\\n config.set_cpu_math_library_num_threads(10)\\n config.collect_shape_range_info(dynamic_shape_file)\\n print(\\\"Start collect dynamic shape...\\\")\\n self.rerun_flag = True\\n\\n if not self.rerun_flag:\\n config.enable_tensorrt_engine(\\n workspace_size=1 << 30,\\n max_batch_size=batch_size,\\n min_subgraph_size=min_subgraph_size,\\n precision_mode=precision_map[precision],\\n use_static=True,\\n use_calib_mode=False,\\n )\\n\\n # enable shared memory\\n config.enable_memory_optim()\\n self.predictor = create_predictor(config)\\n self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()]\\n self.output_handles = [self.predictor.get_output_handle(name) for name in self.predictor.get_output_names()]\\n print(\\\"[Paddle Inference Backend] Completed PaddleInferenceEngine init ...\\\")\",\n \"def main():\\n\\n args = get_arguments()\\n\\n w, h = map(int, args.input_size.split(','))\\n\\n config_path = os.path.join(os.path.dirname(args.restore_from),'opts.yaml')\\n with open(config_path, 'r') as stream:\\n config = yaml.load(stream)\\n\\n args.model = config['model']\\n print('ModelType:%s'%args.model)\\n print('NormType:%s'%config['norm_style'])\\n gpu0 = args.gpu\\n batchsize = args.batchsize\\n\\n model_name = os.path.basename( os.path.dirname(args.restore_from) )\\n #args.save += model_name\\n\\n if not os.path.exists(args.save):\\n os.makedirs(args.save)\\n confidence_path = os.path.join(args.save, 'submit/confidence')\\n label_path = os.path.join(args.save, 'submit/labelTrainIds')\\n label_invalid_path = os.path.join(args.save, 'submit/labelTrainIds_invalid')\\n for path in [confidence_path, label_path, label_invalid_path]:\\n if not os.path.exists(path):\\n os.makedirs(path)\\n\\n if args.model == 'DeepLab':\\n model = DeeplabMulti(num_classes=args.num_classes, use_se = config['use_se'], train_bn = False, norm_style = config['norm_style'])\\n elif args.model == 'Oracle':\\n model = Res_Deeplab(num_classes=args.num_classes)\\n if args.restore_from == RESTORE_FROM:\\n args.restore_from = RESTORE_FROM_ORC\\n elif args.model == 'DeeplabVGG':\\n model = DeeplabVGG(num_classes=args.num_classes)\\n if args.restore_from == RESTORE_FROM:\\n args.restore_from = RESTORE_FROM_VGG\\n\\n if args.restore_from[:4] == 'http' :\\n saved_state_dict = model_zoo.load_url(args.restore_from)\\n else:\\n saved_state_dict = torch.load(args.restore_from)\\n\\n try:\\n model.load_state_dict(saved_state_dict)\\n except:\\n model = torch.nn.DataParallel(model)\\n model.load_state_dict(saved_state_dict)\\n model.eval()\\n model.cuda(gpu0)\\n\\n testloader = data.DataLoader(DarkZurichDataSet(args.data_dir, args.data_list, crop_size=(h, w), resize_size=(w, h), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),\\n batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)\\n\\n scale = 1.25\\n testloader2 = data.DataLoader(DarkZurichDataSet(args.data_dir, args.data_list, crop_size=(round(h*scale), round(w*scale) ), resize_size=( round(w*scale), round(h*scale)), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),\\n batch_size=batchsize, shuffle=False, pin_memory=True, num_workers=4)\\n\\n\\n if version.parse(torch.__version__) >= version.parse('0.4.0'):\\n interp = nn.Upsample(size=(1080, 1920), mode='bilinear', align_corners=True)\\n else:\\n interp = nn.Upsample(size=(1080, 1920), mode='bilinear')\\n\\n sm = torch.nn.Softmax(dim = 1)\\n log_sm = torch.nn.LogSoftmax(dim = 1)\\n kl_distance = nn.KLDivLoss( reduction = 'none')\\n prior = np.load('./utils/prior_all.npy').transpose((2,0,1))[np.newaxis, :, :, :]\\n prior = torch.from_numpy(prior)\\n for index, img_data in enumerate(zip(testloader, testloader2) ):\\n batch, batch2 = img_data\\n image, _, name = batch\\n image2, _, name2 = batch2\\n\\n inputs = image.cuda()\\n inputs2 = image2.cuda()\\n print('\\\\r>>>>Extracting feature...%04d/%04d'%(index*batchsize, args.batchsize*len(testloader)), end='')\\n if args.model == 'DeepLab':\\n with torch.no_grad():\\n output1, output2 = model(inputs)\\n output_batch = interp(sm(0.5* output1 + output2))\\n\\n heatmap_batch = torch.sum(kl_distance(log_sm(output1), sm(output2)), dim=1)\\n\\n output1, output2 = model(fliplr(inputs))\\n output1, output2 = fliplr(output1), fliplr(output2)\\n output_batch += interp(sm(0.5 * output1 + output2))\\n del output1, output2, inputs\\n\\n output1, output2 = model(inputs2)\\n output_batch += interp(sm(0.5* output1 + output2))\\n output1, output2 = model(fliplr(inputs2))\\n output1, output2 = fliplr(output1), fliplr(output2)\\n output_batch += interp(sm(0.5 * output1 + output2))\\n del output1, output2, inputs2\\n ratio = 0.95\\n output_batch = output_batch.cpu() / 4\\n # output_batch = output_batch *(ratio + (1 - ratio) * prior)\\n output_batch = output_batch.data.numpy()\\n heatmap_batch = heatmap_batch.cpu().data.numpy()\\n elif args.model == 'DeeplabVGG' or args.model == 'Oracle':\\n output_batch = model(Variable(image).cuda())\\n output_batch = interp(output_batch).cpu().data.numpy()\\n\\n output_batch = output_batch.transpose(0,2,3,1)\\n score_batch = np.max(output_batch, axis=3)\\n output_batch = np.asarray(np.argmax(output_batch, axis=3), dtype=np.uint8)\\n\\n threshold = 0.3274\\n for i in range(output_batch.shape[0]):\\n output_single = output_batch[i,:,:]\\n output_col = colorize_mask(output_single)\\n output = Image.fromarray(output_single)\\n\\n name_tmp = name[i].split('/')[-1]\\n dir_name = name[i].split('/')[-2]\\n save_path = args.save + '/' + dir_name\\n if not os.path.isdir(save_path):\\n os.mkdir(save_path)\\n output.save('%s/%s' % (save_path, name_tmp))\\n print('%s/%s' % (save_path, name_tmp))\\n output_col.save('%s/%s_color.png' % (save_path, name_tmp.split('.')[0]))\\n\\n # heatmap_tmp = heatmap_batch[i,:,:]/np.max(heatmap_batch[i,:,:])\\n # fig = plt.figure()\\n # plt.axis('off')\\n # heatmap = plt.imshow(heatmap_tmp, cmap='viridis')\\n # fig.colorbar(heatmap)\\n # fig.savefig('%s/%s_heatmap.png' % (save_path, name_tmp.split('.')[0]))\\n\\n if args.set == 'test' or args.set == 'val':\\n # label\\n output.save('%s/%s' % (label_path, name_tmp))\\n # label invalid\\n output_single[score_batch[i, :, :] < threshold] = 255\\n output = Image.fromarray(output_single)\\n output.save('%s/%s' % (label_invalid_path, name_tmp))\\n # conficence\\n\\n confidence = score_batch[i, :, :] * 65535\\n confidence = np.asarray(confidence, dtype=np.uint16)\\n print(confidence.min(), confidence.max())\\n iio.imwrite('%s/%s' % (confidence_path, name_tmp), confidence)\\n\\n return args.save\",\n \"def _ProjectImpl(self, tensor_names: List[Text]) -> \\\"TFXIO\\\":\",\n \"def convert_to_model(self, *args):\",\n \"def run_model (arguments):\\n if arguments.train is not None:\\n # Train a new model, optionally with a certain number of epochs\\n predictor = None\\n if len(arguments.train) > 0:\\n predictor = train(n_epochs=arguments.train[0])\\n else:\\n predictor = train()\\n # Afterwards save it\\n now = datetime.now(timezone.utc)\\n predictor.to_disk(fname=f\\\"model_parameters_{now.strftime('%Y%m%d%H%M%S')}\\\")\\n elif arguments.export_embeddings:\\n # Load the saved predictor ...\\n predictor = Predictor.from_file()\\n # ... and then dump the models to disk.\\n predictor.subj.export_embeddings(\\\"subject\\\")\\n predictor.obj.export_embeddings(\\\"object\\\")\\n print(\\\"Models are saved to output directory for loading with http://projector.tensorflow.org/.\\\")\\n elif arguments.console:\\n # Opens a console for prediction without training\\n predictor = Predictor.from_file()\\n tinker(predictor)\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n (total_loss, per_example_loss, logits, probabilities) = model_function.create(\\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\\n num_labels, use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n scaffold_fn = None\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu,\\n scope=(\\\"loss\\\" if model_function.freeze else None))\\n\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n scaffold_fn=scaffold_fn)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n\\n if model_function.task_type == TaskType.CLASSIFICATION:\\n\\n def metric_fn(per_example_loss, label_ids, logits):\\n predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)\\n accuracy = tf.metrics.accuracy(label_ids, predictions)\\n loss = tf.metrics.mean(per_example_loss)\\n return {\\n \\\"eval_accuracy\\\": accuracy,\\n \\\"eval_loss\\\": loss,\\n }\\n elif model_function.task_type == TaskType.REGRESSION:\\n\\n def metric_fn(per_example_loss, label_ids, logits):\\n ground_truth = tf.log1p(tf.clip_by_value(tf.cast(label_ids, tf.float32), 1e-8, 1e+30))\\n predictions = tf.log1p(tf.clip_by_value(logits, 1e-8, 1e+30))\\n return {\\n \\\"eval_loss\\\": tf.metrics.mean(per_example_loss),\\n \\\"another_loss\\\": tf.metrics.mean_squared_error(ground_truth, predictions)\\n }\\n else:\\n raise NotImplementedError()\\n\\n eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metrics=eval_metrics,\\n scaffold_fn=scaffold_fn)\\n else:\\n predictions = {\\n \\\"result\\\": probabilities\\n }\\n print(probabilities.shape)\\n print(type(probabilities))\\n\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)\\n return output_spec\",\n \"def main(args):\\n with tf.Graph().as_default():\\n with tf.Session() as sess:\\n # prepare validate datasets\\n train_x, train_y, test_x, test_y = load_dataset()\\n\\n # Load the modelc\\n load_model(args.model)\\n\\n # Get input and output tensors, ignore phase_train_placeholder\\n # for it have default value.\\n inputs_placeholder = tf.get_default_graph().get_tensor_by_name(\\\"input:0\\\")\\n\\n feature_maps = tf.get_default_graph().\\\\\\n get_tensor_by_name('MobileFaceNet/MobileFaceNet/'\\n 'Conv2d_4_InvResBlock_5/Conv/Conv2D:0')\\n\\n feature_maps_r = tf.reshape(feature_maps, [-1, 3, 14, 14, 256])\\n anchor_feature_maps = feature_maps_r[:, 0, :, :, :]\\n pos_feature_maps = feature_maps_r[:, 1, :, :, :]\\n neg_feature_maps = feature_maps_r[:, 2, :, :, :]\\n\\n anchor_labels = tf.placeholder(tf.int32, [None, 3], name='anchor_labels')\\n neg_labels = tf.placeholder(tf.int32, [None, 3], name='neg_labels')\\n test_labels = tf.placeholder(tf.int32, [None, 3], name='test_labels')\\n learning_rate = tf.placeholder(tf.float32, name='learning_rate')\\n\\n anchor_logits, anchor_feature = network(feature_maps=anchor_feature_maps, keep_prob=0.5)\\n pos_logits, pos_feature = network(feature_maps=pos_feature_maps, keep_prob=0.5)\\n neg_logits, neg_feature = network(feature_maps=neg_feature_maps, keep_prob=0.5)\\n test_logits, _ = network(feature_maps=feature_maps, keep_prob=1.0,\\n is_training=False, reuse=True)\\n train_accuracy = calculate_accuracy(logit=anchor_logits, label=anchor_labels,\\n name='train_accuracy')\\n test_accuracy = calculate_accuracy(logit=test_logits, label=test_labels,\\n name='test_accuracy')\\n\\n with tf.name_scope(\\\"retrain_loss\\\"):\\n pos_pair_loss = tf.losses.mean_squared_error(anchor_feature, pos_feature)\\n temp_neg_pair_loss = tf.losses.mean_squared_error(anchor_feature, neg_feature)\\n neg_pair_loss = tf.maximum(0.0, 2.0 - temp_neg_pair_loss)\\n\\n anchor_loss = cross_entropy_loss(anchor_logits, anchor_labels)\\n pos_loss = cross_entropy_loss(pos_logits, anchor_labels)\\n neg_loss = cross_entropy_loss(neg_logits, neg_labels)\\n\\n loss = anchor_loss + pos_loss + neg_loss + pos_pair_loss + neg_pair_loss\\n\\n with tf.name_scope(\\\"retrain_op\\\"): # not shown in the book\\n optimizer = tf.train.GradientDescentOptimizer(learning_rate) # not shown\\n train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,\\n scope=\\\"retrain_net\\\")\\n train_op = optimizer.minimize(loss, var_list=train_vars)\\n\\n uninitialized_vars = []\\n for var in tf.all_variables():\\n try:\\n sess.run(var)\\n except tf.errors.FailedPreconditionError:\\n uninitialized_vars.append(var)\\n\\n init_new_vars_op = tf.initialize_variables(uninitialized_vars)\\n sess.run(init_new_vars_op)\\n\\n model_path = './checkpoint/SR_model/'\\n new_saver = tf.train.Saver()\\n\\n batch_size = 7\\n n_epochs = 45\\n train_iteration = len(train_x) // batch_size\\n test_iteration = len(test_x) // batch_size\\n\\n # loop for epoch\\n best_test_accuracy = 0\\n epoch_lr = 0.0005\\n for epoch in range(0, n_epochs):\\n if epoch == int(epoch * 0.33) or epoch == int(epoch * 0.66):\\n epoch_lr = epoch_lr * 0.1\\n\\n # get batch data\\n for idx in range(0, train_iteration):\\n batch_x = train_x[idx * batch_size:(idx + 1) * batch_size]\\n batch_y = train_y[idx * batch_size:(idx + 1) * batch_size]\\n\\n concat_data = convert_train_data_batch(batch_x)\\n anchor_label_r, neg_label_r = convert_train_label_batch(batch_y)\\n\\n train_feed_dict = {\\n inputs_placeholder: concat_data,\\n anchor_labels: anchor_label_r,\\n neg_labels: neg_label_r,\\n learning_rate: epoch_lr\\n }\\n\\n _, train_accuracy_v, anchor_loss_v, pos_loss_v, neg_loss_v, pos_pair_loss_v, \\\\\\n neg_pair_loss_v = sess.run([train_op, train_accuracy,\\n anchor_loss, pos_loss, neg_loss, pos_pair_loss,\\n neg_pair_loss], feed_dict=train_feed_dict)\\n\\n if idx % 10 == 0:\\n # display training status\\n print('Epoch: [%2d][%4d/%4d] Anchor Loss %.4f Pos Loss %.4f Neg Loss %.4f '\\n 'Pos Pair Loss %.4f Neg Pair Loss %.4f Prec %.4f\\\\t'\\n % (epoch, idx, train_iteration, anchor_loss_v, pos_loss_v, neg_loss_v,\\n pos_pair_loss_v, neg_pair_loss_v, train_accuracy_v))\\n\\n total_test_accuracy = 0\\n for idx in range(test_iteration):\\n batch_x = test_x[idx * batch_size:(idx + 1) * batch_size]\\n batch_y = test_y[idx * batch_size:(idx + 1) * batch_size]\\n\\n batch_x_r = convert_test_data_batch(batch_x)\\n batch_y_r = convert_test_label_batch(batch_y)\\n\\n test_feed_dict = {\\n inputs_placeholder: batch_x_r,\\n test_labels: batch_y_r\\n }\\n\\n test_accuracy_v = sess.run(test_accuracy, feed_dict=test_feed_dict)\\n\\n total_test_accuracy += test_accuracy_v\\n total_test_accuracy /= test_iteration\\n\\n # display training status\\n print(\\\"Epoch: [%2d/%2d]\\\\ttest_accuracy: %.2f\\\" \\\\\\n % (epoch, n_epochs, total_test_accuracy))\\n\\n # save model\\n if best_test_accuracy < total_test_accuracy:\\n best_test_accuracy = total_test_accuracy\\n new_saver.save(sess, os.path.join(model_path, 'SR_model_%2.4f.ckpt'\\n % best_test_accuracy))\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n unique_ids = features[\\\"unique_ids\\\"]\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n (subject_logits, property_logits, value_logits) = self.create_model(\\n bert_config=bert_config,\\n is_training=is_training,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n params=params,\\n use_one_hot_embeddings=use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n\\n initialized_variable_names = {}\\n scaffold_fn = None\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n seq_length = modeling.get_shape_list(input_ids)[1]\\n\\n def compute_loss(logits, positions, depth):\\n one_hot_positions = tf.one_hot(\\n positions, depth=depth, dtype=tf.float32)\\n log_probs = tf.nn.log_softmax(logits, axis=-1)\\n loss = -tf.reduce_mean(\\n tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\\n return loss\\n\\n # subject, property, value로 나오도록\\n subject_label = features[\\\"subject\\\"]\\n property_label = features[\\\"property\\\"]\\n value_label = features[\\\"value\\\"]\\n res_length = params[\\\"res_length\\\"]\\n ont_length = params[\\\"ont_length\\\"]\\n\\n subject_loss = compute_loss(subject_logits, subject_label, res_length)\\n property_loss = compute_loss(property_logits, property_label, ont_length)\\n value_loss = compute_loss(value_logits, value_label, res_length)\\n\\n total_loss = (subject_loss + property_loss + value_loss) / 3.0\\n\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\\n\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n scaffold_fn=scaffold_fn)\\n elif mode == tf.estimator.ModeKeys.PREDICT:\\n predictions = {\\n \\\"unique_ids\\\": unique_ids,\\n \\\"subject_logits\\\": subject_logits,\\n \\\"property_logits\\\": property_logits,\\n \\\"value_logits\\\": value_logits,\\n }\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)\\n else:\\n raise ValueError(\\n \\\"Only TRAIN and PREDICT modes are supported: %s\\\" % (mode))\\n\\n return output_spec\",\n \"def build(self):\\n\\n self.W = self.init([self.n_atom_input_feat, self.n_output])\\n self.b = model_ops.zeros(shape=[\\n self.n_output,\\n ])\\n\\n self.trainable_weights = self.W + self.b\",\n \"def main():\\n args = get_arguments()\\n\\n # Create network.\\n net = DeepLabLFOVModel()\\n\\n # Which variables to load.\\n trainable = tf.trainable_variables()\\n\\n \\n # Set up TF session and initialize variables. \\n config = tf.ConfigProto()\\n config.gpu_options.allow_growth = True\\n sess = tf.Session(config=config)\\n init = tf.initialize_all_variables()\\n \\n sess.run(init)\\n \\n # Load weights.\\n saver = tf.train.Saver(var_list=trainable)\\n load(saver, sess, args.model_weights)\\n\\n path_to_save_parmas = '../pretrained_params/'\\n net.save_convs_params(sess, path_to_save_parmas)\\n #writer = tf.summary.FileWriter('./deeplab_graph/', sess.graph)\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n (total_loss, per_example_loss, log_probs) = create_model(\\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\\n num_labels, use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n\\n scaffold_fn = None\\n initialized_variable_names = []\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\\n\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n scaffold_fn=scaffold_fn)\\n\\n elif mode == tf.estimator.ModeKeys.PREDICT:\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n mode=mode,\\n predictions={\\n \\\"log_probs\\\": log_probs,\\n \\\"label_ids\\\": label_ids,\\n },\\n scaffold_fn=scaffold_fn)\\n\\n else:\\n raise ValueError(\\n \\\"Only TRAIN and PREDICT modes are supported: %s\\\" % (mode))\\n\\n return output_spec\",\n \"def build(model_name):\\n return pretrain.factory.create(model_name)\",\n \"def build_model(cls, args, task):\\n global PAD_IDX, EOS_IDX\\n # make sure all arguments are present in older models\\n w2v_lm_architecture2(args)\\n\\n if not hasattr(args, \\\"max_source_positions\\\"):\\n args.max_source_positions = 2048\\n if not hasattr(args, \\\"max_target_positions\\\"):\\n args.max_target_positions = 2048\\n\\n tgt_dict = task.target_dictionary\\n PAD_IDX = tgt_dict.pad()\\n EOS_IDX = tgt_dict.eos()\\n\\n encoder = cls.build_encoder(args)\\n assigner = cls.build_assigner(args, encoder.d)\\n lm = cls.build_lm(args, task)\\n\\n return cls(args, encoder, assigner, lm)\",\n \"def make_model():\\n m = model_class(*argv[2:-1])\\n modelobj[\\\"model\\\"] = m\",\n \"def create(self) -> tf.keras.models.Model:\\n raise NotImplementedError()\",\n \"def __init__(self, model_name, logger=None, gpu_ids=None):\\n super().__init__(model_name, 'generator', logger, gpu_ids)\",\n \"def _tf2_ ( self , *args ) :\\n ##\\n if not hasattr ( self , '_wo2' ) : self._wo2 = _WO2_ ( self )\\n if not self._wo2 : self._wo2 = _WO2_ ( self )\\n ## \\n _wo = self._wo2\\n fun = ROOT.TF2 ( funID () , _wo , *args )\\n fun.SetNpx ( 100 ) \\n fun.SetNpy ( 100 ) \\n #\\n return fun\",\n \"def __init__(self , model_file_name ):\\n logging.set_verbosity(logging.ERROR)\\n with TheTFGraph.as_default():\\n with TheTFSession.as_default():\\n self.model = keras.models.load_model( model_file_name + \\\".hdf5\\\" , compile=False )\\n JSON = json.load( open(model_file_name + \\\".json\\\" ) )\\n self.all_sites = list(JSON['all_sites'])\\n self.all_errors = list(JSON['all_errors'])\\n self.all_actions = list(JSON['all_actions'])\\n self.IsBinary = bool(JSON['IsBinary'])\\n self.TiersOnly = bool(JSON['TiersOnly'])\\n self.Task = Task({} , \\\"TaskLoader\\\" , self)\\n self.Name = model_file_name.split('/')[-1]\\n self.ModelID = int( JSON['model'] )\\n self.InputTrainingDataID = int( JSON['trainingdata'])\\n\\n self.Prediction = Prediction.Prediction( self.ModelID , self.InputTrainingDataID )\",\n \"def model_fn(features,labels,mode,params):\\n input_q = features[\\\"input_q\\\"] # query feature vector\\n input_K = features[\\\"input_K\\\"] # Key set Matrix\\n input_v = features[\\\"input_v\\\"] # image visual feature vector\\n input_labels = features[\\\"input_labels\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n model = modeling.AMT(\\n config = config,\\n is_trainging = is_training, \\n scope = \\\"AMT\\\",\\n input_q = input_q,\\n input_K = input_K,\\n input_v = input_v\\n )\\n loss = model.loss\\n q_doc_rank = model.get_predict()\\n output_spec = None\\n scaffold_fn = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer()\\n output_spec = tf.estimator.EstimatorSpec(\\n mode = mode,\\n loss = loss,\\n train_op = train_op,\\n scaffold_fn = scaffold_fn)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n def metric_fn():\\n return 0\\n else:\\n output_spec = tf.estimator.EstimatorSpec(\\n mode = mode,\\n predictions = q_doc_rank,\\n scaffold_fn = scaffold_fn)\\n return output_spec\",\n \"def __init__(self,\\n model,\\n weights,\\n singa_ops,\\n keep_initializers_as_inputs=True):\\n super(SingaRep, self).__init__()\\n self.model = model\\n self.tensor_map = weights\\n self.keep_initializers_as_inputs = keep_initializers_as_inputs\\n # this each item of singa_ops is: ('name', 'op', 'handle', 'forward')\\n # the name is a string, op is OnnxNode,\\n # handle is Singa handle to store the tensor into singa operator\\n # the forward is singa autograd operator\\n self.singa_ops = singa_ops\",\n \"def initialize_model(\\n *,\\n model_def: nn.Module,\\n input_spec: Sequence[Union[Tuple[Tuple[int, ...], jnp.dtype],\\n Tuple[int, ...], None]],\\n config: ml_collections.ConfigDict,\\n rngs: Union[jnp.ndarray, Mapping[str, jnp.ndarray]],\\n) -> Tuple[PyTree, PyTree, int, Optional[float]]:\\n batch_size = (config.batch_size //\\n jax.device_count()) if config.get('batch_size') else None\\n dummy_input = []\\n for spec in input_spec:\\n if spec is not None:\\n in_st = debug_utils.input_spec_to_jax_shape_dtype_struct(\\n spec, batch_size=batch_size)\\n dummy_input.append(jnp.zeros(in_st.shape, in_st.dtype))\\n else:\\n dummy_input.append(None)\\n\\n # We want all parameters to be created in host RAM, not on any device, they'll\\n # be sent there later as needed, otherwise we already encountered two\\n # situations where we allocate them twice.\\n @functools.partial(jax.jit, backend='cpu')\\n def _initialize_model(rngs):\\n \\\"\\\"\\\"Initialization function to be jitted.\\\"\\\"\\\"\\n init_model_state, init_params = model_def.init(\\n rngs, *dummy_input, train=False, debug=False).pop('params')\\n # Set bias in the head to low value, such that loss is small initially.\\n if config.get('init_head_bias', None) is not None:\\n init_params = flax.core.unfreeze(init_params)\\n init_params['output_projection'] = optimizers.tree_map_with_names(\\n lambda p: jnp.full_like(p, config.init_head_bias),\\n init_params['output_projection'],\\n match_name_fn=lambda name: 'bias' in name)\\n init_params = flax.core.freeze(init_params)\\n return init_params, init_model_state\\n\\n if not isinstance(rngs, dict):\\n rngs = {'params': rngs}\\n init_params, init_model_state = _initialize_model(rngs)\\n # Pop out params rng:\\n rngs.pop('params')\\n\\n # Count number of trainable parameters:\\n num_trainable_params = debug_utils.log_param_shapes(init_params)\\n\\n # Count gflops:\\n count_flops = config.get('count_flops',\\n ml_collections.ConfigDict({'count_flops': True}))\\n if count_flops:\\n variables = {'params': init_params, **init_model_state}\\n flops = debug_utils.compute_flops(\\n flax_model_apply_fn=functools.partial(\\n model_def.apply, variables, train=False, debug=False, rngs=rngs),\\n input_spec=count_flops.get('input_spec', input_spec),\\n fuse_multiply_add=count_flops.get('fuse_multiply_add', True))\\n gflops = flops / (10**9)\\n else:\\n gflops = None\\n\\n return init_params, init_model_state, num_trainable_params, gflops\",\n \"def build_model(self, constructor, args):\\n dims = {'en': 300, 'es': 50}\\n dists = {'en': 'Normal',\\n 'es': 'Normal',}\\n z_dim = args.model_args.get('z_dim', 64)\\n h_dim = args.model_args.get('h_dim', 64)\\n n_layers = args.model_args.get('n_layers', 3)\\n gauss_out = (args.model != 'MultiDKS') \\n encoders = {'en': models.common.DeepGaussianMLP(dims['en'], z_dim, h_dim, n_layers),\\n 'es': models.common.DeepGaussianMLP(dims['es'], z_dim, h_dim, n_layers)}\\n decoders = {'en': models.common.DeepGaussianMLP(z_dim, dims['en'], h_dim, n_layers),\\n 'es': models.common.DeepGaussianMLP(z_dim, dims['es'], h_dim, n_layers)}\\n custom_mods = [m for m in ['en', 'es'] if m in args.modalities]\\n model = constructor(args.modalities,\\n dims=(dims[m] for m in args.modalities),\\n dists=[dists[m] for m in args.modalities],\\n encoders={m: encoders[m] for m in custom_mods},\\n decoders={m: decoders[m] for m in custom_mods},\\n z_dim=z_dim, h_dim=h_dim,\\n device=args.device, **args.model_args)\\n return model\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n # tf.logging.info(\\\"*** Features ***\\\")\\n # for name in sorted(features.keys()):\\n # tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n is_real_example = None\\n if \\\"is_real_example\\\" in features:\\n is_real_example = tf.cast(features[\\\"is_real_example\\\"], dtype=tf.float32)\\n else:\\n is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)\\n\\n is_training = mode == tf.estimator.ModeKeys.TRAIN\\n\\n model = modeling.BertModel(\\n config=bert_config,\\n is_training=is_training,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n token_type_ids=segment_ids,\\n use_one_hot_embeddings=use_one_hot_embeddings,\\n )\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n scaffold_fn = None\\n if init_checkpoint:\\n (\\n assignment_map,\\n initialized_variable_names,\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n\\n output_spec = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu\\n )\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode, loss=total_loss, train_op=train_op, scaffold=scaffold_fn\\n )\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n\\n def metric_fn(per_example_loss, label_ids, probabilities, is_real_example):\\n\\n logits_split = tf.split(probabilities, num_labels, axis=-1)\\n label_ids_split = tf.split(label_ids, num_labels, axis=-1)\\n # metrics change to auc of every class\\n eval_dict = {}\\n for j, logits in enumerate(logits_split):\\n label_id_ = tf.cast(label_ids_split[j], dtype=tf.int32)\\n current_auc, update_op_auc = tf.metrics.auc(label_id_, logits)\\n eval_dict[str(j)] = (current_auc, update_op_auc)\\n eval_dict[\\\"eval_loss\\\"] = tf.metrics.mean(values=per_example_loss)\\n return eval_dict\\n\\n\\n eval_metrics = metric_fn(\\n per_example_loss, label_ids, probabilities, is_real_example\\n )\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metric_ops=eval_metrics,\\n scaffold=scaffold_fn,\\n )\\n else:\\n out = {\\n \\\"input_ids\\\": input_ids,\\n \\\"label_ids\\\": label_ids,\\n }\\n all_layers = model.get_all_encoder_layers()\\n for (i, layer_index) in enumerate(layer_indexes):\\n out[\\\"layer_output_%d\\\" % i] = all_layers[layer_index]\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode, predictions=out, scaffold=scaffold_fn\\n )\\n return output_spec\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n is_prediction = (mode == tf.estimator.ModeKeys.PREDICT)\\n\\n (total_loss, per_example_loss, logits, probabilities) = create_model(\\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\\n num_labels, use_one_hot_embeddings, is_prediction)\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, False)\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n )\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)\\n accuracy = tf.metrics.accuracy(\\n labels=label_ids, predictions=predictions)\\n loss = tf.metrics.mean(values=per_example_loss)\\n eval_metrics = {\\n \\\"eval_accuracy\\\": accuracy,\\n \\\"eval_loss\\\": loss,\\n }\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metric_ops=eval_metrics\\n )\\n else:\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n predictions={\\\"probabilities\\\": probabilities},\\n export_outputs={'predict': tf.estimator.export.PredictOutput(outputs=probabilities)}\\n )\\n return output_spec\",\n \"def new(cls, args, src_meta, trg_meta, waitk_lagging, name=None):\\n # build source and target modality\\n src_modality, trg_modality = cls.build_modalities(args, src_meta, trg_meta)\\n encoder_params, decoder_params = {}, {}\\n for f in cls.class_or_method_args():\\n if f.name in args:\\n if f.name.startswith(\\\"encoder.\\\"):\\n encoder_params[f.name[8:]] = args[f.name]\\n elif f.name.startswith(\\\"decoder.\\\"):\\n decoder_params[f.name[8:]] = args[f.name]\\n # build encoder and decoder\\n encoder = build_encoder({\\n \\\"encoder.class\\\": \\\"TransformerEncoder\\\",\\n \\\"encoder.params\\\": encoder_params})\\n decoder = build_decoder({\\n \\\"decoder.class\\\": \\\"TransformerDecoder\\\",\\n \\\"decoder.params\\\": decoder_params})\\n model = cls(args, src_meta, trg_meta, src_modality, trg_modality,\\n encoder, decoder, name=name)\\n model.wait_k = waitk_lagging\\n _ = model({\\\"src\\\": tf.convert_to_tensor([[1, 2, 3]], tf.int64),\\n \\\"src_padding\\\": tf.convert_to_tensor([[0, 0., 0]], tf.float32),\\n \\\"trg_input\\\": tf.convert_to_tensor([[1, 2, 3]], tf.int64)})\\n return model\",\n \"def main(args):\\n # Use CUDA\\n use_cuda = args.use_cuda and torch.cuda.is_available()\\n device = torch.device(\\\"cuda\\\" if use_cuda else \\\"cpu\\\")\\n\\n # Fix random seed\\n torch.manual_seed(args.seed)\\n\\n # Generate token-to-index and index-to-token mapping\\n tok2id, id2tok = data_loader.build_or_load_vocab(\\n args.train, overwrite=False)\\n\\n print(\\\"*\\\" * 5)\\n print(args)\\n\\n # Create DataLoader() objects\\n params = {\\n \\\"batch_size\\\": args.batch_size,\\n \\\"collate_fn\\\": data_loader.collate_fn,\\n \\\"shuffle\\\": args.shuffle,\\n \\\"num_workers\\\": args.num_workers,\\n }\\n # train_dataset = data_loader.SNLIDataSet(args.train, tok2id)\\n # train_loader = torch.utils.data.DataLoader(train_dataset, **params)\\n val_dataset = data_loader.SNLIDataSet(args.val, tok2id)\\n val_loader = torch.utils.data.DataLoader(val_dataset, **params)\\n\\n # Initialize model\\n if args.model == \\\"rnn\\\": # RNN model\\n model = RNN(\\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\\n emb_dim=const.EMB_DIM, # Embedding dimensions\\n hidden_dim=args.hidden_dim, # Hidden dimensions\\n dropout_prob=args.dropout_prob, # Dropout probability\\n padding_idx=const.PAD_IDX, # Padding token index\\n num_classes=const.NUM_CLASSES, # Number of class labels\\n id2tok=id2tok, # Vocabulary\\n ).to(device)\\n # Load model weights from disk\\n model.load_state_dict(torch.load(const.MODELS + \\\"rnn.pt\\\"))\\n model.eval()\\n elif args.model == \\\"cnn\\\": # CNN model\\n model = CNN(\\n vocab_size=const.MAX_VOCAB_SIZE, # Vocabulary size\\n emb_dim=const.EMB_DIM, # Embedding dimensions\\n hidden_dim=args.hidden_dim, # Hidden dimensions\\n kernel_size=args.kernel_size, # Kernel size\\n dropout_prob=args.dropout_prob, # Dropout probability\\n padding_idx=const.PAD_IDX, # Padding token index\\n num_classes=const.NUM_CLASSES, # Number of class labels\\n id2tok=id2tok, # Vocabulary\\n ).to(device)\\n # Load model weights from disk\\n model.load_state_dict(torch.load(const.MODELS + \\\"cnn.pt\\\"))\\n model.eval()\\n else:\\n print(\\\"Invalid model specification, exiting\\\")\\n exit()\\n\\n # Criterion\\n criterion = torch.nn.CrossEntropyLoss()\\n # Model parameters\\n params = [p for p in model.parameters() if p.requires_grad]\\n\\n # Inspect correct/incorrect predictions\\n if args.inspect:\\n right, wrong = eval_model(val_loader, model, device, criterion,\\n inspect=True)\\n print(\\\"\\\\nValidation premises with correct predictions:\\\\n\\\")\\n for i, item in enumerate(right):\\n text = \\\" \\\".join([id2tok[idx] for idx in item if idx > 0])\\n print(\\\"#{}\\\\n {}\\\".format(i + 1, text))\\n print(\\\"\\\\nValidation premises with incorrect predictions:\\\\n\\\")\\n for i, item in enumerate(wrong):\\n text = \\\" \\\".join([id2tok[idx] for idx in item if idx > 0])\\n print(\\\"#{}\\\\n {}\\\".format(i + 1, text))\\n return\\n\\n # Validation\\n val_acc, _ = eval_model(val_loader, model, device, criterion)\\n print(\\\"\\\\n Validation accuracy: {}\\\".format(val_acc))\\n\\n print(\\\"*\\\" * 5 + \\\"\\\\n\\\")\",\n \"def __init__(self,args,train=True,reuse=None,model=None):\\n self.max_seq_len = args.max_seq_len\\n self.vocab_size = args.vocab_size\\n self.hidden_size = args.hidden_size\\n\\n initialize = model is None # whether to initialize variables\\n\\n # evice = \\\"/cpu:0\\\" if args.cpu else \\\"\\\"\\n self.graph = tf.Graph() if model is None else model.graph\\n self.session = tf.Session(graph=self.graph) \\\\\\n if model is None else model.session\\n\\n with self.graph.as_default(),\\\\\\n tf.variable_scope(\\\"LanguageModel\\\") as vs:\\n self._seq = tf.placeholder(\\n tf.int64,[None,self.max_seq_len])\\n self._len = tf.placeholder(\\n tf.int64,[None,])\\n\\n cell = tf.nn.rnn_cell.BasicLSTMCell(\\n self.hidden_size,state_is_tuple=True)\\n\\n # Running RNN through sequence\\n logit, _ = self.rnn_with_embedding(\\n cell,None,self._seq, self._len,reuse=reuse)\\n\\n logit_list = tf.unpack(tf.transpose(logit,[1,0,2]))\\n seq_list = tf.unpack(tf.transpose(self._seq,[1,0]))\\n seq_list = seq_list[1:]\\n\\n xent = self.softmax_xent_loss_sequence(\\n logit_list,seq_list,self._len,self.max_seq_len)\\n\\n self._cost = xent\\n\\n if train:\\n log(vs.name+\\\"/Adding optimizer\\\")\\n with tf.variable_scope(\\\"AdamOptimizer\\\"):\\n optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)\\n self._train_op = optimizer.minimize(self._cost)\\n\\n if initialize:\\n log(vs.name+\\\"/Initializing variables\\\")\\n self.session.run(tf.initialize_all_variables())\\n\\n log(\\\"Done with constructor.\\\")\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n # tf.logging.info(\\\"*** Features ***\\\")\\n # for name in sorted(features.keys()):\\n # tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n (total_loss, per_example_loss, logits, probabilities) = create_model(\\n bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,\\n num_labels, use_one_hot_embeddings)\\n\\n # output_spec = tf.contrib.tpu.TPUEstimatorSpec(\\n # mode=tf.estimator.ModeKeys.PREDICT,\\n # predictions=probabilities)\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=tf.estimator.ModeKeys.PREDICT,\\n predictions=probabilities\\n )\\n return output_spec\",\n \"def do_new(self, args):\\n model_name = questionary.text(\\\"Target name:\\\").ask()\\n model_name = model_name.replace(\\\" \\\", \\\"\\\")\\n\\n available_frameworks = list(CFState.get_instance().loaded_frameworks.keys())\\n framework_choice = questionary.select(\\\"Which framework?\\\", choices=available_frameworks).ask()\\n\\n if \\\"textattack\\\" in framework_choice:\\n framework = \\\"TextTarget\\\"\\n elif \\\"art\\\" in framework_choice:\\n framework = \\\"ArtTarget\\\"\\n else:\\n raise ValueError(\\\"invalid framework\\\")\\n\\n if framework == \\\"TextTarget\\\":\\n model_data_type = \\\"text\\\"\\n elif framework == \\\"ArtTarget\\\":\\n model_data_type = questionary.select(\\\"What data type?\\\", choices=[\\\"numpy\\\", \\\"image\\\"]).ask()\\n else:\\n raise ValueError(\\\"invalid framework\\\")\\n\\n if model_name not in os.listdir(config.targets_path):\\n try:\\n os.mkdir(f\\\"{config.targets_path}/{model_name}\\\")\\n open(f\\\"{config.targets_path}/{model_name}/__init__.py\\\", \\\"w\\\").close()\\n with open(f\\\"{config.targets_path}/{model_name}/{model_name}.py\\\", \\\"w\\\") as f:\\n f.write(\\n f\\\"\\\"\\\"\\n\\n# Generated by counterfit #\\n\\nfrom counterfit.core.targets import {framework}\\n\\nclass {model_name.capitalize()}({framework}):\\n model_name = \\\"{model_name.lower()}\\\"\\n model_data_type = \\\"{model_data_type}\\\"\\n model_endpoint = \\\"\\\"\\n model_input_shape = ()\\n model_output_classes = []\\n X = []\\n\\n def __init__(self):\\n self.X = []\\n\\n def __call__(self, x):\\n return x\\n\\\"\\\"\\\"\\n )\\n\\n CFState.get_instance().import_targets()\\n except Exception as e:\\n\\n self.pwarning(f\\\"\\\\n [!] Failed to write target file: {e}.\\\\n\\\")\\n\\n else:\\n self.pwarning(f\\\"\\\\n [!] {model_name} already exists. Choose a new name.\\\\n\\\")\",\n \"def build_model(self) -> nn.Module:\\n pass\",\n \"def _tf1_ ( self , *args ) :\\n #\\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\\n if not self._wo1 : self._wo1 = _WO1_ ( self )\\n ## \\n _wo = self._wo1 \\n fun = ROOT.TF1 ( funID() , _wo , *args )\\n fun.SetNpx ( 500 ) \\n #\\n return fun\",\n \"def model_fn_builder(num_labels, learning_rate, num_train_steps, num_warmup_steps):\\n def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n \\\"\\\"\\\"The `model_fn` for TPUEstimator.\\\"\\\"\\\"\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n label_ids = features[\\\"label_ids\\\"]\\n\\n is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\\n\\n # TRAIN and EVAL\\n if not is_predicting:\\n\\n (loss, predicted_labels, log_probs) = create_model(\\n is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\\n\\n train_op = create_optimizer(\\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)\\n\\n # Calculate evaluation metrics.\\n def metric_fn(label_ids, predicted_labels):\\n accuracy = tf.compat.v1.metrics.accuracy(label_ids, predicted_labels)\\n #f1_score = tf.contrib.metrics.f1_score(\\n # label_ids,\\n # predicted_labels)\\n #auc = tf.metrics.auc(\\n # label_ids,\\n # predicted_labels)\\n #recall = tf.metrics.recall(\\n # label_ids,\\n # predicted_labels)\\n #precision = tf.metrics.precision(\\n # label_ids,\\n # predicted_labels)\\n #true_pos = tf.metrics.true_positives(\\n # label_ids,\\n # predicted_labels)\\n #true_neg = tf.metrics.true_negatives(\\n # label_ids,\\n # predicted_labels)\\n #false_pos = tf.metrics.false_positives(\\n # label_ids,\\n # predicted_labels)\\n #false_neg = tf.metrics.false_negatives(\\n # label_ids,\\n # predicted_labels)\\n return {\\n \\\"eval_accuracy\\\": accuracy,\\n # \\\"f1_score\\\": f1_score,\\n #\\\"auc\\\": auc,\\n # \\\"precision\\\": precision,\\n # \\\"recall\\\": recall,\\n # \\\"true_positives\\\": true_pos,\\n # \\\"true_negatives\\\": true_neg,\\n # \\\"false_positives\\\": false_pos,\\n # \\\"false_negatives\\\": false_neg\\n }\\n\\n eval_metrics = metric_fn(label_ids, predicted_labels)\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n return tf.estimator.EstimatorSpec(mode=mode,\\n loss=loss,\\n train_op=train_op)\\n else:\\n return tf.estimator.EstimatorSpec(mode=mode,\\n loss=loss,\\n eval_metric_ops=eval_metrics)\\n else:\\n (predicted_labels, log_probs) = create_model(\\n is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)\\n\\n predictions = {\\n 'probabilities': log_probs,\\n 'labels': predicted_labels\\n }\\n return tf.estimator.EstimatorSpec(mode, predictions=predictions)\\n\\n # Return the actual model function in the closure\\n return model_fn\",\n \"def construct_model():\\n import lbann\\n\\n # Layer graph\\n input = lbann.Input(target_mode='N/A', name='inp_data')\\n # data is 64*64*4 images + 15 scalar + 5 param\\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\\\"0 16399 16404\\\",name='inp_slice')\\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\\n gt_y = lbann.Identity(inp_slice,name='gt_y')\\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\\n\\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\\n\\n z_dim = 20 #Latent space dim\\n\\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\\\"20\\\")\\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\\n\\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\\\"1 -1\\\"))\\n\\n layers = list(lbann.traverse_layer_graph(input))\\n # Setup objective function\\n weights = set()\\n src_layers = []\\n dst_layers = []\\n for l in layers:\\n if(l.weights and \\\"disc0\\\" in l.name and \\\"instance1\\\" in l.name):\\n src_layers.append(l.name)\\n #freeze weights in disc2\\n if(l.weights and \\\"disc1\\\" in l.name):\\n dst_layers.append(l.name)\\n for idx in range(len(l.weights)):\\n l.weights[idx].optimizer = lbann.NoOptimizer()\\n weights.update(l.weights)\\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\\n # Initialize check metric callback\\n metrics = [lbann.Metric(img_loss, name='recon_error')]\\n #pred_y = macc_models.MACCWAE.pred_y_name\\n callbacks = [lbann.CallbackPrint(),\\n lbann.CallbackTimer(),\\n lbann.CallbackSaveModel(dir=args.dump_models),\\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\\n destination_layers=list2str(dst_layers),\\n batch_interval=2)]\\n\\n if(args.ltfb_batch_interval > 0) :\\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\\n low_score_wins=True,\\n exchange_hyperparameters=True))\\n\\n # Construct model\\n return lbann.Model(args.num_epochs,\\n serialize_io=True,\\n weights=weights,\\n layers=layers,\\n metrics=metrics,\\n objective_function=obj,\\n callbacks=callbacks)\",\n \"def build_model_mobilenet(num_classes):\",\n \"def _create_model(self):\\n if torch.cuda.is_available():\\n model = torch.jit.load(self.torch_jit).cuda()\\n else:\\n model = torch.jit.load(self.torch_jit)\\n model.eval()\\n return model\",\n \"def __init__(self, model):\\r\\n self._tensorflow_session = model._tensorflow_session\\r\\n self._model = model\",\n \"def build_model(cls, args, task):\\n # from fairseq.tasks.multilingual_translation import MultilingualTranslationTask\\n # assert isinstance(task, MultilingualTranslationTask)\\n\\n # make sure all arguments are present in older models\\n base_architecture(args)\\n\\n if args.share_encoders:\\n args.share_encoder_embeddings = True\\n\\n ### nat model\\n # build shared embeddings (if applicable)\\n src_dict, tgt_dict = task.source_dictionary, task.target_dictionary\\n if args.share_all_embeddings:\\n if src_dict != tgt_dict:\\n raise ValueError(\\\"--share-all-embeddings requires a joined dictionary\\\")\\n if args.encoder_embed_dim != args.decoder_embed_dim:\\n raise ValueError(\\n \\\"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim\\\"\\n )\\n if args.decoder_embed_path and (\\n args.decoder_embed_path != args.encoder_embed_path\\n ):\\n raise ValueError(\\n \\\"--share-all-embeddings not compatible with --decoder-embed-path\\\"\\n )\\n encoder_embed_tokens = TransformerModel.build_embedding(\\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\\n )\\n decoder_embed_tokens = encoder_embed_tokens\\n args.share_decoder_input_output_embed = True\\n else:\\n encoder_embed_tokens = TransformerModel.build_embedding(\\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\\n )\\n decoder_embed_tokens = TransformerModel.build_embedding(\\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\\n )\\n\\n\\n student_cls = ARCH_MODEL_REGISTRY[args.student_arch]\\n encoder = student_cls.build_encoder(args, src_dict, encoder_embed_tokens)\\n decoder = student_cls.build_decoder(args, tgt_dict, decoder_embed_tokens)\\n student = student_cls(args,encoder,decoder)\\n\\n teacher_cls = ARCH_MODEL_REGISTRY[args.teacher_arch]\\n if not issubclass(teacher_cls, NATransformerModel):\\n teacher_cls = PatchedTransformerModel\\n\\n teacher_encoder = teacher_cls.build_encoder(\\n args, src_dict,\\n encoder_embed_tokens if args.share_encoder_embeddings else TransformerModel.build_embedding(\\n args, src_dict, args.encoder_embed_dim, args.encoder_embed_path\\n )\\n )\\n teacher_decoder = teacher_cls.build_decoder(\\n args, tgt_dict,\\n decoder_embed_tokens if args.share_decoder_embeddings else TransformerModel.build_embedding(\\n args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path\\n )\\n )\\n teacher = teacher_cls(args,teacher_encoder,teacher_decoder)\\n\\n return cls(args, student, teacher)\",\n \"def __init__(self, args, task_name, input_fields, conversion_name, cache=None, train_types_only=False):\\n self.input_fields = input_fields\\n self.task_name = task_name\\n self.conversion_name = conversion_name\\n self.conversion_dict = None\\n self.name_to_index_dict = None\\n self.cache = None\\n self.ints_to_strings = {}\\n self.label_vocab = {'[PAD]':0, '-':0, '_':0}\\n self.train_types_only = train_types_only\\n self.train_type_vocab = set()\",\n \"def _train_model(self):\\n raise NotImplementedError()\",\n \"def get_model(point_cloud, feature_cloud, color_cloud, s1, s2, s3, s4, g1, g2, g3, g4, c1, c2, c3, c4, t1, t2, t3, t4, is_training, num_class, use_color=0, bn_decay=None):\\n batch_size = point_cloud.get_shape()[0].value\\n num_point = point_cloud.get_shape()[1].value\\n l0_xyz = point_cloud\\n l0_points = None\\n\\n if use_color == 0:\\n l0_points = None\\n else:\\n l0_points = feature_cloud\\n if use_color == 2:\\n l0_cloud = TextureConv(color_cloud, is_training, bn_decay)\\n l0_points = tf.concat([l0_points,l0_cloud],axis=-1)\\n\\n # Layer 1\\n l1_xyz, l1_points = texture_geodesic_conv(s1, g1, c1, t1, l0_xyz, l0_points, npoint=1024, radius=0.1, conv_radius=0.1, conv_mlp = None, nsample=32, mlp=[32, 32, 64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_color=use_color)\\n l2_xyz, l2_points = texture_geodesic_conv(s2, g2, c2, t2, l1_xyz, l1_points, npoint=256, radius=0.2, conv_radius=0.2, conv_mlp = None, nsample=32, mlp=[64, 64, 128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2', use_color=use_color)\\n l3_xyz, l3_points = texture_geodesic_conv(s3, g3, c3, t3, l2_xyz, l2_points, npoint=64, radius=0.4, conv_radius=0.4, conv_mlp = None, nsample=32, mlp=[128, 128, 256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3', use_color=use_color)\\n l4_xyz, l4_points = texture_geodesic_conv(s4, g4, c4, t4, l3_xyz, l3_points, npoint=16, radius=0.8, conv_radius=0.8, conv_mlp = None, nsample=32, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4', use_color=use_color)\\n\\n # Feature Propagation layers\\n l3_points = texture_geodesic_tconv(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay, scope='fa_layer1')\\n l2_points = texture_geodesic_tconv(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer2')\\n l1_points = texture_geodesic_tconv(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer3')\\n l0_points = texture_geodesic_tconv(l0_xyz, l1_xyz, l0_points, l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer4')\\n\\n # FC layers\\n net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay)\\n net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1')\\n net = tf_util.conv1d(net, num_class, 1, padding='VALID', activation_fn=None, scope='fc2')\\n\\n return net\",\n \"def __init__(self, input_seq_batch, seq_length_batch, image_feat_batch,\\n num_vocab, num_choices, module_names, dropout_keep_prob,\\n scope='model', reuse=None):\\n\\n with tf.variable_scope(scope, reuse=reuse):\\n self.T_ctrl = cfg.MODEL.T_CTRL\\n\\n # Input unit\\n lstm_seq, q_encoding, embed_seq = input_unit.build_input_unit(\\n input_seq_batch, seq_length_batch, num_vocab)\\n kb_batch = input_unit.build_kb_batch(image_feat_batch)\\n\\n # Controller and NMN\\n num_module = len(module_names)\\n self.controller = controller.Controller(\\n lstm_seq, q_encoding, embed_seq, seq_length_batch, num_module)\\n self.c_list = self.controller.c_list\\n self.module_logits = self.controller.module_logits\\n self.module_probs = self.controller.module_probs\\n self.module_prob_list = self.controller.module_prob_list\\n self.nmn = nmn.NMN(\\n kb_batch, self.c_list, module_names, self.module_prob_list)\\n\\n # Output unit\\n if cfg.MODEL.BUILD_VQA:\\n self.vqa_scores = output_unit.build_output_unit_vqa(\\n q_encoding, self.nmn.mem_last, num_choices,\\n dropout_keep_prob=dropout_keep_prob)\\n if cfg.MODEL.BUILD_LOC:\\n loc_scores, bbox_offset, bbox_offset_fcn = \\\\\\n output_unit.build_output_unit_loc(\\n q_encoding, kb_batch, self.nmn.att_last)\\n self.loc_scores = loc_scores\\n self.bbox_offset = bbox_offset\\n self.bbox_offset_fcn = bbox_offset_fcn\\n\\n # Reconstruction loss\\n if cfg.MODEL.REC.USE_REC_LOSS:\\n rec_inputs = (self.module_logits if cfg.MODEL.REC.USE_LOGITS\\n else self.module_probs)\\n if cfg.MODEL.REC.USE_TXT_ATT:\\n rec_inputs = tf.concat(\\n [rec_inputs, tf.stack(self.c_list)], axis=-1)\\n self.rec_loss = output_unit.build_output_unit_rec(\\n rec_inputs, input_seq_batch, embed_seq, seq_length_batch,\\n num_vocab)\\n else:\\n self.rec_loss = tf.convert_to_tensor(0.)\\n\\n self.params = [\\n v for v in tf.trainable_variables() if scope in v.op.name]\\n self.l2_reg = tf.add_n(\\n [tf.nn.l2_loss(v) for v in self.params\\n if v.op.name.endswith('weights')])\\n\\n # tensors for visualization\\n self.vis_outputs = {\\n 'txt_att': # [N, T, S]\\n tf.transpose( # [S, N, T] -> [N, T, S]\\n tf.concat(self.controller.cv_list, axis=2), (1, 2, 0)),\\n 'att_stack': # [N, T, H, W, L]\\n tf.stack(self.nmn.att_stack_list, axis=1),\\n 'stack_ptr': # [N, T, L]\\n tf.stack(self.nmn.stack_ptr_list, axis=1),\\n 'module_prob': # [N, T, D]\\n tf.stack(self.module_prob_list, axis=1)}\\n if cfg.MODEL.BUILD_VQA:\\n self.vis_outputs['vqa_scores'] = self.vqa_scores\\n if cfg.MODEL.BUILD_LOC:\\n self.vis_outputs['loc_scores'] = self.loc_scores\\n self.vis_outputs['bbox_offset'] = self.bbox_offset\",\n \"def model_fn_builder(albert_config,num_labels,init_checkpoint,learning_rate,\\n num_train_steps,num_warmup_steps,\\n use_one_hot_embeddings,optimizer='adamw'):\\n\\n def model_fn(features,labels,mode,params):\\n \\\"\\\"\\\"The `model_fn` for TPUEstimator.\\\"\\\"\\\"\\n tf.logging.info('*** Features ***')\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s ,shape = %s\\\" % (name,features[name].shape))\\n\\n input_ids = features['input_ids']\\n input_mask = features['input_mask']\\n segment_ids = features['segment_ids']\\n label_ids = features['label_ids']\\n if 'is_real_example' in features:\\n is_real_example = tf.cast(features['is_real_example'],dtype=tf.float32)\\n else:\\n is_real_example = tf.ones(tf.shape(label_ids),dtype=tf.float32)\\n\\n is_training = (mode == tf_estimator.estimator.ModeKeys.TRAIN)\\n\\n (total_loss,per_example_loss,probabilities,predictions) = \\\\\\n create_model(albert_config,is_training,input_ids,input_mask,\\n segment_ids,label_ids,num_labels,\\n use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n if init_checkpoint:\\n (assignment_map,initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,init_checkpoint)\\n tf.train.init_from_checkpoint(init_checkpoint,assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf_estimator.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer(total_loss,\\n learning_rate,\\n num_train_steps,\\n num_warmup_steps,use_tpu=False)\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op,\\n )\\n elif mode == tf_estimator.estimator.ModeKeys.EVAL:\\n def metric_fn(per_example_loss,label_ids,logits,is_real_example):\\n accuracy = tf.metrics.accuracy(\\n labels=label_ids,predictions=predictions,\\n weights=is_real_example\\n )\\n loss = tf.metrics.mean(\\n values=per_example_loss,weights=is_real_example\\n )\\n return {\\n 'eval_accuracy':accuracy,\\n 'eval_loss':loss,\\n }\\n\\n eval_metrics = metric_fn(per_example_loss,label_ids,predictions,is_real_example)\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metric_ops=eval_metrics\\n )\\n else:\\n output_spec = tf_estimator.estimator.EstimatorSpec(\\n mode=mode,\\n predictions={\\n 'probabilities':probabilities,\\n 'predictions':predictions,\\n },\\n )\\n\\n return output_spec\\n\\n return model_fn\",\n \"def model_fn_builder(adj_mat, w2n, n2w, bert_config, init_checkpoint, learning_rate,\\n num_train_steps, num_warmup_steps, use_tpu,\\n use_one_hot_embeddings):\\n\\n def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n \\\"\\\"\\\"The `model_fn` for TPUEstimator.\\\"\\\"\\\"\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n # INFO:tensorflow: name = input_ids, shape = (?, 180)\\n # INFO:tensorflow: name = input_mask, shape = (?, 180)\\n # INFO:tensorflow: name = is_real_example, shape = (?,)\\n # INFO:tensorflow: name = label_ids, shape = (?,)\\n # INFO:tensorflow: name = masked_lm_ids, shape = (?, 180)\\n # INFO:tensorflow: name = masked_lm_positions, shape = (?, 180)\\n # INFO:tensorflow: name = masked_lm_weights, shape = (?, 180)\\n # INFO:tensorflow: name = segment_ids, shape = (?, 180)\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n masked_lm_positions = features[\\\"masked_lm_positions\\\"]\\n masked_lm_ids = features[\\\"masked_lm_ids\\\"]\\n masked_lm_weights = features[\\\"masked_lm_weights\\\"]\\n #next_sentence_labels = features[\\\"next_sentence_labels\\\"]\\n \\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n model = modeling.BertModel(\\n config=bert_config,\\n is_training=is_training,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n token_type_ids=segment_ids,\\n use_one_hot_embeddings=use_one_hot_embeddings)\\n \\n gcn_embedding = build_gcn_output(adj_mat, w2n, n2w, model.get_embedding_table(), bert_config, is_training)\\n \\n (masked_lm_loss,\\n masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(\\n bert_config, model.get_sequence_output(), gcn_embedding,\\n masked_lm_positions, masked_lm_ids, masked_lm_weights)\\n\\n\\n masked_lm_loss = tf.identity(masked_lm_loss, name=\\\"masked_lm_loss\\\")\\n\\n\\n total_loss = masked_lm_loss\\n\\n total_loss = tf.identity(total_loss, name='total_loss')\\n\\n tvars = tf.trainable_variables()\\n\\n initialized_variable_names = {}\\n scaffold_fn = None\\n if init_checkpoint and (not FLAGS.use_horovod or hvd.rank() == 0):\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n if not FLAGS.use_horovod or hvd.rank() == 0:\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer(\\n total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, FLAGS.use_horovod)\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n train_op=train_op)\\n return output_spec\\n elif mode == tf.estimator.ModeKeys.PREDICT:\\n\\n #def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\\n # masked_lm_weights):#, next_sentence_example_loss,\\n #next_sentence_log_probs, next_sentence_labels):\\n \\\"\\\"\\\"Computes the loss and accuracy of the model.\\\"\\\"\\\"\\n #masked_lm_log_probs = tf.reshape(masked_lm_log_probs,\\n # [-1, masked_lm_log_probs.shape[-1]])\\n masked_lm_predictions = tf.argmax(\\n masked_lm_log_probs, axis=-1, output_type=tf.int32)\\n # values=next_sentence_example_loss)\\n\\n predictions = {\\n \\\"input_ids\\\": tf.reshape(input_ids, [-1]),\\n \\\"predictions\\\": masked_lm_log_probs\\n }\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n predictions=predictions)\\n #eval_metric_ops=eval_metrics)\\n return output_spec\\n else:\\n def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\\n masked_lm_weights):\\n \\\"\\\"\\\"Computes the loss and accuracy of the model.\\\"\\\"\\\"\\n masked_lm_log_probs = tf.reshape(masked_lm_log_probs,\\n [-1, masked_lm_log_probs.shape[-1]])\\n masked_lm_predictions = tf.argmax(\\n masked_lm_log_probs, axis=-1, output_type=tf.int32)\\n masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])\\n masked_lm_ids = tf.reshape(masked_lm_ids, [-1])\\n masked_lm_weights = tf.reshape(masked_lm_weights, [-1])\\n masked_lm_accuracy = tf.metrics.accuracy(\\n labels=masked_lm_ids,\\n predictions=masked_lm_predictions,\\n weights=masked_lm_weights)\\n masked_lm_mean_loss = tf.metrics.mean(\\n values=masked_lm_example_loss, weights=masked_lm_weights)\\n\\n return {\\n \\\"masked_lm_accuracy\\\": masked_lm_accuracy,\\n \\\"masked_lm_loss\\\": masked_lm_mean_loss,\\n }\\n\\n eval_metrics = metric_fn(\\n masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,\\n masked_lm_weights)\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=total_loss,\\n eval_metric_ops=eval_metrics)\\n\\n return output_spec\\n\\n return model_fn\",\n \"def build_model(self, **kwargs):\\n raise NotImplementedError()\",\n \"def _main():\\n\\n # setup paths\\n json_model_path = osp.join(FLAGS.input_dir, FLAGS.json_model_fname)\\n weights_path = osp.join(FLAGS.input_dir, FLAGS.weights_fname)\\n save_path = osp.splitext(json_model_path)[0][:-6] + \\\"graph_w\\\" + str(weights_path.split(\\\"_\\\")[-1][:-3]) + \\\".pb\\\"\\n print(\\\"Loading Model: \\\" + json_model_path)\\n print(\\\"Loading Weights: \\\" + weights_path)\\n\\n # Set keras to test phase\\n k.set_learning_phase(0)\\n\\n # Load json and weights, then compile model\\n with open(json_model_path, 'r') as json_file:\\n loaded_model_json = json_file.read()\\n model = model_from_json(loaded_model_json)\\n model.load_weights(weights_path)\\n model.compile(loss='mse', optimizer='sgd')\\n\\n # Freeze graph\\n frozen_graph = freeze_session(k.get_session(), output_names=[out.op.name for out in model.outputs])\\n\\n # Write graph to protobuf file\\n tf.train.write_graph(frozen_graph, \\\"model\\\", save_path, as_text=False)\\n print(\\\"Written Graph to: \\\" + save_path)\",\n \"def train_model(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def __init__(self, emotion_model=os.path.join(execution_path,os.path.join(\\\"models\\\", \\\"emotion_model.hdf5\\\")),\\n cascade_file: str = None,\\n scale_factor: float = 1.3,\\n offsets: tuple = (20, 40),\\n compile: bool = False): \\n self.__scale_factor = scale_factor\\n self.__offsets = offsets\\n self.__labels={\\n 0: 'angry',\\n 1: 'disgust',\\n 2: 'fear',\\n 3: 'happy',\\n 4: 'sad',\\n 5: 'surprise',\\n 6: 'neutral'\\n }\\n \\n cascade_file=os.path.join(execution_path ,os.path.join(\\\"models\\\",\\\"haarcascade_frontalface_default.xml\\\")) \\n \\n self.__face_detector = cv2.CascadeClassifier(cascade_file)\\n\\n # Local Keras model\\n self.deployment = False\\n config = tf.ConfigProto(log_device_placement=False)\\n config.gpu_options.allow_growth = True\\n self.__emotion_classifier = load_model(emotion_model, compile=compile)\\n self.__emotion_classifier._make_predict_function()\\n self.__emotion_target_size = self.__emotion_classifier.input_shape[\\n 1:3]\",\n \"def model_fn(features, mode, params): # pylint: disable=unused-argument\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"input_type_ids\\\"]\\n # label_ids = features[\\\"label_ids\\\"]\\n vocab = vocab_list\\n vocab_size = len(vocab_list)\\n\\n is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)\\n\\n\\n tvars = tf.trainable_variables()\\n initialized_variable_names = {}\\n scaffold_fn = None\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n if use_tpu:\\n\\n def tpu_scaffold():\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n return tf.train.Scaffold()\\n\\n scaffold_fn = tpu_scaffold\\n else:\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n\\n\\n \\n # TRAIN\\n if not is_predicting:\\n\\n (loss, predictions, log_probs) = create_model(\\n is_predicting, input_ids, input_mask, segment_ids, vocab, vocab_size, bert_config, use_one_hot_embeddings)\\n\\n train_op = bert.optimization.create_optimizer(\\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=FLAGS.use_tpu)\\n\\n # if mode == tf.estimator.ModeKeys.TRAIN:\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(mode=mode,\\n loss=loss,\\n train_op=train_op,\\n scaffold_fn=scaffold_fn)\\n ## else:\\n # return tf.estimator.EstimatorSpec(mode=mode,\\n # loss=loss,\\n # eval_metric_ops=eval_metrics)\\n else:\\n (predictions, log_probs) = create_model(\\n is_predicting, input_ids, input_mask, segment_ids, vocab, vocab_size, bert_config, use_one_hot_embeddings)\\n\\n predictions = {\\n 'probabilities': log_probs,\\n 'predictions': predictions\\n }\\n output_spec = tf.contrib.tpu.TPUEstimatorSpec(mode, predictions=predictions, scaffold_fn=scaffold_fn)\\n\\n return output_spec if use_tpu else output_spec.as_estimator_spec()\",\n \"def model_fn_builder(config):\\n def model_fn(features,labels,mode,params):\\n \\\"\\\"\\\"The model_fn for Estimator\\\"\\\"\\\"\\n input_q = features[\\\"input_q\\\"] # query feature vector\\n input_K = features[\\\"input_K\\\"] # Key set Matrix\\n input_v = features[\\\"input_v\\\"] # image visual feature vector\\n input_labels = features[\\\"input_labels\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n model = modeling.AMT(\\n config = config,\\n is_trainging = is_training, \\n scope = \\\"AMT\\\",\\n input_q = input_q,\\n input_K = input_K,\\n input_v = input_v\\n )\\n loss = model.loss\\n q_doc_rank = model.get_predict()\\n output_spec = None\\n scaffold_fn = None\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer()\\n output_spec = tf.estimator.EstimatorSpec(\\n mode = mode,\\n loss = loss,\\n train_op = train_op,\\n scaffold_fn = scaffold_fn)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n def metric_fn():\\n return 0\\n else:\\n output_spec = tf.estimator.EstimatorSpec(\\n mode = mode,\\n predictions = q_doc_rank,\\n scaffold_fn = scaffold_fn)\\n return output_spec\\n return model_fn\",\n \"def __init__(self, dim_input=1, dim_output=1, test_num_updates=5):\\n self.dim_input = dim_input\\n self.dim_output = dim_output\\n self.update_lr = FLAGS.update_lr\\n self.meta_lr = tf.placeholder_with_default(FLAGS.meta_lr, ())\\n self.auto_lr = tf.placeholder_with_default(FLAGS.auto_lr, ())\\n \\n self.classification = False\\n self.test_num_updates = test_num_updates\\n self.dim_auto = 2 #This should be able to be arbitrary\\n if auto:\\n self.real_input = 39 # This is square root of the total (its a kernel)\\n #self.real_output = 40#self.dim_output\\n self.real_output = 39*39 # This should be the complete dimension out. \\n self.dim_input = 3*self.dim_auto #= 3*self.dim_auto \\n self.dim_output = self.dim_auto\\n #This is from each. \\n #if auto: self.dim_input, self.dim_output = self.dim_auto, self.dim_auto #If auto, pass in/out the dimension of the latent (auto_\\n if FLAGS.datasource == 'sinusoid':\\n self.dim_hidden = [40, 40,40]\\n self.loss_func = mse\\n self.forward = self.forward_fc\\n self.construct_weights = self.construct_fc_weights\\n elif FLAGS.datasource == 'omniglot' or FLAGS.datasource == 'miniimagenet':\\n self.loss_func = xent\\n self.classification = True\\n if FLAGS.conv:\\n self.dim_hidden = FLAGS.num_filters\\n self.forward = self.forward_conv\\n self.construct_weights = self.construct_conv_weights\\n else:\\n self.dim_hidden = [256, 128, 64, 64]\\n self.forward=self.forward_fc\\n self.construct_weights = self.construct_fc_weights\\n if FLAGS.datasource == 'miniimagenet':\\n self.channels = 3\\n else:\\n self.channels = 1\\n self.img_size = int(np.sqrt(self.dim_input/self.channels))\\n else:\\n raise ValueError('Unrecognized data source.')\",\n \"def build_model(cls, args, task):\\n # make sure that all args are properly defaulted (in case there are any new ones)\\n base_architecture(args)\\n\\n decoder_embed_dict = None\\n if args.decoder_embed_path:\\n decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)\\n utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)\\n\\n out_channels = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_channels, type=int)\\n kernel_sizes = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_kernel_sizes, type=int)\\n strides = speech_utils.eval_str_nested_list_or_tuple(args.encoder_conv_strides, type=int)\\n logger.info('input feature dimension: {}, channels: {}'.format(task.feat_dim, task.feat_in_channels))\\n assert task.feat_dim % task.feat_in_channels == 0\\n conv_layers = ConvBNReLU(\\n out_channels, kernel_sizes, strides, in_channels=task.feat_in_channels,\\n ) if out_channels is not None else None\\n\\n fconv_encoder_input_size = task.feat_dim // task.feat_in_channels\\n if conv_layers is not None:\\n for stride in strides:\\n if isinstance(stride, (list, tuple)):\\n assert len(stride) > 0\\n s = stride[1] if len(stride) > 1 else stride[0]\\n else:\\n assert isinstance(stride, int)\\n s = stride\\n fconv_encoder_input_size = (fconv_encoder_input_size + s - 1) // s\\n fconv_encoder_input_size *= out_channels[-1]\\n\\n encoder = SpeechFConvEncoder(\\n conv_layers_before=conv_layers,\\n input_size=fconv_encoder_input_size,\\n embed_dim=args.encoder_embed_dim,\\n convolutions=eval(args.encoder_layers),\\n dropout=args.dropout,\\n )\\n decoder = SpeechFConvDecoder(\\n dictionary=task.target_dictionary,\\n embed_dim=args.decoder_embed_dim,\\n embed_dict=decoder_embed_dict,\\n convolutions=eval(args.decoder_layers),\\n out_embed_dim=args.decoder_out_embed_dim,\\n attention=eval(args.decoder_attention),\\n dropout=args.dropout,\\n max_positions=args.max_target_positions,\\n share_embed=args.share_input_output_embed,\\n positional_embeddings=args.decoder_positional_embed,\\n )\\n return cls(encoder, decoder)\",\n \"def create_model(self, model_input, vocab_size, num_frames, **unused_params):\\n num_frames_t=num_frames\\n num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)\\n feature_size = model_input.get_shape().as_list()[2]\\n iterations=5#150\\n if FLAGS.is_train: \\n iterations=120\\n model_input = utils.SampleRandomFrames(model_input[:,15:,:], num_frames-15-15,\\n iterations)\\n # iterations=50\\n # model_input=model_input[:,20:-30:5,:]\\n model_input=model_input+tf.random_normal(shape=tf.shape(model_input), mean=0.0, stddev=1e-3, dtype=tf.float32)\\n\\n # print('model_input is', model_input)\\n # print('vocab_size is',vocab_size)\\n aggregated_model = getattr(video_level_models,\\n FLAGS.video_level_classifier_model)\\n\\n video_attention = AttentionLayers(1024,iterations,256)#256\\n audio_attention = AttentionLayers(128,iterations,256/4)#256/4\\n\\n model_input = slim.batch_norm(\\n model_input,\\n center=True,\\n scale=True,\\n is_training=True,\\n scope=\\\"model_input_bn\\\")\\n\\n with tf.variable_scope(\\\"video_Attention\\\"):\\n attention_video = video_attention.forward(model_input[:,:,0:1024]) \\n # print('vlad_video is',vlad_video)\\n with tf.variable_scope(\\\"audio_Attention\\\"):\\n attention_audio = audio_attention.forward(model_input[:,:,1024:])\\n\\n pooled=tf.concat([attention_video,attention_audio],axis=1)\\n #instance_att#tf.reduce_mean(pooledi,axis=1)\\n\\n print('pooled is',pooled)\\n\\n dr2 = tf.get_variable(\\\"dr2\\\",\\n [feature_size,1024],\\n initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))\\n pooled=tf.matmul(pooled,dr2)\\n\\n pooled = slim.batch_norm(\\n pooled,\\n center=True,\\n scale=True,\\n is_training=True,\\n scope=\\\"pooled_bn\\\")\\n\\n gating_weights = tf.get_variable(\\\"gating_weights_2\\\",\\n [1024, 1024],\\n initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(1024))) \\n gates = tf.matmul(pooled, gating_weights) \\n gates = slim.batch_norm(\\n gates,\\n center=True,\\n scale=True,\\n is_training=True,\\n scope=\\\"gating_bn\\\")\\n gates = tf.sigmoid(gates)\\n pooled = tf.multiply(pooled,gates)\\n\\n return aggregated_model().create_model(\\n model_input=pooled, vocab_size=vocab_size, **unused_params)\",\n \"def __init__(self, input_tensor_spec):\\n self._input_tensor_spec = input_tensor_spec\\n super().__init__()\",\n \"def build_model(cls, args, task):\\n # print(\\\"In build_model !!!\\\")\\n default_architecture(args)\\n assert args.load_hf_bert_from != ''\\n encoder = HuggingFaceBertEncoder(args, task.dictionary)\\n\\n return cls(args, encoder, task)\",\n \"def model_fn_builder():\\n \\n def model_fn(features, labels, mode, params):\\n # features name and shape\\n _info('*** Features ****')\\n for name in sorted(features.keys()):\\n tf.logging.info(' name = {}, shape = {}'.format(name, features[name].shape))\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n # get data\\n input_x = features['input_x']\\n input_mask = features['input_mask']\\n if is_training:\\n input_y = features['input_y']\\n seq_length = features['seq_length']\\n else:\\n input_y = None\\n seq_length = None\\n\\n # build encoder\\n model = BertEncoder(\\n config=cg.BertEncoderConfig,\\n is_training=is_training,\\n input_ids=input_x,\\n input_mask=input_mask)\\n embedding_table = model.get_embedding_table()\\n encoder_output = tf.reduce_sum(model.get_sequence_output(), axis=1)\\n\\n # build decoder\\n decoder_model = Decoder(\\n config=cg.DecoderConfig,\\n is_training=is_training,\\n encoder_state=encoder_output,\\n embedding_table=embedding_table,\\n decoder_intput_data=input_y,\\n seq_length_decoder_input_data=seq_length)\\n logits, sample_id, ppl_seq, ppl = decoder_model.get_decoder_output()\\n\\n if mode == tf.estimator.ModeKeys.PREDICT:\\n predictions = {'sample_id': sample_id, 'ppls': ppl_seq}\\n output_spec = tf.estimator.EstimatorSpec(mode, predictions=predictions)\\n else:\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n max_time = ft.get_shape_list(labels, expected_rank=2)[1]\\n target_weights = tf.sequence_mask(seq_length, max_time, dtype=logits.dtype)\\n batch_size = tf.cast(ft.get_shape_list(labels, expected_rank=2)[0], tf.float32)\\n\\n loss = tf.reduce_sum(\\n tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits) * target_weights) / batch_size\\n\\n learning_rate = tf.train.polynomial_decay(cg.learning_rate,\\n tf.train.get_or_create_global_step(),\\n cg.train_steps / 100,\\n end_learning_rate=1e-4,\\n power=1.0,\\n cycle=False)\\n\\n lr = tf.maximum(tf.constant(cg.lr_limit), learning_rate)\\n optimizer = tf.train.AdamOptimizer(lr, name='optimizer')\\n tvars = tf.trainable_variables()\\n gradients = tf.gradients(loss, tvars, colocate_gradients_with_ops=cg.colocate_gradients_with_ops)\\n clipped_gradients, _ = tf.clip_by_global_norm(gradients, 5.0)\\n train_op = optimizer.apply_gradients(zip(clipped_gradients, tvars), global_step=tf.train.get_global_step())\\n\\n\\n # this is excellent, because it could display the result each step, i.e., each step equals to batch_size.\\n # the output_spec, display the result every save checkpoints step.\\n logging_hook = tf.train.LoggingTensorHook({'loss' : loss, 'ppl': ppl, 'lr': lr}, every_n_iter=cg.print_info_interval)\\n\\n output_spec = tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[logging_hook])\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n # TODO\\n raise NotImplementedError\\n \\n return output_spec\\n \\n return model_fn\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n\\n bert_config = modeling.BertConfig.from_json_file(bert_config_file)\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n (total_loss, per_example_loss, log_probs) = create_model(\\n bert_config, is_training, input_ids, input_mask, segment_ids,\\n num_labels, False)\\n\\n tvars = tf.trainable_variables()\\n\\n initialized_variable_names = []\\n print('****TRYING TO LOAD FROM INIT CHECKPOINT %s****' % init_checkpoint)\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n print(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n print(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n predictions={\\n \\\"log_probs\\\": log_probs\\n })\\n\\n return output_spec\",\n \"def main(unused_argv):\\n model_params = sketch_rnn_model.get_default_hparams()\\n if FLAGS.hparams:\\n model_params.parse(FLAGS.hparams)\\n trainer(model_params)\",\n \"def model_fn(features, labels, mode, params): # pylint: disable=unused-argument\\n\\n tf.logging.info(\\\"*** Features ***\\\")\\n for name in sorted(features.keys()):\\n tf.logging.info(\\\" name = %s, shape = %s\\\" % (name, features[name].shape))\\n\\n unique_ids = features[\\\"unique_ids\\\"]\\n input_ids = features[\\\"input_ids\\\"]\\n input_mask = features[\\\"input_mask\\\"]\\n segment_ids = features[\\\"segment_ids\\\"]\\n\\n is_training = (mode == tf.estimator.ModeKeys.TRAIN)\\n\\n logits = create_model(\\n bert_config=bert_config,\\n is_training=is_training,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n use_one_hot_embeddings=use_one_hot_embeddings)\\n\\n tvars = tf.trainable_variables()\\n\\n initialized_variable_names = {}\\n\\n if init_checkpoint:\\n (assignment_map, initialized_variable_names\\n ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)\\n\\n tf.train.init_from_checkpoint(init_checkpoint, assignment_map)\\n\\n tf.logging.info(\\\"**** Trainable Variables ****\\\")\\n for var in tvars:\\n init_string = \\\"\\\"\\n if var.name in initialized_variable_names:\\n init_string = \\\", *INIT_FROM_CKPT*\\\"\\n tf.logging.info(\\\" name = %s, shape = %s%s\\\", var.name, var.shape,\\n init_string)\\n\\n output_spec = None\\n if mode == tf.estimator.ModeKeys.PREDICT:\\n predictions = {\\n \\\"unique_ids\\\": unique_ids,\\n \\\"prediction\\\": tf.argmax(logits, axis=-1),\\n }\\n output_spec = tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)\\n else:\\n def compute_loss(logits, positions):\\n one_hot_positions = tf.one_hot(\\n positions, depth=3, dtype=tf.float32)\\n log_probs = tf.nn.log_softmax(logits, axis=-1)\\n loss = -tf.reduce_mean(\\n tf.reduce_sum(one_hot_positions * log_probs, axis=-1))\\n return loss\\n\\n label = features[\\\"label\\\"]\\n\\n loss = compute_loss(logits, label)\\n predicted_classes = tf.argmax(logits, axis=-1)\\n accuracy = tf.metrics.accuracy(labels=label, predictions=predicted_classes, name='acc_op')\\n\\n # global global_acc_list\\n # global_acc_list.append(accuracy)\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n train_op = optimization.create_optimizer(\\n loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)\\n\\n metrics = {'accuracy': accuracy}\\n output_spec = tf.estimator.EstimatorSpec(\\n mode=mode,\\n loss=loss,\\n eval_metric_ops=metrics,\\n train_op=train_op)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n metrics = {'accuracy': accuracy}\\n output_spec = tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)\\n else:\\n raise ValueError(\\n \\\"Only TRAIN and PREDICT modes are supported: %s\\\" % (mode))\\n\\n return output_spec\",\n \"def create():\\n with torch.set_grad_enabled(False):\\n model = torch.hub.load(\\n \\\"pytorch/vision:v0.6.0\\\", \\\"vgg11\\\", pretrained=True).eval()\\n\\n with_cuda = torch.cuda.is_available()\\n if with_cuda:\\n model.to(\\\"cuda\\\")\\n else:\\n logging.warn(\\\"Running on CPU, no CUDA detected.\\\")\\n\\n def call(features):\\n images = features[\\\"image\\\"].numpy()\\n # Normalize according to the documentation. Note that the pro-processing\\n # will already have the range normalized to [0, 1].\\n mean = [0.485, 0.456, 0.406]\\n std = [0.229, 0.224, 0.225]\\n images_normalized = (images - mean) / std\\n # Reshape from [batch, h, w, c] -> [batch, c, h, w]\\n images_normalized_bchw = np.transpose(\\n images_normalized, [0, 3, 1, 2]).astype(np.float32).copy()\\n with torch.no_grad():\\n images_torch = torch.from_numpy(images_normalized_bchw)\\n if with_cuda:\\n images_torch = images_torch.to(\\\"cuda\\\")\\n logits = model(images_torch)\\n return torch.nn.functional.softmax(logits, dim=-1).cpu().numpy()\\n\\n preprocess_config = \\\"resize_small(256)|central_crop(224)|value_range(0,1)\\\"\\n preprocess_fn = pipeline_builder.get_preprocess_fn(\\n preprocess_config, remove_tpu_dtypes=False)\\n return call, preprocess_fn\",\n \"def __init__(self, pkl_path, joint_type='cocoplus', dtype=tf.float32):\\n # -- Load SMPL params --\\n with open(pkl_path, 'r') as f:\\n dd = pickle.load(f) \\n # Mean template vertices\\n self.v_template = tf.Variable(\\n undo_chumpy(dd['v_template']),\\n name='v_template',\\n dtype=dtype,\\n trainable=False)\\n # Size of mesh [Number of vertices, 3]\\n self.size = [self.v_template.shape[0].value, 3]\\n self.num_betas = dd['shapedirs'].shape[-1]\\n # Shape blend shape basis: 6980 x 3 x 10\\n # reshaped to 6980*30 x 10, transposed to 10x6980*3\\n shapedir = np.reshape(\\n undo_chumpy(dd['shapedirs']), [-1, self.num_betas]).T\\n self.shapedirs = tf.Variable(\\n shapedir, name='shapedirs', dtype=dtype, trainable=False)\\n\\n # Regressor for joint locations given shape - 6890 x 24\\n self.J_regressor = tf.Variable(\\n dd['J_regressor'].T.todense(),\\n name=\\\"J_regressor\\\",\\n dtype=dtype,\\n trainable=False)\\n\\n # Pose blend shape basis: 6890 x 3 x 207, reshaped to 6890*30 x 207\\n num_pose_basis = dd['posedirs'].shape[-1]\\n # 207 x 20670\\n posedirs = np.reshape(\\n undo_chumpy(dd['posedirs']), [-1, num_pose_basis]).T\\n self.posedirs = tf.Variable(\\n posedirs, name='posedirs', dtype=dtype, trainable=False)\\n\\n # indices of parents for each joints\\n self.parents = dd['kintree_table'][0].astype(np.int32)\\n\\n # LBS weights\\n self.weights = tf.Variable(\\n undo_chumpy(dd['weights']),\\n name='lbs_weights',\\n dtype=dtype,\\n trainable=False)\\n\\n # This returns 19 keypoints: 6890 x 19\\n self.joint_regressor = tf.Variable(\\n dd['cocoplus_regressor'].T.todense(),\\n name=\\\"cocoplus_regressor\\\",\\n dtype=dtype,\\n trainable=False)\\n if joint_type == 'lsp': # 14 LSP joints!\\n self.joint_regressor = self.joint_regressor[:, :14]\\n\\n if joint_type not in ['cocoplus', 'lsp']:\\n print('BAD!! Unknown joint type: %s, it must be either \\\"cocoplus\\\" or \\\"lsp\\\"' % joint_type)\\n import ipdb\\n ipdb.set_trace()\",\n \"def create_model(model_name, random_state, epoch, device, log_path, **hparams):\\n model = eval(f'{model_name}')(\\n **hparams, epoch=int(epoch), random_state=random_state, device=device,\\n log_path=log_path\\n )\\n\\n return model\",\n \"def init_tensors(self, sample, *args):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67856354","0.6725397","0.6558513","0.65427756","0.6461063","0.64487284","0.63853717","0.6380155","0.6334473","0.63143593","0.62861764","0.6284722","0.6238022","0.62292343","0.6205176","0.6196909","0.6196012","0.61855704","0.6158863","0.61315936","0.6114675","0.6085014","0.60655206","0.6055188","0.6049067","0.60445416","0.60302657","0.6014607","0.6008279","0.60066134","0.6005976","0.6005338","0.60011613","0.59996986","0.59936076","0.5989531","0.5988167","0.59861606","0.59858096","0.59658843","0.5962221","0.5951206","0.5950305","0.5948262","0.5940291","0.59347624","0.59341013","0.5930249","0.5927861","0.5921328","0.5918885","0.5912987","0.59122497","0.5910063","0.58976513","0.5894516","0.58898926","0.5887741","0.5883058","0.5880978","0.58734435","0.58687097","0.58606255","0.5859897","0.585927","0.585884","0.5851366","0.58509743","0.5848814","0.58479184","0.58303225","0.5829106","0.58270127","0.58184695","0.58116716","0.5796363","0.57961595","0.5790042","0.57888544","0.5788784","0.57836086","0.5782435","0.5780871","0.57791823","0.57773596","0.5775049","0.5773353","0.5771105","0.5767814","0.5766412","0.576285","0.5757329","0.5756639","0.5751006","0.5746874","0.57454574","0.5745176","0.5743617","0.57432854","0.5736373","0.5731714"],"string":"[\n \"0.67856354\",\n \"0.6725397\",\n \"0.6558513\",\n \"0.65427756\",\n \"0.6461063\",\n \"0.64487284\",\n \"0.63853717\",\n \"0.6380155\",\n \"0.6334473\",\n \"0.63143593\",\n \"0.62861764\",\n \"0.6284722\",\n \"0.6238022\",\n \"0.62292343\",\n \"0.6205176\",\n \"0.6196909\",\n \"0.6196012\",\n \"0.61855704\",\n \"0.6158863\",\n \"0.61315936\",\n \"0.6114675\",\n \"0.6085014\",\n \"0.60655206\",\n \"0.6055188\",\n \"0.6049067\",\n \"0.60445416\",\n \"0.60302657\",\n \"0.6014607\",\n \"0.6008279\",\n \"0.60066134\",\n \"0.6005976\",\n \"0.6005338\",\n \"0.60011613\",\n \"0.59996986\",\n \"0.59936076\",\n \"0.5989531\",\n \"0.5988167\",\n \"0.59861606\",\n \"0.59858096\",\n \"0.59658843\",\n \"0.5962221\",\n \"0.5951206\",\n \"0.5950305\",\n \"0.5948262\",\n \"0.5940291\",\n \"0.59347624\",\n \"0.59341013\",\n \"0.5930249\",\n \"0.5927861\",\n \"0.5921328\",\n \"0.5918885\",\n \"0.5912987\",\n \"0.59122497\",\n \"0.5910063\",\n \"0.58976513\",\n \"0.5894516\",\n \"0.58898926\",\n \"0.5887741\",\n \"0.5883058\",\n \"0.5880978\",\n \"0.58734435\",\n \"0.58687097\",\n \"0.58606255\",\n \"0.5859897\",\n \"0.585927\",\n \"0.585884\",\n \"0.5851366\",\n \"0.58509743\",\n \"0.5848814\",\n \"0.58479184\",\n \"0.58303225\",\n \"0.5829106\",\n \"0.58270127\",\n \"0.58184695\",\n \"0.58116716\",\n \"0.5796363\",\n \"0.57961595\",\n \"0.5790042\",\n \"0.57888544\",\n \"0.5788784\",\n \"0.57836086\",\n \"0.5782435\",\n \"0.5780871\",\n \"0.57791823\",\n \"0.57773596\",\n \"0.5775049\",\n \"0.5773353\",\n \"0.5771105\",\n \"0.5767814\",\n \"0.5766412\",\n \"0.576285\",\n \"0.5757329\",\n \"0.5756639\",\n \"0.5751006\",\n \"0.5746874\",\n \"0.57454574\",\n \"0.5745176\",\n \"0.5743617\",\n \"0.57432854\",\n \"0.5736373\",\n \"0.5731714\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94588,"cells":{"query":{"kind":"string","value":"Return parameter details for dim, nIter, lamb and alph"},"document":{"kind":"string","value":"def paramDetails(cls):\n return {\n 'dim': (10, 20, 2, 20),\n 'nIter': (1, 10, 2, 5),\n 'lamb': (.1, 1., .1, .05),\n 'alph': (30, 50, 5, 40)\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setParams(self,dim=20, nIter=5, lamb=0.05, alph=40):\n self._dim = dim\n self._nIter = nIter\n self._lamb = lamb\n self._alph = alph","def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.ncomp))\n print('galactic parameter = {}'.format(self.scaling_field))\n print('reference height = {}'.format(self.z0))\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\n params = getattr(self,p)\n print(p)\n for k,v in params.items():\n print(' {} = {}'.format(k,v))","def params(self):\n return {'out_dim': self.out_dim,\n 'act_fn': self.act_fn,\n 'use_bias': self.use_bias,\n 'idx': self.idx}","def inspect_state(self):\n for name in self._param_store.get_all_param_names():\n self._logger.info(\"Param [%s]: %r\", name,\n pyro.param(name).data.numpy())","def get_pars(self):\n return [self.z, self.b, self.logN]","def param_info():\n\n\tgizmo_names = syn.getGizmoNames()\n\n\tfor gizmo in gizmo_names:\n\t\tparams = syn.getParameterNames(gizmo)\n\t#doesnt get all parameters from gizmos i.e. WaveFreq\n\n\t# get all info on the 'WaveFreq' parameter\n\tGIZMO = 'aStim2'\n\tPARAMETER = 'WaveFreq'\n\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # get the array size (should be 100)\n\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\n\t#\n\t# # write values 1 to 50 in second half of buffer\n\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\n\t#\n\t# # read all values from buffer\n\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\n\t#\n\t# # get all info on the 'Go' parameter\n\t# PARAMETER = 'Go'\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # flip the switch\n\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\n\t#\n\t# # check the value\n\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\n\tprint('value =', freq)\n\tfreq = [freq]\n\n\t# also verify visually that the switch slipped in the run\n\t# time interface. This state change will be logged just\n\t# like any other variable change and saved with the runtime\n\t# state.\n\n\tnumTrials = 5 #total number of trials across stimuli\n\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\n\n\t# flash parameters\n\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\n\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\n\n\t# auditory parameters\n\tduration = [.005] # in seconds; pulseDur in TDT\n\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\n\n\t# Auditory on (T/F? if T then A+V, if F then Visual only)\n\tstims = {0: \"auditory_only\",\n\t\t\t 1: \"visual_only\",\n\t\t\t 2: \"A+V\"\n\t\t\t }\n\n\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\n\t\t\t\t\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\n\texper.run_experiment()","def getParam(self):\n return self.__alpha0, self.__alpha1, self.__beta, self.__eta","def params(self) -> Iterable[sympy.Symbol]:\n for i in range(self.iterations):\n for p in range(len(self.qubits)):\n if (self.include_all_z or not\n numpy.isclose(self.orbital_energies[p], 0)):\n yield LetterWithSubscripts('U', p, i)\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\n if (self.include_all_cz or not\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\n yield LetterWithSubscripts('V', p, q, i)","def give_block_param_info(self):\n\n nb_block=0\n nb_values=[]\n k=0\n while 1:\n k+=1\n try:\n self.info['mw_parameter'][str(10*k+1)]\n except:\n break\n nb_block+=1\n if type(self.info['mw_parameter'][str(10*k+3)])==list:\n nb_values.append(len(self.info['mw_parameter'][str(10*k+3)]))\n else:\n nb_values.append(1)\n\n return nb_block,nb_values","def parameters(self):","def param(self,name,i):\n state = self.getstate(name)\n x,C = state.vec,state.cov\n cc = C[i,i]\n if (cc>0.): cc=sqrt(cc)\n xx,cc = x[i],cc\n debug('kfnode.param ',(name,xx,cc))\n return xx,cc","def _determine_parameters(self, paramdict):\n for var in paramdict:\n if is_dimensionless(paramdict[var]):\n self._all_params_unit[var] = \"none\"\n yield lems.Parameter(var, \"none\")\n else:\n dim = _determine_dimension(paramdict[var])\n self._all_params_unit[var] = dim\n yield lems.Parameter(var, dim)","def parameters(self):\n return {\"P\": self.P,\n \"T\": self.T}","def lattice_parameters(self):\n return self.a, self.b, self.c, self.alpha, self.beta, self.gamma","def get_layer_var_names(self):\n return(self.params)","def parameters(self):\n return self.pars","def params(dim):\r\n m = 3\r\n s = 1\r\n q = 2 ** (m - 1)\r\n while s < dim:\r\n m += 1\r\n s = m + math.factorial(m - 1) / (2 * math.factorial(m - 3))\r\n q = 2 ** (m - 1)\r\n\r\n return (\r\n m, q, s - dim)","def getParams(self):\n return self.trainError, self.trainAcc, self.w","def getParameters(self):\n\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\n\n return (current_params)","def parameters(self):\n return {\"W\": self.W,\n \"T\": self.T,\n \"P\": self.P}","def potential_parameters(cls):\n return [\"k\", \"length\"]","def get_iperparams(self):\n\t\treturn (self.D, self.K)","def get_params(self):\n return {\n \"nspecies\": self.nspecies,\n \"lmax\": self.lmax,\n \"nmax\": self.nmax,\n \"rcut\": self.rcut,\n \"sigma\": self.sigma,\n \"trans_width\": self.trans_width\n }","def get_hyperparameter_names():\n params = ['mu', 'nu', 'r', 's']\n return params","def get_params(self):","def parameters(self):\n params = []\n for layer in (self.conv1, self.conv2, self.conv3, self.conv4, self.dense1, self.dense2):\n params += list(layer.parameters)\n return params","def get_hyperparams(self):","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict['method'] = self.method\n paramDict['dimension'] = self.dimension\n paramDict['rank'] = self.rank\n paramDict['mu'] = self.mu\n paramDict['covariance'] = self.covariance\n return paramDict","def params_desc(self):\n return \"{}/{}/{}/{}\".format(\n self.learning_rate, self.movement, self.milestones, self.gamma\n )","def get_parameters(self):\n return self.sess.run(self.A_symm)","def get_params(self, deep=False):\n return {\"alpha\": self.alpha, \"beta\": self.beta, \"gamma\": self.gamma, \"W\": self.W, \"bias\": self.bias, \"add_bias\": self.add_bias, \"opts\": self.opts}","def _get_parameters(self):\n return (self.SYMBOL, self.parameterArray())","def print_param_names(model):\n for (param_name, param) in model.get_parameters().items():\n print(param_name, param.shape)","def get_params(self) -> np.array:\n pass","def parameters(self):\n #print \"in instrument.parameter()\"\n return self._params","def def_paramt():\n Zeff = 1.0\n amu = 2.0\n mf = mp*amu\n return Zeff, amu,mf","def params(self):\n return self._pars","def params(self):\n return {'kernel_size': self.kernel_size,\n 'stride': self.stride,\n 'n_kernel': self.n_kernel,\n 'padding': self.padding,\n 'act_fn': self.act_fn,\n 'output_shape': self.output_shape,\n 'w_init_fn': self.w_init_fn,\n 'use_bias': self.use_bias,\n 'idx': self.idx}","def getParameters(self):\n params = []\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\n # we do not get param of output module\n l = list(m.parameters())\n params.extend(l)\n\n one_dim = [p.view(p.numel()) for p in params]\n params = F.torch.cat(one_dim)\n return params","def getParameters(self):\n params = []\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\n # we do not get param of output module\n l = list(m.parameters())\n params.extend(l)\n\n one_dim = [p.view(p.numel()) for p in params]\n params = F.torch.cat(one_dim)\n return params","def getParams(self):\n return self.W, self.b","def param(self):\r\n paramlist = []\r\n gradlist = []\r\n\r\n for layer in self.layers:\r\n try:\r\n layer_param, layer_grad = layer.param()\r\n paramlist = paramlist + layer_param\r\n gradlist = gradlist + layer_grad\r\n except ValueError:\r\n continue\r\n return paramlist, gradlist","def params(self):\n return {'kernel_size': self.kernel_size,\n 'stride': self.stride,\n 'n_kernel': self.n_kernel,\n 'padding': self.padding,\n 'act_fn': self.act_fn,\n 'w_init_fn': self.w_init_fn,\n 'resize': self.resize,\n 'use_bias': self.use_bias,\n 'atrous': self.atrous,\n 'idx': self.idx}","def display_parameters(self):\n\n self.logging.debug(\"============\")\n for attr in self.parm_list:\n self.logging.debug(attr.label + \" (\" + attr.when + \")\" + \" = \" + str(attr.value))\n self.logging.debug(\"============\")","def print_params_shape(scope, param_type):\n shapes = [var.get_shape().as_list() for var in get_trainable_vars(scope)]\n nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in shapes])\n print(' {} shapes: {}'.format(param_type, shapes))\n print(' {} params: {}'.format(param_type, nb_params))","def num_params(self):","def _get_params(self):\r\n return self.k._get_params()","def getParameters(self):\n\t\td = AdaptiveBatAlgorithm.getParameters(self)\n\t\td.update({\n\t\t\t'A_l': self.A_l,\n\t\t\t'A_u': self.A_u,\n\t\t\t'r_l': self.r_l,\n\t\t\t'r_u': self.r_u,\n\t\t\t'tao_1': self.tao_1,\n\t\t\t'tao_2': self.tao_2\n\t\t})\n\t\treturn d","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def get_params(self):\n return self.arr","def printParameters(self):\n print(\"----------Model Parameters----------\")\n print(\"Initial Conv. Depth : \" + str(self.conv_depth))\n print(\"Number of Classes : \" + str(self.n_classes))\n print(\"Dropout : \" + str(self.dropout))\n print(\"Activation Function : Relu\")\n print(\"Input Shape : \" + str(self.input_shape))\n print(\"Batch Size : \" + str(self.batch_size))\n print(\"--------Optimizer Parameters--------\")\n print(\"Learning Rate : \" + str(self.optimizer.lr))\n print(\"Momentum : \" + str(self.optimizer.momentum))\n print(\"Initial Decay : \" + str(self.optimizer.initial_decay))","def parameter_names(self) -> List[str]:","def params(self):\n return {'cfg': self.cfg,\n 'momentum': self.momentum,\n 'center': self.center,\n 'scale': self.scale,\n 'epsilon': self.epsilon,\n 'act_fn': self.act_fn}","def params():\n raise NotImplementedError","def get_params(self):\n return self.w, self.b","def parameters(self):\n pass","def parameterNames(self, p_int): # real signature unknown; restored from __doc__\n return []","def parameters(self):\n return NeuralNetwork.flatten([module.parameters() for module in self.modules])","def parameters(self):\n return self.vars","def __init__(self, N, n):\n #adam params\n self.beta_1 = 0.9\n self.beta_2 = 0.999\n self.epsilon = 1e-8\n #not adam params\n self.arr = np.zeros((N, 2*n+1)) #param array\n self.max = sum([psi.lbd(p + 1, gamma, n) for p in range(n)])\n self.loss_memory = []\n self.eta_memory = []\n self.grad_memory = []\n self.N = N\n self.n = n","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def printfunc(self, params, iter, resid, *args, **kwargs):\n\n print(iter) \n print(params.valuesdict())","def getParams(self):\n\n\t\tparams = {\"Nparticles\":self.__Nparticles,\"Nkicks\":self.__Nkicks,\"kappa\":self.__kappa, \"eta\":self.__eta,\"gamma\":self.__gamma, \"omega\":self.__omega,\n\t\t\"Kbt\":self.__Kbt, \"tk\":self.__tk}\n\n\t\treturn params","def algorithm_parameters(alg):\n if alg in list(SEM_TYPE.keys()):\n return simulate_parameters(alg)\n\n param_dict = dict()\n\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].__init__)\n if param is not None:\n param_len = len(param.args)\n if param.defaults:\n if 'input_dim' in param.args:\n param_dict.update({'input_dim': None})\n for index, value in enumerate(reversed(param.defaults)):\n if not isfunction(value) and (value is not None):\n param_dict.update(\n {param.args[param_len - index - 1]: value})\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].learn)\n if param is not None:\n param_len = len(param.args)\n if param_len > 2:\n if 'rank' in param.args:\n param_dict.update({'rank': None})\n return param_dict","def get_params(self):\n raise NotImplementedError","def param_init(self, sig=0.01):\n self.rhos = np.ones(self.Ndim)\n self.a = np.random.rand(self.Ndim, self.Nhidden)\n self.c = np.random.rand(self.Nhidden)\n self.W = np.random.randn(self.Nhidden, self.Ndim) * sig\n self.alphas = np.zeros((self.Ndim, self.Ncomponents))\n self.mus = np.zeros((self.Ndim, self.Ncomponents))\n self.sigmas = np.zeros((self.Ndim, self.Ncomponents))\n self.optimize_params = [self.rhos, self.c, self.W]\n\n types = ['alpha', 'mu', 'sigma']\n self.bs = {}\n self.Vs = {}\n for t in types:\n self.bs[t] = np.random.randn(self.Ndim, self.Ncomponents) * sig\n self.Vs[t] = np.random.randn(self.Ndim, self.Nhidden,\n self.Ncomponents) * sig\n self.optimize_params.append(self.bs[t])\n self.optimize_params.append(self.Vs[t])","def getInitParams(self):\n paramDict = super().getInitParams()\n paramDict[f'OutStream Available {self.dim}D :'] = self.availableOutStreamTypes[self.dim]\n paramDict['Plot is '] = str(self.dim) + 'D'\n for index in range(len(self.sourceName)):\n paramDict['Source Name ' + str(index) + ' :'] = self.sourceName[index]\n\n return paramDict","def getParameters(self): #$NON-NLS-1$\r","def derived_parameters(cls):\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']","def get_parameters(self):\n return self.sess.run(self.A)","def _get_params_summary(self, alpha=0.1):\n\n # TODO: Acknowledge that this code was modified from the statsmodels package\n\n results = self._model.fit()\n\n def forg(x, prec=3):\n if prec == 3:\n # for 3 decimals\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\n return '%9.3g' % x\n else:\n return '%9.3f' % x\n elif prec == 4:\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\n return '%10.4g' % x\n else:\n return '%10.4f' % x\n else:\n raise NotImplementedError\n\n # Parameters part of the summary table\n conf_int = results.conf_int(alpha)\n\n # Dictionary to store the header names for the parameter part of the\n # summary table. look up by modeltype\n alp = str((1 - alpha) * 100) + '%'\n\n param_header = ['coef', 'std err', 't', 'P>|t|',\n '[' + alp + ' Conf. Int.]']\n\n xname = self._model.exog_names\n\n params_stubs = xname\n\n exog_idx = range(len(xname))\n\n # center confidence intervals if they are unequal lengths\n confint = [\"%s %s\" % tuple(map(forg, conf_int.ix[i])) for i in exog_idx]\n len_ci = list(map(len, confint))\n max_ci = max(len_ci)\n min_ci = min(len_ci)\n\n if min_ci < max_ci:\n confint = [ci.center(max_ci) for ci in confint]\n\n # explicit f/g formatting, now uses forg, f or g depending on values\n params_data = zip([forg(results.params[i], prec=4) for i in exog_idx],\n [forg(results.bse[i]) for i in exog_idx],\n [forg(results.tvalues[i]) for i in exog_idx],\n # [\"%#6.3f\" % (results.pvalues[i]) for i in exog_idx],\n [\"%#6.3g\" % (results.pvalues[i]) for i in exog_idx],\n confint\n )\n params_data = list(params_data)\n parameter_table = SimpleTable(params_data,\n param_header,\n params_stubs,\n txt_fmt=fmt_params\n )\n\n if results.params.shape[0] > 2:\n vif_table = self._get_vif_table()\n parameter_table.extend_right(vif_table)\n\n return parameter_table","def parameterNode(self):\r\n # framework\r\n profbox()\r\n return self.parameterNode","def parameters(self):\n return []","def _LayerParams(ii):\n if isinstance(p.transformer_layer_params_tpl, list):\n factor = p.num_layers // len(p.transformer_layer_params_tpl)\n i = ii // factor\n p_ii = p.transformer_layer_params_tpl[i].Copy()\n else:\n p_ii = p.transformer_layer_params_tpl.Copy()\n p_ii.name = 'layer_%d' % ii\n p_ii.has_aux_atten = p.has_aux_atten\n p_ii.mask_self_atten = p.mask_self_atten\n p_ii.input_dim = p.mdl_dim or p_ii.input_dim\n p_ii.output_dim = p.mdl_dim or p_ii.output_dim\n p_ii.packed_input = p.packed_input\n if (not isinstance(p_ii.tr_atten_tpl.num_heads, list) and\n p.num_atten_heads is not None):\n p_ii.tr_atten_tpl.num_heads = p.num_atten_heads\n if p.dropout_prob is not None:\n p_ii.tr_atten_tpl.atten_dropout_prob = p.dropout_prob\n p_ii.tr_atten_tpl.residual_dropout_prob = p.dropout_prob\n p_ii.tr_fflayer_tpl.residual_dropout_prob = p.dropout_prob\n p_ii.tr_fflayer_tpl.relu_dropout_prob = p.dropout_prob\n if p.stochastic_depth_droppath_prob is not None:\n ratio = p.stochastic_depth_droppath_prob * ii / (p.num_layers - 1)\n p_ii.tr_atten_tpl.residual_droppath_prob = ratio\n p_ii.tr_fflayer_tpl.residual_droppath_prob = ratio\n if p.hidden_dim is not None:\n p_ii.tr_fflayer_tpl.hidden_dim = p.hidden_dim\n p_ii.tr_atten_tpl.add_unnormalized_input = p.add_unnormalized_input\n if ii in p.moe_layers:\n p_ii.tr_fflayer_tpl = _MoeLayerParams(p_ii.tr_fflayer_tpl)\n return p_ii","def get_parameters(self):\n params = {\"train_frac\": self.train_frac, \"split_alg\": self.split_alg,\n \"nw_name\": self._nw_name, \"split_id\": self.split_id}\n return params","def params(self):\n return {'shape': self.shape,\n 'name': self.name}","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['n' ] = self.n\n paramDict['p' ] = self.p\n return paramDict","def getstate(self):\r\n return Parameterized.getstate(self) + [self.parts,\r\n self.num_parts,\r\n self.num_params,\r\n self.input_dim,\r\n self.input_slices,\r\n self.param_slices\r\n ]","def _pprint_params(self):\n return {'x_range': self.x_range, 'y_range': self.y_range,\n 'step': self.step, 'shape': self.shape,\n 'type': self.type}","def __parameters__(self) -> tuple[TypeVar, ...]:\n return super().__getattribute__(\"_parameters\")","def display_layer_parameters(self):\n pprint.pprint(vars(self))\n return","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['lambda'] = self.lambdaVar\n paramDict['k' ] = self.k\n paramDict['low' ] = self.low\n return paramDict","def _get_params(self):\r\n return np.hstack((self.variance,self.lengthscale))","def _core_init_params(self) :\n\t\ta_list,b_list = [],[]\n\t\tg_list,h_list = [],[]\n\t\t\n\t\t\n\t\tfor eqnid,eqn in enumerate(self.equations) : \n\t\t\treg_p = self.regressors[eqnid]['prod']\n\t\t\treg_d = self.regressors[eqnid]['degrad']\n\t\t\th_eqn = self.initsol['h'][eqn-1]\n\t\t\tg_eqn = self.initsol['g'][eqn-1]\n\n\n\t\t\ta_list.append(self.initsol['alpha'][eqn-1])\n\t\t\tb_list.append(self.initsol['beta'][eqn-1])\n\t\t\t\n\t\t\tg_eqn = np.array([g_eqn[reg-1] for reg in reg_p])\n\t\t\th_eqn = np.array([h_eqn[reg-1] for reg in reg_d])\n\t\t\th_list.append(h_eqn)\n\t\t\tg_list.append(g_eqn)\n\t\n\t\treturn (a_list,b_list,g_list,h_list)","def parameters(self):\n return self._params","def get_parameters(self) -> Dict[str, ParameterInfo]:\n parameter_info_list = {}\n\n for associated_op in self.associated_ops:\n word_tensor = self._get_word_tensor(associated_op)\n position_tensor = self._get_position_tensor(associated_op)\n token_tensor = self._get_token_tensor(associated_op)\n\n for param_tensor in [word_tensor, position_tensor, token_tensor]:\n op_with_param = None\n for consumer in param_tensor.consumers():\n if not consumer.name.startswith('gradients/'):\n assert op_with_param is None\n op_with_param = consumer\n assert op_with_param is not None\n parameter_info_list[param_tensor.op.name] = ParameterInfo('weight', [op_with_param.name])\n\n return parameter_info_list","def print_all_params(self, disp=True):\n descriptions = {'general': {}}\n for name, param in self.params.items():\n descriptions['general'][name] = param.get_description()\n\n for comp, comp_obj in self.components.items():\n descriptions[comp] = {}\n for name in comp_obj.get_params():\n descriptions[comp][name] = comp_obj.get_param_description(name)\n return self._print_params(descriptions, disp)","def _get_params(self):\r\n return np.hstack((self.variance, self.lengthscale))","def _get_params(self):\r\n return np.hstack((self.variance, self.lengthscale))","def get_parameter_names(self):\n parNames = []\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\n # EstimationVariable\n parNames.append(par.name)\n return parNames","def derived_parameters(cls):\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']","def _get_parameters(n, j, domain, g, ncap):\n alphas, betas = rc.recurrenceCoefficients(n - 2, lb=domain[0], rb=domain[1],\n j=j, g=g, ncap=ncap)\n omegas = g * np.array(alphas)\n ts = g * np.sqrt(np.array(betas)[1::])\n c0 = np.sqrt(betas[0])\n return omegas, ts, c0","def _mn_par_ ( self , i ) :\n if not i in self : raise IndexError\n #\n val = ctypes.c_double ( 0 ) \n err = ctypes.c_double ( 0 ) \n #\n res = self.GetParameter ( i , val , err )\n #\n val = float ( val.value )\n err = float ( err.value )\n #\n return VE ( val , err*err )","def Get_params(numparams, dt, D):\n # bounds from table 1 Kowalek et al 2020\n Nmin, Nmax = 30, 600\n Bmin, Bmax = 1, 6\n Rmin, Rmax = 1, 17\n alphamin, alphamax = 0.3, 0.7\n Qmin, Qmax = 1, 9\n\n # Gen parameters\n Q = np.random.uniform(Qmin, Qmax, size=numparams)\n Q1, Q2 = Q, Q\n\n NsND = np.random.randint(Nmin, Nmax + 1, size=numparams)\n NsAD = np.random.randint(Nmin, Nmax + 1, size=numparams)\n NsCD = np.random.randint(Nmin, Nmax + 1, size=numparams)\n NsDM = np.random.randint(Nmin, Nmax + 1, size=numparams)\n TDM = NsDM * dt\n\n B = np.random.uniform(Bmin, Bmax, size=numparams)\n r_c = np.sqrt(D * NsCD * dt / B) # solving for r_c in eq. 8 Kowalek\n\n R = np.random.uniform(Rmin, Rmax, size=numparams)\n v = np.sqrt(R * 4 * D / TDM) # solving for v in eq. 7 Kowalek\n\n alpha = np.random.uniform(alphamin, alphamax, size=numparams)\n\n # Compute sigma for ND, AD, CD from eq. 12 Kowalek\n sigmaND = np.sqrt(D * dt) / Q1\n sigmaAD = np.sqrt(D * dt) / Q1\n sigmaCD = np.sqrt(D * dt) / Q1\n\n # Compute sigma for DM from eq. 12 Kowalek\n sigmaDM = np.sqrt(D * dt + v ** 2 * dt ** 2) / Q2\n\n return np.array(\n [\n NsND,\n NsAD,\n NsCD,\n NsDM,\n D * np.ones(numparams),\n dt * np.ones(numparams),\n r_c,\n v,\n alpha,\n sigmaND,\n sigmaAD,\n sigmaCD,\n sigmaDM,\n ]\n ).T","def get_params(self, deep=True):\n #params = dict(kernel=self.kernel, dim=self.dim)\n params = dict(columns=self.columns)\n if deep:\n for i, kernel in enumerate(self.kernels):\n print(\"--->\", \"\\ti = \", i, \"\\tkernel = \", kernel)\n deep_items = kernel.get_params().items()\n #params.update((k, val) for k, val in deep_items)\n for k, val in deep_items:\n print(\"\\tkey = \", k, \"\\tvalue = \", val)\n params.update(('k{}__{}'.format(i, k), val) for k, val in deep_items)\n return params","def getParameters(self):\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}","def print_trainable_params(scope=None):\n n_params = 0\n print('name \\t| shape \\t| num parameters')\n\n for var in tf.trainable_variables(scope):\n # shape is an array of tf.Dimension\n shape = var.get_shape()\n n_elems = shape.num_elements()\n print(var.name, shape, n_elems)\n n_params += n_elems\n\n print('Total parameters:', n_params)","def parameters(self):\n return [o.parameters for o in self.obs]","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['lambda'] = self.lambdaVar # rate parameter\n paramDict['low' ] = self.low # lower domain boundary\n return paramDict","def get_params(self, deep = True, bounds = True):\n params = dict() \n for p in self._LIST_PARAMETERS:\n params[p] = self._get_one_param(p)\n if(bounds):\n params[p + '_bounds'] = self._get_one_bound(p)\n if(deep and self._FLAG_TYPE == 'collection' and p == 'list_func'):\n for n, sub_obj in enumerate(params[p]):\n sub_params = sub_obj.get_params(deep, bounds)\n params.update({'f' + str(n) + '__' + key: val for key, val in sub_params.items()})\n \n return params"],"string":"[\n \"def setParams(self,dim=20, nIter=5, lamb=0.05, alph=40):\\n self._dim = dim\\n self._nIter = nIter\\n self._lamb = lamb\\n self._alph = alph\",\n \"def show_parameters(self):\\n with np.printoptions(precision=3, suppress=True):\\n print('number of wind phase = {}'.format(self.ncomp))\\n print('galactic parameter = {}'.format(self.scaling_field))\\n print('reference height = {}'.format(self.z0))\\n for p in ['cool_params','hot_params','params','ref_params','scaling_params']:\\n params = getattr(self,p)\\n print(p)\\n for k,v in params.items():\\n print(' {} = {}'.format(k,v))\",\n \"def params(self):\\n return {'out_dim': self.out_dim,\\n 'act_fn': self.act_fn,\\n 'use_bias': self.use_bias,\\n 'idx': self.idx}\",\n \"def inspect_state(self):\\n for name in self._param_store.get_all_param_names():\\n self._logger.info(\\\"Param [%s]: %r\\\", name,\\n pyro.param(name).data.numpy())\",\n \"def get_pars(self):\\n return [self.z, self.b, self.logN]\",\n \"def param_info():\\n\\n\\tgizmo_names = syn.getGizmoNames()\\n\\n\\tfor gizmo in gizmo_names:\\n\\t\\tparams = syn.getParameterNames(gizmo)\\n\\t#doesnt get all parameters from gizmos i.e. WaveFreq\\n\\n\\t# get all info on the 'WaveFreq' parameter\\n\\tGIZMO = 'aStim2'\\n\\tPARAMETER = 'WaveFreq'\\n\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # get the array size (should be 100)\\n\\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\\n\\t#\\n\\t# # write values 1 to 50 in second half of buffer\\n\\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\\n\\t#\\n\\t# # read all values from buffer\\n\\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\\n\\t#\\n\\t# # get all info on the 'Go' parameter\\n\\t# PARAMETER = 'Go'\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # flip the switch\\n\\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\\n\\t#\\n\\t# # check the value\\n\\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\\n\\tprint('value =', freq)\\n\\tfreq = [freq]\\n\\n\\t# also verify visually that the switch slipped in the run\\n\\t# time interface. This state change will be logged just\\n\\t# like any other variable change and saved with the runtime\\n\\t# state.\\n\\n\\tnumTrials = 5 #total number of trials across stimuli\\n\\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\\n\\n\\t# flash parameters\\n\\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\\n\\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\\n\\n\\t# auditory parameters\\n\\tduration = [.005] # in seconds; pulseDur in TDT\\n\\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\\n\\n\\t# Auditory on (T/F? if T then A+V, if F then Visual only)\\n\\tstims = {0: \\\"auditory_only\\\",\\n\\t\\t\\t 1: \\\"visual_only\\\",\\n\\t\\t\\t 2: \\\"A+V\\\"\\n\\t\\t\\t }\\n\\n\\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\\n\\t\\t\\t\\t\\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\\n\\texper.run_experiment()\",\n \"def getParam(self):\\n return self.__alpha0, self.__alpha1, self.__beta, self.__eta\",\n \"def params(self) -> Iterable[sympy.Symbol]:\\n for i in range(self.iterations):\\n for p in range(len(self.qubits)):\\n if (self.include_all_z or not\\n numpy.isclose(self.orbital_energies[p], 0)):\\n yield LetterWithSubscripts('U', p, i)\\n for p, q in itertools.combinations(range(len(self.qubits)), 2):\\n if (self.include_all_cz or not\\n numpy.isclose(self.hamiltonian.two_body[p, q], 0)):\\n yield LetterWithSubscripts('V', p, q, i)\",\n \"def give_block_param_info(self):\\n\\n nb_block=0\\n nb_values=[]\\n k=0\\n while 1:\\n k+=1\\n try:\\n self.info['mw_parameter'][str(10*k+1)]\\n except:\\n break\\n nb_block+=1\\n if type(self.info['mw_parameter'][str(10*k+3)])==list:\\n nb_values.append(len(self.info['mw_parameter'][str(10*k+3)]))\\n else:\\n nb_values.append(1)\\n\\n return nb_block,nb_values\",\n \"def parameters(self):\",\n \"def param(self,name,i):\\n state = self.getstate(name)\\n x,C = state.vec,state.cov\\n cc = C[i,i]\\n if (cc>0.): cc=sqrt(cc)\\n xx,cc = x[i],cc\\n debug('kfnode.param ',(name,xx,cc))\\n return xx,cc\",\n \"def _determine_parameters(self, paramdict):\\n for var in paramdict:\\n if is_dimensionless(paramdict[var]):\\n self._all_params_unit[var] = \\\"none\\\"\\n yield lems.Parameter(var, \\\"none\\\")\\n else:\\n dim = _determine_dimension(paramdict[var])\\n self._all_params_unit[var] = dim\\n yield lems.Parameter(var, dim)\",\n \"def parameters(self):\\n return {\\\"P\\\": self.P,\\n \\\"T\\\": self.T}\",\n \"def lattice_parameters(self):\\n return self.a, self.b, self.c, self.alpha, self.beta, self.gamma\",\n \"def get_layer_var_names(self):\\n return(self.params)\",\n \"def parameters(self):\\n return self.pars\",\n \"def params(dim):\\r\\n m = 3\\r\\n s = 1\\r\\n q = 2 ** (m - 1)\\r\\n while s < dim:\\r\\n m += 1\\r\\n s = m + math.factorial(m - 1) / (2 * math.factorial(m - 3))\\r\\n q = 2 ** (m - 1)\\r\\n\\r\\n return (\\r\\n m, q, s - dim)\",\n \"def getParams(self):\\n return self.trainError, self.trainAcc, self.w\",\n \"def getParameters(self):\\n\\n current_params = {'taux': self.taux, 'mu': self.mu, 'G': self.G, 'alpha_0': self.alpha_0,\\n 'delta': self.delta, 'p': self.p, 'I0': self.I0, 'kparam': self.kparam}\\n\\n return (current_params)\",\n \"def parameters(self):\\n return {\\\"W\\\": self.W,\\n \\\"T\\\": self.T,\\n \\\"P\\\": self.P}\",\n \"def potential_parameters(cls):\\n return [\\\"k\\\", \\\"length\\\"]\",\n \"def get_iperparams(self):\\n\\t\\treturn (self.D, self.K)\",\n \"def get_params(self):\\n return {\\n \\\"nspecies\\\": self.nspecies,\\n \\\"lmax\\\": self.lmax,\\n \\\"nmax\\\": self.nmax,\\n \\\"rcut\\\": self.rcut,\\n \\\"sigma\\\": self.sigma,\\n \\\"trans_width\\\": self.trans_width\\n }\",\n \"def get_hyperparameter_names():\\n params = ['mu', 'nu', 'r', 's']\\n return params\",\n \"def get_params(self):\",\n \"def parameters(self):\\n params = []\\n for layer in (self.conv1, self.conv2, self.conv3, self.conv4, self.dense1, self.dense2):\\n params += list(layer.parameters)\\n return params\",\n \"def get_hyperparams(self):\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict['method'] = self.method\\n paramDict['dimension'] = self.dimension\\n paramDict['rank'] = self.rank\\n paramDict['mu'] = self.mu\\n paramDict['covariance'] = self.covariance\\n return paramDict\",\n \"def params_desc(self):\\n return \\\"{}/{}/{}/{}\\\".format(\\n self.learning_rate, self.movement, self.milestones, self.gamma\\n )\",\n \"def get_parameters(self):\\n return self.sess.run(self.A_symm)\",\n \"def get_params(self, deep=False):\\n return {\\\"alpha\\\": self.alpha, \\\"beta\\\": self.beta, \\\"gamma\\\": self.gamma, \\\"W\\\": self.W, \\\"bias\\\": self.bias, \\\"add_bias\\\": self.add_bias, \\\"opts\\\": self.opts}\",\n \"def _get_parameters(self):\\n return (self.SYMBOL, self.parameterArray())\",\n \"def print_param_names(model):\\n for (param_name, param) in model.get_parameters().items():\\n print(param_name, param.shape)\",\n \"def get_params(self) -> np.array:\\n pass\",\n \"def parameters(self):\\n #print \\\"in instrument.parameter()\\\"\\n return self._params\",\n \"def def_paramt():\\n Zeff = 1.0\\n amu = 2.0\\n mf = mp*amu\\n return Zeff, amu,mf\",\n \"def params(self):\\n return self._pars\",\n \"def params(self):\\n return {'kernel_size': self.kernel_size,\\n 'stride': self.stride,\\n 'n_kernel': self.n_kernel,\\n 'padding': self.padding,\\n 'act_fn': self.act_fn,\\n 'output_shape': self.output_shape,\\n 'w_init_fn': self.w_init_fn,\\n 'use_bias': self.use_bias,\\n 'idx': self.idx}\",\n \"def getParameters(self):\\n params = []\\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\\n # we do not get param of output module\\n l = list(m.parameters())\\n params.extend(l)\\n\\n one_dim = [p.view(p.numel()) for p in params]\\n params = F.torch.cat(one_dim)\\n return params\",\n \"def getParameters(self):\\n params = []\\n for m in [self.ix, self.ih, self.fx, self.fh, self.ox, self.oh, self.ux, self.uh]:\\n # we do not get param of output module\\n l = list(m.parameters())\\n params.extend(l)\\n\\n one_dim = [p.view(p.numel()) for p in params]\\n params = F.torch.cat(one_dim)\\n return params\",\n \"def getParams(self):\\n return self.W, self.b\",\n \"def param(self):\\r\\n paramlist = []\\r\\n gradlist = []\\r\\n\\r\\n for layer in self.layers:\\r\\n try:\\r\\n layer_param, layer_grad = layer.param()\\r\\n paramlist = paramlist + layer_param\\r\\n gradlist = gradlist + layer_grad\\r\\n except ValueError:\\r\\n continue\\r\\n return paramlist, gradlist\",\n \"def params(self):\\n return {'kernel_size': self.kernel_size,\\n 'stride': self.stride,\\n 'n_kernel': self.n_kernel,\\n 'padding': self.padding,\\n 'act_fn': self.act_fn,\\n 'w_init_fn': self.w_init_fn,\\n 'resize': self.resize,\\n 'use_bias': self.use_bias,\\n 'atrous': self.atrous,\\n 'idx': self.idx}\",\n \"def display_parameters(self):\\n\\n self.logging.debug(\\\"============\\\")\\n for attr in self.parm_list:\\n self.logging.debug(attr.label + \\\" (\\\" + attr.when + \\\")\\\" + \\\" = \\\" + str(attr.value))\\n self.logging.debug(\\\"============\\\")\",\n \"def print_params_shape(scope, param_type):\\n shapes = [var.get_shape().as_list() for var in get_trainable_vars(scope)]\\n nb_params = sum([reduce(lambda x, y: x * y, shape) for shape in shapes])\\n print(' {} shapes: {}'.format(param_type, shapes))\\n print(' {} params: {}'.format(param_type, nb_params))\",\n \"def num_params(self):\",\n \"def _get_params(self):\\r\\n return self.k._get_params()\",\n \"def getParameters(self):\\n\\t\\td = AdaptiveBatAlgorithm.getParameters(self)\\n\\t\\td.update({\\n\\t\\t\\t'A_l': self.A_l,\\n\\t\\t\\t'A_u': self.A_u,\\n\\t\\t\\t'r_l': self.r_l,\\n\\t\\t\\t'r_u': self.r_u,\\n\\t\\t\\t'tao_1': self.tao_1,\\n\\t\\t\\t'tao_2': self.tao_2\\n\\t\\t})\\n\\t\\treturn d\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def get_params(self):\\n return self.arr\",\n \"def printParameters(self):\\n print(\\\"----------Model Parameters----------\\\")\\n print(\\\"Initial Conv. Depth : \\\" + str(self.conv_depth))\\n print(\\\"Number of Classes : \\\" + str(self.n_classes))\\n print(\\\"Dropout : \\\" + str(self.dropout))\\n print(\\\"Activation Function : Relu\\\")\\n print(\\\"Input Shape : \\\" + str(self.input_shape))\\n print(\\\"Batch Size : \\\" + str(self.batch_size))\\n print(\\\"--------Optimizer Parameters--------\\\")\\n print(\\\"Learning Rate : \\\" + str(self.optimizer.lr))\\n print(\\\"Momentum : \\\" + str(self.optimizer.momentum))\\n print(\\\"Initial Decay : \\\" + str(self.optimizer.initial_decay))\",\n \"def parameter_names(self) -> List[str]:\",\n \"def params(self):\\n return {'cfg': self.cfg,\\n 'momentum': self.momentum,\\n 'center': self.center,\\n 'scale': self.scale,\\n 'epsilon': self.epsilon,\\n 'act_fn': self.act_fn}\",\n \"def params():\\n raise NotImplementedError\",\n \"def get_params(self):\\n return self.w, self.b\",\n \"def parameters(self):\\n pass\",\n \"def parameterNames(self, p_int): # real signature unknown; restored from __doc__\\n return []\",\n \"def parameters(self):\\n return NeuralNetwork.flatten([module.parameters() for module in self.modules])\",\n \"def parameters(self):\\n return self.vars\",\n \"def __init__(self, N, n):\\n #adam params\\n self.beta_1 = 0.9\\n self.beta_2 = 0.999\\n self.epsilon = 1e-8\\n #not adam params\\n self.arr = np.zeros((N, 2*n+1)) #param array\\n self.max = sum([psi.lbd(p + 1, gamma, n) for p in range(n)])\\n self.loss_memory = []\\n self.eta_memory = []\\n self.grad_memory = []\\n self.N = N\\n self.n = n\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def printfunc(self, params, iter, resid, *args, **kwargs):\\n\\n print(iter) \\n print(params.valuesdict())\",\n \"def getParams(self):\\n\\n\\t\\tparams = {\\\"Nparticles\\\":self.__Nparticles,\\\"Nkicks\\\":self.__Nkicks,\\\"kappa\\\":self.__kappa, \\\"eta\\\":self.__eta,\\\"gamma\\\":self.__gamma, \\\"omega\\\":self.__omega,\\n\\t\\t\\\"Kbt\\\":self.__Kbt, \\\"tk\\\":self.__tk}\\n\\n\\t\\treturn params\",\n \"def algorithm_parameters(alg):\\n if alg in list(SEM_TYPE.keys()):\\n return simulate_parameters(alg)\\n\\n param_dict = dict()\\n\\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].__init__)\\n if param is not None:\\n param_len = len(param.args)\\n if param.defaults:\\n if 'input_dim' in param.args:\\n param_dict.update({'input_dim': None})\\n for index, value in enumerate(reversed(param.defaults)):\\n if not isfunction(value) and (value is not None):\\n param_dict.update(\\n {param.args[param_len - index - 1]: value})\\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].learn)\\n if param is not None:\\n param_len = len(param.args)\\n if param_len > 2:\\n if 'rank' in param.args:\\n param_dict.update({'rank': None})\\n return param_dict\",\n \"def get_params(self):\\n raise NotImplementedError\",\n \"def param_init(self, sig=0.01):\\n self.rhos = np.ones(self.Ndim)\\n self.a = np.random.rand(self.Ndim, self.Nhidden)\\n self.c = np.random.rand(self.Nhidden)\\n self.W = np.random.randn(self.Nhidden, self.Ndim) * sig\\n self.alphas = np.zeros((self.Ndim, self.Ncomponents))\\n self.mus = np.zeros((self.Ndim, self.Ncomponents))\\n self.sigmas = np.zeros((self.Ndim, self.Ncomponents))\\n self.optimize_params = [self.rhos, self.c, self.W]\\n\\n types = ['alpha', 'mu', 'sigma']\\n self.bs = {}\\n self.Vs = {}\\n for t in types:\\n self.bs[t] = np.random.randn(self.Ndim, self.Ncomponents) * sig\\n self.Vs[t] = np.random.randn(self.Ndim, self.Nhidden,\\n self.Ncomponents) * sig\\n self.optimize_params.append(self.bs[t])\\n self.optimize_params.append(self.Vs[t])\",\n \"def getInitParams(self):\\n paramDict = super().getInitParams()\\n paramDict[f'OutStream Available {self.dim}D :'] = self.availableOutStreamTypes[self.dim]\\n paramDict['Plot is '] = str(self.dim) + 'D'\\n for index in range(len(self.sourceName)):\\n paramDict['Source Name ' + str(index) + ' :'] = self.sourceName[index]\\n\\n return paramDict\",\n \"def getParameters(self): #$NON-NLS-1$\\r\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'css', 'cbb', 'vstar', 'gain', 'ft']\",\n \"def get_parameters(self):\\n return self.sess.run(self.A)\",\n \"def _get_params_summary(self, alpha=0.1):\\n\\n # TODO: Acknowledge that this code was modified from the statsmodels package\\n\\n results = self._model.fit()\\n\\n def forg(x, prec=3):\\n if prec == 3:\\n # for 3 decimals\\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\\n return '%9.3g' % x\\n else:\\n return '%9.3f' % x\\n elif prec == 4:\\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\\n return '%10.4g' % x\\n else:\\n return '%10.4f' % x\\n else:\\n raise NotImplementedError\\n\\n # Parameters part of the summary table\\n conf_int = results.conf_int(alpha)\\n\\n # Dictionary to store the header names for the parameter part of the\\n # summary table. look up by modeltype\\n alp = str((1 - alpha) * 100) + '%'\\n\\n param_header = ['coef', 'std err', 't', 'P>|t|',\\n '[' + alp + ' Conf. Int.]']\\n\\n xname = self._model.exog_names\\n\\n params_stubs = xname\\n\\n exog_idx = range(len(xname))\\n\\n # center confidence intervals if they are unequal lengths\\n confint = [\\\"%s %s\\\" % tuple(map(forg, conf_int.ix[i])) for i in exog_idx]\\n len_ci = list(map(len, confint))\\n max_ci = max(len_ci)\\n min_ci = min(len_ci)\\n\\n if min_ci < max_ci:\\n confint = [ci.center(max_ci) for ci in confint]\\n\\n # explicit f/g formatting, now uses forg, f or g depending on values\\n params_data = zip([forg(results.params[i], prec=4) for i in exog_idx],\\n [forg(results.bse[i]) for i in exog_idx],\\n [forg(results.tvalues[i]) for i in exog_idx],\\n # [\\\"%#6.3f\\\" % (results.pvalues[i]) for i in exog_idx],\\n [\\\"%#6.3g\\\" % (results.pvalues[i]) for i in exog_idx],\\n confint\\n )\\n params_data = list(params_data)\\n parameter_table = SimpleTable(params_data,\\n param_header,\\n params_stubs,\\n txt_fmt=fmt_params\\n )\\n\\n if results.params.shape[0] > 2:\\n vif_table = self._get_vif_table()\\n parameter_table.extend_right(vif_table)\\n\\n return parameter_table\",\n \"def parameterNode(self):\\r\\n # framework\\r\\n profbox()\\r\\n return self.parameterNode\",\n \"def parameters(self):\\n return []\",\n \"def _LayerParams(ii):\\n if isinstance(p.transformer_layer_params_tpl, list):\\n factor = p.num_layers // len(p.transformer_layer_params_tpl)\\n i = ii // factor\\n p_ii = p.transformer_layer_params_tpl[i].Copy()\\n else:\\n p_ii = p.transformer_layer_params_tpl.Copy()\\n p_ii.name = 'layer_%d' % ii\\n p_ii.has_aux_atten = p.has_aux_atten\\n p_ii.mask_self_atten = p.mask_self_atten\\n p_ii.input_dim = p.mdl_dim or p_ii.input_dim\\n p_ii.output_dim = p.mdl_dim or p_ii.output_dim\\n p_ii.packed_input = p.packed_input\\n if (not isinstance(p_ii.tr_atten_tpl.num_heads, list) and\\n p.num_atten_heads is not None):\\n p_ii.tr_atten_tpl.num_heads = p.num_atten_heads\\n if p.dropout_prob is not None:\\n p_ii.tr_atten_tpl.atten_dropout_prob = p.dropout_prob\\n p_ii.tr_atten_tpl.residual_dropout_prob = p.dropout_prob\\n p_ii.tr_fflayer_tpl.residual_dropout_prob = p.dropout_prob\\n p_ii.tr_fflayer_tpl.relu_dropout_prob = p.dropout_prob\\n if p.stochastic_depth_droppath_prob is not None:\\n ratio = p.stochastic_depth_droppath_prob * ii / (p.num_layers - 1)\\n p_ii.tr_atten_tpl.residual_droppath_prob = ratio\\n p_ii.tr_fflayer_tpl.residual_droppath_prob = ratio\\n if p.hidden_dim is not None:\\n p_ii.tr_fflayer_tpl.hidden_dim = p.hidden_dim\\n p_ii.tr_atten_tpl.add_unnormalized_input = p.add_unnormalized_input\\n if ii in p.moe_layers:\\n p_ii.tr_fflayer_tpl = _MoeLayerParams(p_ii.tr_fflayer_tpl)\\n return p_ii\",\n \"def get_parameters(self):\\n params = {\\\"train_frac\\\": self.train_frac, \\\"split_alg\\\": self.split_alg,\\n \\\"nw_name\\\": self._nw_name, \\\"split_id\\\": self.split_id}\\n return params\",\n \"def params(self):\\n return {'shape': self.shape,\\n 'name': self.name}\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['n' ] = self.n\\n paramDict['p' ] = self.p\\n return paramDict\",\n \"def getstate(self):\\r\\n return Parameterized.getstate(self) + [self.parts,\\r\\n self.num_parts,\\r\\n self.num_params,\\r\\n self.input_dim,\\r\\n self.input_slices,\\r\\n self.param_slices\\r\\n ]\",\n \"def _pprint_params(self):\\n return {'x_range': self.x_range, 'y_range': self.y_range,\\n 'step': self.step, 'shape': self.shape,\\n 'type': self.type}\",\n \"def __parameters__(self) -> tuple[TypeVar, ...]:\\n return super().__getattribute__(\\\"_parameters\\\")\",\n \"def display_layer_parameters(self):\\n pprint.pprint(vars(self))\\n return\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['lambda'] = self.lambdaVar\\n paramDict['k' ] = self.k\\n paramDict['low' ] = self.low\\n return paramDict\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance,self.lengthscale))\",\n \"def _core_init_params(self) :\\n\\t\\ta_list,b_list = [],[]\\n\\t\\tg_list,h_list = [],[]\\n\\t\\t\\n\\t\\t\\n\\t\\tfor eqnid,eqn in enumerate(self.equations) : \\n\\t\\t\\treg_p = self.regressors[eqnid]['prod']\\n\\t\\t\\treg_d = self.regressors[eqnid]['degrad']\\n\\t\\t\\th_eqn = self.initsol['h'][eqn-1]\\n\\t\\t\\tg_eqn = self.initsol['g'][eqn-1]\\n\\n\\n\\t\\t\\ta_list.append(self.initsol['alpha'][eqn-1])\\n\\t\\t\\tb_list.append(self.initsol['beta'][eqn-1])\\n\\t\\t\\t\\n\\t\\t\\tg_eqn = np.array([g_eqn[reg-1] for reg in reg_p])\\n\\t\\t\\th_eqn = np.array([h_eqn[reg-1] for reg in reg_d])\\n\\t\\t\\th_list.append(h_eqn)\\n\\t\\t\\tg_list.append(g_eqn)\\n\\t\\n\\t\\treturn (a_list,b_list,g_list,h_list)\",\n \"def parameters(self):\\n return self._params\",\n \"def get_parameters(self) -> Dict[str, ParameterInfo]:\\n parameter_info_list = {}\\n\\n for associated_op in self.associated_ops:\\n word_tensor = self._get_word_tensor(associated_op)\\n position_tensor = self._get_position_tensor(associated_op)\\n token_tensor = self._get_token_tensor(associated_op)\\n\\n for param_tensor in [word_tensor, position_tensor, token_tensor]:\\n op_with_param = None\\n for consumer in param_tensor.consumers():\\n if not consumer.name.startswith('gradients/'):\\n assert op_with_param is None\\n op_with_param = consumer\\n assert op_with_param is not None\\n parameter_info_list[param_tensor.op.name] = ParameterInfo('weight', [op_with_param.name])\\n\\n return parameter_info_list\",\n \"def print_all_params(self, disp=True):\\n descriptions = {'general': {}}\\n for name, param in self.params.items():\\n descriptions['general'][name] = param.get_description()\\n\\n for comp, comp_obj in self.components.items():\\n descriptions[comp] = {}\\n for name in comp_obj.get_params():\\n descriptions[comp][name] = comp_obj.get_param_description(name)\\n return self._print_params(descriptions, disp)\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance, self.lengthscale))\",\n \"def _get_params(self):\\r\\n return np.hstack((self.variance, self.lengthscale))\",\n \"def get_parameter_names(self):\\n parNames = []\\n # for par in self.variables: # TODO: LIKELY A BUG! DOES THE SAME AS get_variable_names()\\n for par in self.parameters: # TRYING TO SOLVE THE ISSUE\\n # EstimationVariable\\n parNames.append(par.name)\\n return parNames\",\n \"def derived_parameters(cls):\\n return ['cgg', 'cdd', 'vstar', 'gain', 'ft']\",\n \"def _get_parameters(n, j, domain, g, ncap):\\n alphas, betas = rc.recurrenceCoefficients(n - 2, lb=domain[0], rb=domain[1],\\n j=j, g=g, ncap=ncap)\\n omegas = g * np.array(alphas)\\n ts = g * np.sqrt(np.array(betas)[1::])\\n c0 = np.sqrt(betas[0])\\n return omegas, ts, c0\",\n \"def _mn_par_ ( self , i ) :\\n if not i in self : raise IndexError\\n #\\n val = ctypes.c_double ( 0 ) \\n err = ctypes.c_double ( 0 ) \\n #\\n res = self.GetParameter ( i , val , err )\\n #\\n val = float ( val.value )\\n err = float ( err.value )\\n #\\n return VE ( val , err*err )\",\n \"def Get_params(numparams, dt, D):\\n # bounds from table 1 Kowalek et al 2020\\n Nmin, Nmax = 30, 600\\n Bmin, Bmax = 1, 6\\n Rmin, Rmax = 1, 17\\n alphamin, alphamax = 0.3, 0.7\\n Qmin, Qmax = 1, 9\\n\\n # Gen parameters\\n Q = np.random.uniform(Qmin, Qmax, size=numparams)\\n Q1, Q2 = Q, Q\\n\\n NsND = np.random.randint(Nmin, Nmax + 1, size=numparams)\\n NsAD = np.random.randint(Nmin, Nmax + 1, size=numparams)\\n NsCD = np.random.randint(Nmin, Nmax + 1, size=numparams)\\n NsDM = np.random.randint(Nmin, Nmax + 1, size=numparams)\\n TDM = NsDM * dt\\n\\n B = np.random.uniform(Bmin, Bmax, size=numparams)\\n r_c = np.sqrt(D * NsCD * dt / B) # solving for r_c in eq. 8 Kowalek\\n\\n R = np.random.uniform(Rmin, Rmax, size=numparams)\\n v = np.sqrt(R * 4 * D / TDM) # solving for v in eq. 7 Kowalek\\n\\n alpha = np.random.uniform(alphamin, alphamax, size=numparams)\\n\\n # Compute sigma for ND, AD, CD from eq. 12 Kowalek\\n sigmaND = np.sqrt(D * dt) / Q1\\n sigmaAD = np.sqrt(D * dt) / Q1\\n sigmaCD = np.sqrt(D * dt) / Q1\\n\\n # Compute sigma for DM from eq. 12 Kowalek\\n sigmaDM = np.sqrt(D * dt + v ** 2 * dt ** 2) / Q2\\n\\n return np.array(\\n [\\n NsND,\\n NsAD,\\n NsCD,\\n NsDM,\\n D * np.ones(numparams),\\n dt * np.ones(numparams),\\n r_c,\\n v,\\n alpha,\\n sigmaND,\\n sigmaAD,\\n sigmaCD,\\n sigmaDM,\\n ]\\n ).T\",\n \"def get_params(self, deep=True):\\n #params = dict(kernel=self.kernel, dim=self.dim)\\n params = dict(columns=self.columns)\\n if deep:\\n for i, kernel in enumerate(self.kernels):\\n print(\\\"--->\\\", \\\"\\\\ti = \\\", i, \\\"\\\\tkernel = \\\", kernel)\\n deep_items = kernel.get_params().items()\\n #params.update((k, val) for k, val in deep_items)\\n for k, val in deep_items:\\n print(\\\"\\\\tkey = \\\", k, \\\"\\\\tvalue = \\\", val)\\n params.update(('k{}__{}'.format(i, k), val) for k, val in deep_items)\\n return params\",\n \"def getParameters(self):\\n return {'channels':self._channels, 'means':self._means, 'stds':self._stds}\",\n \"def print_trainable_params(scope=None):\\n n_params = 0\\n print('name \\\\t| shape \\\\t| num parameters')\\n\\n for var in tf.trainable_variables(scope):\\n # shape is an array of tf.Dimension\\n shape = var.get_shape()\\n n_elems = shape.num_elements()\\n print(var.name, shape, n_elems)\\n n_params += n_elems\\n\\n print('Total parameters:', n_params)\",\n \"def parameters(self):\\n return [o.parameters for o in self.obs]\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['lambda'] = self.lambdaVar # rate parameter\\n paramDict['low' ] = self.low # lower domain boundary\\n return paramDict\",\n \"def get_params(self, deep = True, bounds = True):\\n params = dict() \\n for p in self._LIST_PARAMETERS:\\n params[p] = self._get_one_param(p)\\n if(bounds):\\n params[p + '_bounds'] = self._get_one_bound(p)\\n if(deep and self._FLAG_TYPE == 'collection' and p == 'list_func'):\\n for n, sub_obj in enumerate(params[p]):\\n sub_params = sub_obj.get_params(deep, bounds)\\n params.update({'f' + str(n) + '__' + key: val for key, val in sub_params.items()})\\n \\n return params\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66055083","0.65783185","0.6249512","0.60537255","0.6009132","0.60000795","0.599074","0.5983164","0.59602505","0.59599835","0.59563273","0.5954735","0.5952508","0.59498894","0.59248936","0.5918452","0.5881164","0.58748","0.5865431","0.5844908","0.5821393","0.58213127","0.58106035","0.58074","0.57923317","0.57842344","0.5756075","0.5753663","0.5751509","0.5746594","0.57446456","0.5740447","0.57372224","0.5724931","0.5690531","0.56810194","0.5676005","0.5664775","0.5645824","0.5645824","0.56443864","0.564181","0.56337863","0.5606107","0.55908024","0.55841166","0.558074","0.55753374","0.5570626","0.5555742","0.5540007","0.55373496","0.55332404","0.55177104","0.55013925","0.5488639","0.5486654","0.5479328","0.54763126","0.5474097","0.5470791","0.54549825","0.5442651","0.5442638","0.54374","0.5437281","0.54298836","0.5426299","0.5418472","0.54146934","0.5414652","0.5412961","0.5408276","0.5399377","0.5389025","0.5386802","0.53856725","0.53846276","0.5381253","0.5381108","0.5377615","0.5369272","0.5369213","0.53682196","0.53675324","0.5367388","0.53629434","0.5361157","0.5361157","0.53603244","0.5359162","0.5358729","0.5354467","0.53512985","0.5350586","0.53493637","0.53474325","0.5344251","0.5341885","0.5336419"],"string":"[\n \"0.66055083\",\n \"0.65783185\",\n \"0.6249512\",\n \"0.60537255\",\n \"0.6009132\",\n \"0.60000795\",\n \"0.599074\",\n \"0.5983164\",\n \"0.59602505\",\n \"0.59599835\",\n \"0.59563273\",\n \"0.5954735\",\n \"0.5952508\",\n \"0.59498894\",\n \"0.59248936\",\n \"0.5918452\",\n \"0.5881164\",\n \"0.58748\",\n \"0.5865431\",\n \"0.5844908\",\n \"0.5821393\",\n \"0.58213127\",\n \"0.58106035\",\n \"0.58074\",\n \"0.57923317\",\n \"0.57842344\",\n \"0.5756075\",\n \"0.5753663\",\n \"0.5751509\",\n \"0.5746594\",\n \"0.57446456\",\n \"0.5740447\",\n \"0.57372224\",\n \"0.5724931\",\n \"0.5690531\",\n \"0.56810194\",\n \"0.5676005\",\n \"0.5664775\",\n \"0.5645824\",\n \"0.5645824\",\n \"0.56443864\",\n \"0.564181\",\n \"0.56337863\",\n \"0.5606107\",\n \"0.55908024\",\n \"0.55841166\",\n \"0.558074\",\n \"0.55753374\",\n \"0.5570626\",\n \"0.5555742\",\n \"0.5540007\",\n \"0.55373496\",\n \"0.55332404\",\n \"0.55177104\",\n \"0.55013925\",\n \"0.5488639\",\n \"0.5486654\",\n \"0.5479328\",\n \"0.54763126\",\n \"0.5474097\",\n \"0.5470791\",\n \"0.54549825\",\n \"0.5442651\",\n \"0.5442638\",\n \"0.54374\",\n \"0.5437281\",\n \"0.54298836\",\n \"0.5426299\",\n \"0.5418472\",\n \"0.54146934\",\n \"0.5414652\",\n \"0.5412961\",\n \"0.5408276\",\n \"0.5399377\",\n \"0.5389025\",\n \"0.5386802\",\n \"0.53856725\",\n \"0.53846276\",\n \"0.5381253\",\n \"0.5381108\",\n \"0.5377615\",\n \"0.5369272\",\n \"0.5369213\",\n \"0.53682196\",\n \"0.53675324\",\n \"0.5367388\",\n \"0.53629434\",\n \"0.5361157\",\n \"0.5361157\",\n \"0.53603244\",\n \"0.5359162\",\n \"0.5358729\",\n \"0.5354467\",\n \"0.53512985\",\n \"0.5350586\",\n \"0.53493637\",\n \"0.53474325\",\n \"0.5344251\",\n \"0.5341885\",\n \"0.5336419\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74654466"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94589,"cells":{"query":{"kind":"string","value":"Java Float Matrix is a 1D array writen column after column. Numpy reads row after row, therefore, we need a conversion."},"document":{"kind":"string","value":"def _float_matrix2numpy(self, java_float_matrix):\n columns_input = java_float_matrix.toArray()\n split = lambda lst, sz: [numpy.fromiter(lst[i:i+sz],dtype=numpy.float)\n for i in range(0, len(lst), sz)]\n cols = split(columns_input, java_float_matrix.rows)\n matrix = numpy.ma.column_stack(cols)\n return matrix"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def matrixToFloatMatrix(matrix):\n\n float_matrix = [matrix(i, j) for i in xrange(4) for j in xrange(4)]\n\n outMatrix = OpenMaya.MFloatMatrix()\n OpenMaya.MScriptUtil.createFloatMatrixFromList(float_matrix , outMatrix)\n\n return outMatrix","def itemsToFloat(self):\n returnvalue = Matrix()\n for row in self._value:\n newRow = list()\n for item in row:\n newRow.append(float(item))\n returnvalue.addRow(*newRow)\n return returnvalue","def _cast_to_float64(matrix):\n return matrix.astype(np.float64) if matrix.dtype != np.float64 else matrix","def read_as_float_matrix(path):\n\n bgr_image = cv2.imread(path)\n return bgr_image.astype(np.float32) / 255.","def getFileAsFloatMatrix(dirPath, columns, delim=\",\"):\n\tmat = list()\n\tfor rec in fileSelFieldsRecGen(dirPath, columns, delim):\n\t\tmat.append(asFloatList(rec))\n\treturn mat","def as_matrix(self) -> types.Matrix:","def write_float_array(f, path, values, dtype='f8'):\n dset = f.create_dataset(path, (len(values),), dtype=dtype)\n dset[:] = values\n f.flush()","def test_convert_numpy_to_libffm(self):\n file = tempfile.NamedTemporaryFile(delete=False)\n\n # write data to libffm format\n write_data_to_xlearn_format(self.X, self.y, file.name, fields=self.fields)\n\n # read back data from file\n X_true, y_true, field_true = self._read_libffm_file(file.name)\n file.close()\n if os.path.exists(file.name):\n os.remove(file.name)\n\n assert np.all(np.isclose(self.X, X_true))\n assert np.all(self.y.ravel() == y_true.ravel())\n assert np.all(self.fields.ravel() == field_true.ravel())","def floatArrayToPrt(float_array):\n\n util = OpenMaya.MScriptUtil() \n util.createFromList(float_array, len(float_array))\n\n return util.asFloatPtr()","def new_float(*args, **kwargs):\n return array.array(FLOAT_TYPECODE, *args, **kwargs)","def getFloatArray2D(self) -> typing.List[typing.List[float]]:\n ...","def write_matrix_to_binary(file_name, val):\n with open(file_name, 'wb') as file:\n nrow = val.shape[0]\n ncol = val.shape[1]\n file.write(int32_to_bytes(nrow) + int32_to_bytes(ncol) + val.astype(float).tobytes(order='C'))","def removeMatrixTranslate(matrix):\n\n float_matrix = [matrix(i, j) for i in xrange(4) for j in xrange(4)]\n for idx in range(12, 15):\n float_matrix[idx] = 0.0\n \n outMatrix = OpenMaya.MFloatMatrix()\n OpenMaya.MScriptUtil.createFloatMatrixFromList(float_matrix , outMatrix)\n\n return outMatrix","def to_matrix(self, normalize: bool = True) -> jnp.ndarray:\n return NotImplemented # pragma: no cover","def convertToFloatArray(booleanArray: typing.List[bool]) -> typing.List[float]:\n ...","def readCSVasFloat(filename):\n returnArray = []\n lines = open(filename).readlines()\n for line in lines:\n line = line.strip().split(\",\")\n if len(line) > 0:\n returnArray.append(np.array([np.float32(x) for x in line]))\n\n returnArray = np.array(returnArray)\n return returnArray","def cfloat(queue=None):\n return np.float32","def read_float(filename):\n\tf = open(filename, \"r\")\n\tarr = np.fromfile(f, dtype='>f4')\n\treturn arr","def convert_stream(self, stream):\n return np.fromstring(stream, \"Float32\")","def get_float_data(dataframe):\n dataframe = dataframe[np.isfinite(dataframe.TIME_StartTime)]\n float_cols = [isfloatarray(col) for col in dataframe.values.T]\n return (dataframe.T[float_cols].T).astype(float)","def column(matrix, i):\r\n return [row[i] for row in matrix if isfloat(row[i])]","def np_floats(self):\n # A numpy-array packaging of the floating-point input parameters\n return np.array([self.chi1, self.chi2, self.chip, self.thetaJ, self.m1, self.m2,\n self.distance, self.alpha0, self.phic, self.fref], dtype=np.float64)","def test_op_fillfrom_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n offl_r = stream.empty_like(a)\n offl_r.fillfrom(a)\n r = offl_r.update_host().array\n stream.sync()\n self.assertTrue((a == r).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(a, r))","def getFileAsFiltFloatMatrix(dirPath, filt, columns, delim=\",\"):\n\tmat = list()\n\tfor rec in fileFiltSelFieldsRecGen(dirPath, filt, columns, delim):\n\t\tmat.append(asFloatList(rec))\n\treturn mat","def test_op_one_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n offl_a = stream.bind(a)\n offl_a.one()\n offl_a.update_host()\n stream.sync()\n self.assertTrue((a == 1.0).all(),\n \"Array should be all one.\" + str(a))","def __type_of_elements_correct_long_Numpy_matrix(self):\n strTestName = 'Float elements in a Numpy matrix (correct)'\n RxCSObject = _RxCSobject()\n\n RxCSObject.paramAddMan('parameter1', 'Numpy matrix #1')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramTypeEl('parameter1', (int, float))\n\n RxCSObject.paramAddMan('parameter2', 'Numpy vector #2')\n RxCSObject.paramType('parameter2', np.ndarray)\n RxCSObject.paramTypeEl('parameter2', (int, float))\n\n RxCSObject.parameter1 = np.random.randn(1e2,1e2)\n RxCSObject.parameter2 = np.random.randn(1e2,1e2)\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def test_convert_matrix():\n foo = Value(matrices=[[1.0, 2.0], [-2.0, 1.0]])\n assert foo.matrices[0][0][1].value == 2.0\n assert foo.matrices[0][1][0].value == -2.0","def _numpy_2_native_matrix(numpy_mat):\n\n # Create native matrix object\n packed_vec = _pack_numpy_matrix(numpy_mat)\n return rqrmilib.create_matrix(packed_vec)","def wrapDBMatrix(self,mat):\n return mat.todense()","def uint8_to_float(im: np.array):\n if im.dtype == np.float32:\n warnings.warn(\"Image is already np.float32\")\n return im\n im = im.astype(np.float32) / 255\n return im","def uint8_to_float(im: np.array):\n if im.dtype == np.float32:\n warnings.warn(\"Image is already np.float32\")\n return im\n im = im.astype(np.float32) / 255\n return im","def getFloat(self):\n try:\n data, n = self._buf.pop(_F_LEN)\n \n if n != _F_LEN:\n raise SerializationError('There is not enough data left.')\n \n return _F_STRUCT.unpack(data)[0]\n except StructError as e:\n raise SerializationError('Data is not a valid float: '\n '{0}'.format(e))","def f1(self) -> float:\n return self._matrix.f1","def readFloat(self) -> float:\n return self._unpack('!f', 4)","def read_input_float_feature(feature_map, key, shape):\n if shape is None:\n (dim_z, dim_y, dim_x) = feature_map.feature[key + '/dim'].int64_list.value\n else:\n (dim_z, dim_y, dim_x) = shape\n tensor = np.array(feature_map.feature[key].float_list.value[:]).reshape(\n dim_z, dim_y, dim_x)\n return tensor","def getFileAsFloatColumn(dirPath):\n\tflist = list()\n\tfor rec in fileRecGen(dirPath, None):\n\t\tflist.append(float(rec))\n\treturn flist","def import_matrix(fileMatrix):\n with open(fileMatrix) as fMat:\n matrix = np.zeros((3,4))\n for ligne in fMat:\n if ligne.startswith(' 1') or ligne.startswith(' 2') or ligne.startswith(' 3'):\n matrix[int(ligne.split()[0])-1,:] = float(ligne.split()[1]),float(ligne.split()[2]),float(ligne.split()[3]),float(ligne.split()[4])\n return deepcopy(matrix)","def writeMatrix(self):\n\t\tpass","def _float_metric_value(metric):\n return metric.result().numpy().astype(float)","def parse_matrix(lines):\r\n col_headers = None\r\n result = []\r\n row_headers = []\r\n for line in lines:\r\n if line[0] == '#':\r\n continue\r\n if line[0] == '\\t': # is header\r\n col_headers = map(strip, line.split('\\t')[1:])\r\n else:\r\n entries = line.split('\\t')\r\n result.append(map(float, entries[1:]))\r\n row_headers.append(entries[0])\r\n return col_headers, row_headers, asarray(result)","def read_matrix_from_binary(file_name):\n with open(file_name, 'rb') as file:\n buffer = file.read()\n n_row = int.from_bytes(buffer[0:4], 'little')\n n_col = int.from_bytes(buffer[4:8], 'little')\n matrix = numpy.frombuffer(buffer[8:], dtype=float).reshape([n_row, n_col])\n return matrix","def py_make_float_array(cst, op_version=None):\n return numpy.array([cst], dtype=numpy.float32)","def _serialize_double_matrix(m):\n if (type(m) == ndarray and m.dtype == float64 and m.ndim == 2):\n rows = m.shape[0]\n cols = m.shape[1]\n ba = bytearray(24 + 8 * rows * cols)\n header = ndarray(shape=[3], buffer=ba, dtype=\"int64\")\n header[0] = 2\n header[1] = rows\n header[2] = cols\n copyto(ndarray(shape=[rows, cols], buffer=ba, offset=24,\n dtype=\"float64\", order='C'), m)\n return ba\n else:\n raise TypeError(\"_serialize_double_matrix called on a \"\n \"non-double-matrix\")","def _native_matrix_2_numpy(mat):\n\n if 'RQRMI matrix' not in str(mat):\n raise ValueError('Input is not valid rqrmi matrix object')\n return np.array(rqrmilib.matrix_to_list(mat))","def tfidfTransform(matrix):\n matrix_float = numpy.zeros(matrix.shape)\n document_Total = len(matrix)\n rows, cols = matrix.shape\n for row in xrange(0, rows): # For each document\n wordTotal = reduce(lambda x, y: x + y, matrix[row])\n for col in xrange(0, cols): # For each term\n # For consistency ensure all self.matrix values are floats\n matrix[row][col] = float(matrix[row][col])\n if matrix[row][col] != 0:\n term_document_occurences = __getTermDocumentOccurences(col, matrix)\n term_frequency = matrix[row][col] / float(wordTotal)\n inverse_document_frequency = math.log(abs(document_Total / float(term_document_occurences)))\n # print(term_document_occurences, term_frequency, inverse_document_frequency, term_frequency *\n # inverse_document_frequency)\n matrix_float[row][col] = term_frequency * inverse_document_frequency\n # print(matrix_float[row][col])\n return matrix_float","def getMultipleFileAsFloatMatrix(dirPathWithCol, delim=\",\"):\n\tmat = list()\n\tminLen = -1\n\tfor path, col in dirPathWithCol:\n\t\tcolVals = getFileColumnAsFloat(path, col, delim)\n\t\tif minLen < 0 or len(colVals) < minLen:\n\t\t\tminLen = len(colVals)\n\t\tmat.append(colVals)\n\t\n\t#make all same length\n\tmat = list(map(lambda li:li[:minLen], mat))\t\n\treturn mat","def make_float(array):\n finial_array = []\n\n for number in array:\n finial_array.append(float(number))\n return finial_array","def _serialize_matrix(matrix):\n\n f = BytesIO()\n spsp.save_npz(f, matrix)\n result = f.getvalue().hex()\n return result","def __type_of_elements_incorrect_float_in_Numpy_vector(self):\n strTestName = 'Float elements in a Numpy vector (incorrect)'\n RxCSObject = _RxCSobject()\n\n RxCSObject.paramAddMan('parameter1', 'Numpy vector #1')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramTypeEl('parameter1', (float))\n\n RxCSObject.paramAddMan('parameter2', 'Numpy vector #2')\n RxCSObject.paramType('parameter2', np.ndarray)\n RxCSObject.paramTypeEl('parameter2', (int))\n\n RxCSObject.parameter1 = np.random.randn(1e2)\n RxCSObject.parameter2 = np.random.randn(1e2)\n\n self.__parametersCheck_error(RxCSObject, ElementTypeError, strTestName)","def floatX(arr):\n return np.asarray(arr, dtype=theano.config.floatX)","def binfloat(filename):\n return np.memmap(filename, dtype='float32')","def read_float(self):\n return self._packers[\"f\"].unpack(self.read(4))[0]","def isfloatarray(cell):\n try:\n cell.astype(float)\n return True\n except ValueError:\n return False","def update_F_matrix(self, F_matrix):\n self.F_matrix = F_matrix","def asfloat(value):\n float_type = 'float32'\n\n if isinstance(value, (np.matrix, np.ndarray)):\n if value.dtype != np.dtype(float_type):\n return value.astype(float_type)\n\n return value\n\n elif isinstance(value, (tf.Tensor, tf.SparseTensor)):\n return tf.cast(value, tf.float32)\n\n elif issparse(value):\n return value\n\n float_x_type = np.cast[float_type]\n return float_x_type(value)","def serialize_numpy(self, buff, numpy):\n try:\n _x = self\n buff.write(_struct_3I.pack(_x.base.header.seq, _x.base.header.stamp.secs, _x.base.header.stamp.nsecs))\n _x = self.base.header.frame_id\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self\n buff.write(_struct_5f.pack(_x.base.cell_width, _x.base.cell_height, _x.base.position.x, _x.base.position.y, _x.base.position.z))\n length = len(self.base.points)\n buff.write(_struct_I.pack(length))\n for val1 in self.base.points:\n _x = val1\n buff.write(_struct_3f.pack(_x.x, _x.y, _x.z))\n length = len(self.base.intensity)\n buff.write(_struct_I.pack(length))\n pattern = '<%sf'%length\n buff.write(self.base.intensity.tostring())\n buff.write(_struct_b.pack(self.base.cost))\n _x = self.base.label\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self\n buff.write(_struct_2f.pack(_x.mean_height, _x.mean_intensity))\n except struct.error as se: self._check_types(se)\n except TypeError as te: self._check_types(te)","def test_message_float():\n result = True\n\n message = msg.Message()\n for i in range(num_it):\n message.appendFloat(i/128.789456)\n if message.length != msg.HEADER_SIZE + (i+1)*msg.floatStruct.size:\n print(\"Size is \", message.length, \" but should be \", msg.HEADER_SIZE + (i+1)*msg.floatStruct.size)\n print(\"Error : message.appendFloat\")\n result = False\n\n message.resetCursor()\n for i in range(num_it):\n r = message.readFloat()\n if abs(r - i/128.789456) > 0.000001:\n print(r, \" vs \", i/128.789456)\n print(\"Error : message.read/appendFloat\")\n result = False\n\n return result","def readFloats(path, dimensions, header=0):\n size = reduce(operator.mul, dimensions)\n ra = RandomAccessFile(path, 'r')\n try:\n ra.skipBytes(header)\n bytes = zeros(size * 4, 'b')\n ra.read(bytes)\n floats = zeros(size, 'f')\n ByteBuffer.wrap(bytes).asFloatBuffer().get(floats)\n return ArrayImgs.floats(floats, dimensions)\n finally:\n ra.close()","def dataConvertToNumpy( self ):\n self.featureNumpy = np.asarray( self.feature )\n self.ClassNumpy = np.asarray( self.Class )","def getFloatRow(self, int: int) -> typing.List[float]:\n ...","def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\"<i4\")\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\"<f8\")\n size = j_obj.getSize()\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()","def py2mat(myobj):\n if isinstance(myobj, pandas.Series):\n mat = r.matrix(myobj,\n rownames=myobj.index,\n dimnames=myobj.name)\n else:\n mat = r.matrix(myobj)\n return mat","def read_float(stream, writer_schema=None, reader_schema=None): # noqa\n return unpack('<f', stream.read(4))[0]","def fread_matrix(stream):\n\n A = fread_vector(stream)\n while (True):\n v = fread_vector(stream)\n if (v.shape[0] == 0):\n return (A)\n A = numpy.vstack((A, v))","def _convert_matrix(m):\n\n return [m[0][0], m[0][1], m[0][2], m[0][3],\n m[2][0], m[2][1], m[2][2], m[2][3],\n -m[1][0], -m[1][1], -m[1][2], -m[1][3],\n m[3][0], m[3][1], m[3][2], m[3][3]]","def decode_float(self, buf, pos):\n return self.decode_struct(self._float_fmt, buf, pos)","def _shorts2float(lo_byte_pair, hi_byte_pair):\n\tba = bytearray(struct.pack(\"HH\", lo_byte_pair, hi_byte_pair))\n\t[f] = struct.unpack('f', ba)\n\treturn f","def __convert_two_cols(raw_array: list, num_rows: int):\n\n # no need to check extra row, we can go right into conversions\n for i in range(num_rows):\n\n # float conversion\n float_value = float(raw_array[i][1])\n raw_array[i][1] = float_value\n\n # no need to delete an extra entry,\n # we can just convert the existing string and assign it\n timestamp = pd.Timestamp(raw_array[i][0])\n raw_array[i][0] = timestamp\n\n return","def strToFloatArray(line, delim=\",\"):\n\tarr = line.split(delim)\n\treturn [float(a) for a in arr]","def data_convert2float32 (self, data):\r\n data = data.astype(np.float32)\r\n\r\n return data","def parse_xml_matrix(mat_element):\n rows = int(mat_element.find(\"rows\").text)\n cols = int(mat_element.find(\"cols\").text)\n type_flag = mat_element.find(\"dt\").text\n if type_flag == \"f\":\n dtype = np.float32\n elif type_flag == \"d\":\n dtype = np.float64\n else:\n raise ValueError(\"dtype flag \" + type_flag + \" not supported.\")\n data_string = mat_element.find(\"data\").text\n data = np.array([float(part) for part in data_string.strip().split(\" \") if len(part) > 0])\n return data.reshape((rows, cols)).astype(dtype)","def solution(array):\n array1 = np.array(array)\n array2 = array1.astype(float)\n return array2","def test_renderer_works_correctly_with_numpy_floating(self):\n data = numpy.float32(0.0)\n rendered = self.renderer.render(\n data=data, media_type=\"application/json\", renderer_context={}\n )\n reloaded = orjson.loads(rendered)\n\n self.assertEqual(reloaded, data)","def convertToFloat(boolean: bool) -> float:\n ...","def transform_mat(matrix):\n delta = 1e-5\n matrix = matrix + delta\n return matrix","def matrix2bytes(matrix):\n return bytes(sum(matrix, []))","def _image_to_vector(image):\n return image.flatten().astype(float)","def to_mat(self) -> np.matrix:\n raise NotImplementedError","def floats(self) -> List[NumericType]:\n return [float(v) for v in self._record]","def test_float(self):\n htype = h5t.py_create('f')\n self.assertIsInstance(htype, h5t.TypeFloatID)","def process(self, mat):","def _convert_data(self, data):\n if isinstance(data, np.ndarray):\n data = data.astype(float_save(), copy=False)\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return KaldiMatrix(data)\n\n if isinstance(data, KaldiMatrix):\n if self.compress:\n return KaldiCompressedMatrix.compress(data, self.compression_method)\n return data\n\n if isinstance(data, KaldiCompressedMatrix):\n if not self.compress:\n return data.to_matrix()\n return data\n\n raise ValueError(\"Data is not ndarray or KaldiMatrix\")","def _decode_float(fp):\n return struct.unpack('>f', fp.read(4))[0]","def _values_number(self) -> ndarray:\n if 'f' in self._data:\n arr_dtype = 'float64'\n else:\n arr_dtype = 'int64'\n\n col_num: int = 0\n for kind, arr in self._data.items():\n if kind in 'ifb':\n col_num += arr.shape[1]\n shape: Tuple[int, int] = (len(self), col_num)\n\n v: ndarray = np.empty(shape, dtype=arr_dtype, order='F')\n for i, (_, col_arr, dtype, _) in enumerate(self._col_info_iter(with_arr=True)):\n if dtype in 'ifb':\n v[:, i] = col_arr\n return v","def test_op_add_array_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n o = a + 1.0\n\n old_a = numpy.empty_like(a)\n old_o = numpy.empty_like(o)\n old_a[:] = a[:]\n old_o[:] = o[:]\n expect = a + o\n\n offl_a = stream.bind(a)\n offl_r = offl_a + o\n offl_a.update_host()\n r = offl_r.update_host().array\n stream.sync()\n\n self.assertEqual(r.shape, a.shape)\n self.assertEqual(r.dtype, a.dtype)\n self.assertTrue((a == old_a).all(),\n \"Input array operand 1 must not be modified: \"\n \"{0} should be {1}\".format(a, old_a))\n self.assertTrue((o == old_o).all(),\n \"Input array operand 2 must not be modified: \"\n \"{0} should be {1}\".format(o, old_o))\n self.assertTrue((r == expect).all(),\n \"Array contains unexpected values: \"\n \"{0} should be {1}\".format(r, expect))","def ts_float32(val):\n return np.float64(val)","def array_to_gf_array(data, m_gf = 1, floatprec = 64):\n data_binary = []\n for val in data:\n data_binary.append(binary_string_to_gf_elements(float_to_binary(val, floatprec), m_gf))\n return np.array(data_binary,dtype=int)","def test_op_zero_float(self):\n\n device = pymic.devices[0]\n stream = device.get_default_stream()\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\n offl_a = stream.bind(a)\n offl_a.zero()\n offl_a.update_host()\n stream.sync()\n self.assertEqual(sum(a), 0.0,\n \"Array should be all zeros.\")","def csr2tensor(self, matrix: sp.csr_matrix):\n matrix = matrix.tocoo()\n x = torch.sparse.FloatTensor(\n torch.LongTensor(np.array([matrix.row, matrix.col])),\n torch.FloatTensor(matrix.data.astype(np.float32)),\n matrix.shape,\n ).to(self.device)\n return x","def to_float32(elem):\n return elem.astype(np.float32)","def to_matrix(array):\n return Matrix(array.tolist())","def PLCTYPE_ARR_REAL(n: int) -> Type[Array]:\n return c_float * n","def save_float16_npy(data, path):\n np.save(path, data.astype(np.float16))","def load_file(file_name) -> np.ndarray:\r\n reader = csv.reader(open(file_name, \"r\"), delimiter=',')\r\n x_rdr = list(reader)\r\n return np.array(x_rdr).astype('float')","def _resolve_float(self, item):\n if not type(item) in (float, int, str, np.number) and not np.isscalar(item):\n if isinstance(item, np.ndarray) and item.shape == ():\n item = float(item)\n else:\n item = item[0]\n\n return item","def write_float32(self, f: float) -> None:\n self.buffer += struct.pack(\"<f\", f)","def as_matrix(self):\n return self._data","def _fileToMatrix(file_name):\r\n # TODO: np.loadtxt() might be an alternative\r\n # try:\r\n if 1 < 3:\r\n lres = []\r\n for line in open(file_name, 'r').readlines():\r\n if len(line) > 0 and line[0] not in ('%', '#'):\r\n lres.append(list(map(float, line.split())))\r\n res = lres\r\n else:\r\n fil = open(file_name, 'r')\r\n fil.readline() # rudimentary, assume one comment line\r\n lineToRow = lambda line: list(map(float, line.split()))\r\n res = list(map(lineToRow, fil.readlines()))\r\n fil.close() # close file could be omitted, reference counting should do during garbage collection, but...\r\n\r\n while res != [] and res[0] == []: # remove further leading empty lines\r\n del res[0]\r\n return res\r\n # except:\r\n print('could not read file ' + file_name)","def test_float_log(self):\n htype = h5t.py_create('f', logical=True)\n self.assertIsInstance(htype, h5t.TypeFloatID)","def _frz(a):\n if a.ndim == 0:\n a.shape = (1,)\n return a"],"string":"[\n \"def matrixToFloatMatrix(matrix):\\n\\n float_matrix = [matrix(i, j) for i in xrange(4) for j in xrange(4)]\\n\\n outMatrix = OpenMaya.MFloatMatrix()\\n OpenMaya.MScriptUtil.createFloatMatrixFromList(float_matrix , outMatrix)\\n\\n return outMatrix\",\n \"def itemsToFloat(self):\\n returnvalue = Matrix()\\n for row in self._value:\\n newRow = list()\\n for item in row:\\n newRow.append(float(item))\\n returnvalue.addRow(*newRow)\\n return returnvalue\",\n \"def _cast_to_float64(matrix):\\n return matrix.astype(np.float64) if matrix.dtype != np.float64 else matrix\",\n \"def read_as_float_matrix(path):\\n\\n bgr_image = cv2.imread(path)\\n return bgr_image.astype(np.float32) / 255.\",\n \"def getFileAsFloatMatrix(dirPath, columns, delim=\\\",\\\"):\\n\\tmat = list()\\n\\tfor rec in fileSelFieldsRecGen(dirPath, columns, delim):\\n\\t\\tmat.append(asFloatList(rec))\\n\\treturn mat\",\n \"def as_matrix(self) -> types.Matrix:\",\n \"def write_float_array(f, path, values, dtype='f8'):\\n dset = f.create_dataset(path, (len(values),), dtype=dtype)\\n dset[:] = values\\n f.flush()\",\n \"def test_convert_numpy_to_libffm(self):\\n file = tempfile.NamedTemporaryFile(delete=False)\\n\\n # write data to libffm format\\n write_data_to_xlearn_format(self.X, self.y, file.name, fields=self.fields)\\n\\n # read back data from file\\n X_true, y_true, field_true = self._read_libffm_file(file.name)\\n file.close()\\n if os.path.exists(file.name):\\n os.remove(file.name)\\n\\n assert np.all(np.isclose(self.X, X_true))\\n assert np.all(self.y.ravel() == y_true.ravel())\\n assert np.all(self.fields.ravel() == field_true.ravel())\",\n \"def floatArrayToPrt(float_array):\\n\\n util = OpenMaya.MScriptUtil() \\n util.createFromList(float_array, len(float_array))\\n\\n return util.asFloatPtr()\",\n \"def new_float(*args, **kwargs):\\n return array.array(FLOAT_TYPECODE, *args, **kwargs)\",\n \"def getFloatArray2D(self) -> typing.List[typing.List[float]]:\\n ...\",\n \"def write_matrix_to_binary(file_name, val):\\n with open(file_name, 'wb') as file:\\n nrow = val.shape[0]\\n ncol = val.shape[1]\\n file.write(int32_to_bytes(nrow) + int32_to_bytes(ncol) + val.astype(float).tobytes(order='C'))\",\n \"def removeMatrixTranslate(matrix):\\n\\n float_matrix = [matrix(i, j) for i in xrange(4) for j in xrange(4)]\\n for idx in range(12, 15):\\n float_matrix[idx] = 0.0\\n \\n outMatrix = OpenMaya.MFloatMatrix()\\n OpenMaya.MScriptUtil.createFloatMatrixFromList(float_matrix , outMatrix)\\n\\n return outMatrix\",\n \"def to_matrix(self, normalize: bool = True) -> jnp.ndarray:\\n return NotImplemented # pragma: no cover\",\n \"def convertToFloatArray(booleanArray: typing.List[bool]) -> typing.List[float]:\\n ...\",\n \"def readCSVasFloat(filename):\\n returnArray = []\\n lines = open(filename).readlines()\\n for line in lines:\\n line = line.strip().split(\\\",\\\")\\n if len(line) > 0:\\n returnArray.append(np.array([np.float32(x) for x in line]))\\n\\n returnArray = np.array(returnArray)\\n return returnArray\",\n \"def cfloat(queue=None):\\n return np.float32\",\n \"def read_float(filename):\\n\\tf = open(filename, \\\"r\\\")\\n\\tarr = np.fromfile(f, dtype='>f4')\\n\\treturn arr\",\n \"def convert_stream(self, stream):\\n return np.fromstring(stream, \\\"Float32\\\")\",\n \"def get_float_data(dataframe):\\n dataframe = dataframe[np.isfinite(dataframe.TIME_StartTime)]\\n float_cols = [isfloatarray(col) for col in dataframe.values.T]\\n return (dataframe.T[float_cols].T).astype(float)\",\n \"def column(matrix, i):\\r\\n return [row[i] for row in matrix if isfloat(row[i])]\",\n \"def np_floats(self):\\n # A numpy-array packaging of the floating-point input parameters\\n return np.array([self.chi1, self.chi2, self.chip, self.thetaJ, self.m1, self.m2,\\n self.distance, self.alpha0, self.phic, self.fref], dtype=np.float64)\",\n \"def test_op_fillfrom_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n offl_r = stream.empty_like(a)\\n offl_r.fillfrom(a)\\n r = offl_r.update_host().array\\n stream.sync()\\n self.assertTrue((a == r).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(a, r))\",\n \"def getFileAsFiltFloatMatrix(dirPath, filt, columns, delim=\\\",\\\"):\\n\\tmat = list()\\n\\tfor rec in fileFiltSelFieldsRecGen(dirPath, filt, columns, delim):\\n\\t\\tmat.append(asFloatList(rec))\\n\\treturn mat\",\n \"def test_op_one_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n offl_a = stream.bind(a)\\n offl_a.one()\\n offl_a.update_host()\\n stream.sync()\\n self.assertTrue((a == 1.0).all(),\\n \\\"Array should be all one.\\\" + str(a))\",\n \"def __type_of_elements_correct_long_Numpy_matrix(self):\\n strTestName = 'Float elements in a Numpy matrix (correct)'\\n RxCSObject = _RxCSobject()\\n\\n RxCSObject.paramAddMan('parameter1', 'Numpy matrix #1')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramTypeEl('parameter1', (int, float))\\n\\n RxCSObject.paramAddMan('parameter2', 'Numpy vector #2')\\n RxCSObject.paramType('parameter2', np.ndarray)\\n RxCSObject.paramTypeEl('parameter2', (int, float))\\n\\n RxCSObject.parameter1 = np.random.randn(1e2,1e2)\\n RxCSObject.parameter2 = np.random.randn(1e2,1e2)\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def test_convert_matrix():\\n foo = Value(matrices=[[1.0, 2.0], [-2.0, 1.0]])\\n assert foo.matrices[0][0][1].value == 2.0\\n assert foo.matrices[0][1][0].value == -2.0\",\n \"def _numpy_2_native_matrix(numpy_mat):\\n\\n # Create native matrix object\\n packed_vec = _pack_numpy_matrix(numpy_mat)\\n return rqrmilib.create_matrix(packed_vec)\",\n \"def wrapDBMatrix(self,mat):\\n return mat.todense()\",\n \"def uint8_to_float(im: np.array):\\n if im.dtype == np.float32:\\n warnings.warn(\\\"Image is already np.float32\\\")\\n return im\\n im = im.astype(np.float32) / 255\\n return im\",\n \"def uint8_to_float(im: np.array):\\n if im.dtype == np.float32:\\n warnings.warn(\\\"Image is already np.float32\\\")\\n return im\\n im = im.astype(np.float32) / 255\\n return im\",\n \"def getFloat(self):\\n try:\\n data, n = self._buf.pop(_F_LEN)\\n \\n if n != _F_LEN:\\n raise SerializationError('There is not enough data left.')\\n \\n return _F_STRUCT.unpack(data)[0]\\n except StructError as e:\\n raise SerializationError('Data is not a valid float: '\\n '{0}'.format(e))\",\n \"def f1(self) -> float:\\n return self._matrix.f1\",\n \"def readFloat(self) -> float:\\n return self._unpack('!f', 4)\",\n \"def read_input_float_feature(feature_map, key, shape):\\n if shape is None:\\n (dim_z, dim_y, dim_x) = feature_map.feature[key + '/dim'].int64_list.value\\n else:\\n (dim_z, dim_y, dim_x) = shape\\n tensor = np.array(feature_map.feature[key].float_list.value[:]).reshape(\\n dim_z, dim_y, dim_x)\\n return tensor\",\n \"def getFileAsFloatColumn(dirPath):\\n\\tflist = list()\\n\\tfor rec in fileRecGen(dirPath, None):\\n\\t\\tflist.append(float(rec))\\n\\treturn flist\",\n \"def import_matrix(fileMatrix):\\n with open(fileMatrix) as fMat:\\n matrix = np.zeros((3,4))\\n for ligne in fMat:\\n if ligne.startswith(' 1') or ligne.startswith(' 2') or ligne.startswith(' 3'):\\n matrix[int(ligne.split()[0])-1,:] = float(ligne.split()[1]),float(ligne.split()[2]),float(ligne.split()[3]),float(ligne.split()[4])\\n return deepcopy(matrix)\",\n \"def writeMatrix(self):\\n\\t\\tpass\",\n \"def _float_metric_value(metric):\\n return metric.result().numpy().astype(float)\",\n \"def parse_matrix(lines):\\r\\n col_headers = None\\r\\n result = []\\r\\n row_headers = []\\r\\n for line in lines:\\r\\n if line[0] == '#':\\r\\n continue\\r\\n if line[0] == '\\\\t': # is header\\r\\n col_headers = map(strip, line.split('\\\\t')[1:])\\r\\n else:\\r\\n entries = line.split('\\\\t')\\r\\n result.append(map(float, entries[1:]))\\r\\n row_headers.append(entries[0])\\r\\n return col_headers, row_headers, asarray(result)\",\n \"def read_matrix_from_binary(file_name):\\n with open(file_name, 'rb') as file:\\n buffer = file.read()\\n n_row = int.from_bytes(buffer[0:4], 'little')\\n n_col = int.from_bytes(buffer[4:8], 'little')\\n matrix = numpy.frombuffer(buffer[8:], dtype=float).reshape([n_row, n_col])\\n return matrix\",\n \"def py_make_float_array(cst, op_version=None):\\n return numpy.array([cst], dtype=numpy.float32)\",\n \"def _serialize_double_matrix(m):\\n if (type(m) == ndarray and m.dtype == float64 and m.ndim == 2):\\n rows = m.shape[0]\\n cols = m.shape[1]\\n ba = bytearray(24 + 8 * rows * cols)\\n header = ndarray(shape=[3], buffer=ba, dtype=\\\"int64\\\")\\n header[0] = 2\\n header[1] = rows\\n header[2] = cols\\n copyto(ndarray(shape=[rows, cols], buffer=ba, offset=24,\\n dtype=\\\"float64\\\", order='C'), m)\\n return ba\\n else:\\n raise TypeError(\\\"_serialize_double_matrix called on a \\\"\\n \\\"non-double-matrix\\\")\",\n \"def _native_matrix_2_numpy(mat):\\n\\n if 'RQRMI matrix' not in str(mat):\\n raise ValueError('Input is not valid rqrmi matrix object')\\n return np.array(rqrmilib.matrix_to_list(mat))\",\n \"def tfidfTransform(matrix):\\n matrix_float = numpy.zeros(matrix.shape)\\n document_Total = len(matrix)\\n rows, cols = matrix.shape\\n for row in xrange(0, rows): # For each document\\n wordTotal = reduce(lambda x, y: x + y, matrix[row])\\n for col in xrange(0, cols): # For each term\\n # For consistency ensure all self.matrix values are floats\\n matrix[row][col] = float(matrix[row][col])\\n if matrix[row][col] != 0:\\n term_document_occurences = __getTermDocumentOccurences(col, matrix)\\n term_frequency = matrix[row][col] / float(wordTotal)\\n inverse_document_frequency = math.log(abs(document_Total / float(term_document_occurences)))\\n # print(term_document_occurences, term_frequency, inverse_document_frequency, term_frequency *\\n # inverse_document_frequency)\\n matrix_float[row][col] = term_frequency * inverse_document_frequency\\n # print(matrix_float[row][col])\\n return matrix_float\",\n \"def getMultipleFileAsFloatMatrix(dirPathWithCol, delim=\\\",\\\"):\\n\\tmat = list()\\n\\tminLen = -1\\n\\tfor path, col in dirPathWithCol:\\n\\t\\tcolVals = getFileColumnAsFloat(path, col, delim)\\n\\t\\tif minLen < 0 or len(colVals) < minLen:\\n\\t\\t\\tminLen = len(colVals)\\n\\t\\tmat.append(colVals)\\n\\t\\n\\t#make all same length\\n\\tmat = list(map(lambda li:li[:minLen], mat))\\t\\n\\treturn mat\",\n \"def make_float(array):\\n finial_array = []\\n\\n for number in array:\\n finial_array.append(float(number))\\n return finial_array\",\n \"def _serialize_matrix(matrix):\\n\\n f = BytesIO()\\n spsp.save_npz(f, matrix)\\n result = f.getvalue().hex()\\n return result\",\n \"def __type_of_elements_incorrect_float_in_Numpy_vector(self):\\n strTestName = 'Float elements in a Numpy vector (incorrect)'\\n RxCSObject = _RxCSobject()\\n\\n RxCSObject.paramAddMan('parameter1', 'Numpy vector #1')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramTypeEl('parameter1', (float))\\n\\n RxCSObject.paramAddMan('parameter2', 'Numpy vector #2')\\n RxCSObject.paramType('parameter2', np.ndarray)\\n RxCSObject.paramTypeEl('parameter2', (int))\\n\\n RxCSObject.parameter1 = np.random.randn(1e2)\\n RxCSObject.parameter2 = np.random.randn(1e2)\\n\\n self.__parametersCheck_error(RxCSObject, ElementTypeError, strTestName)\",\n \"def floatX(arr):\\n return np.asarray(arr, dtype=theano.config.floatX)\",\n \"def binfloat(filename):\\n return np.memmap(filename, dtype='float32')\",\n \"def read_float(self):\\n return self._packers[\\\"f\\\"].unpack(self.read(4))[0]\",\n \"def isfloatarray(cell):\\n try:\\n cell.astype(float)\\n return True\\n except ValueError:\\n return False\",\n \"def update_F_matrix(self, F_matrix):\\n self.F_matrix = F_matrix\",\n \"def asfloat(value):\\n float_type = 'float32'\\n\\n if isinstance(value, (np.matrix, np.ndarray)):\\n if value.dtype != np.dtype(float_type):\\n return value.astype(float_type)\\n\\n return value\\n\\n elif isinstance(value, (tf.Tensor, tf.SparseTensor)):\\n return tf.cast(value, tf.float32)\\n\\n elif issparse(value):\\n return value\\n\\n float_x_type = np.cast[float_type]\\n return float_x_type(value)\",\n \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self\\n buff.write(_struct_3I.pack(_x.base.header.seq, _x.base.header.stamp.secs, _x.base.header.stamp.nsecs))\\n _x = self.base.header.frame_id\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self\\n buff.write(_struct_5f.pack(_x.base.cell_width, _x.base.cell_height, _x.base.position.x, _x.base.position.y, _x.base.position.z))\\n length = len(self.base.points)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.base.points:\\n _x = val1\\n buff.write(_struct_3f.pack(_x.x, _x.y, _x.z))\\n length = len(self.base.intensity)\\n buff.write(_struct_I.pack(length))\\n pattern = '<%sf'%length\\n buff.write(self.base.intensity.tostring())\\n buff.write(_struct_b.pack(self.base.cost))\\n _x = self.base.label\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self\\n buff.write(_struct_2f.pack(_x.mean_height, _x.mean_intensity))\\n except struct.error as se: self._check_types(se)\\n except TypeError as te: self._check_types(te)\",\n \"def test_message_float():\\n result = True\\n\\n message = msg.Message()\\n for i in range(num_it):\\n message.appendFloat(i/128.789456)\\n if message.length != msg.HEADER_SIZE + (i+1)*msg.floatStruct.size:\\n print(\\\"Size is \\\", message.length, \\\" but should be \\\", msg.HEADER_SIZE + (i+1)*msg.floatStruct.size)\\n print(\\\"Error : message.appendFloat\\\")\\n result = False\\n\\n message.resetCursor()\\n for i in range(num_it):\\n r = message.readFloat()\\n if abs(r - i/128.789456) > 0.000001:\\n print(r, \\\" vs \\\", i/128.789456)\\n print(\\\"Error : message.read/appendFloat\\\")\\n result = False\\n\\n return result\",\n \"def readFloats(path, dimensions, header=0):\\n size = reduce(operator.mul, dimensions)\\n ra = RandomAccessFile(path, 'r')\\n try:\\n ra.skipBytes(header)\\n bytes = zeros(size * 4, 'b')\\n ra.read(bytes)\\n floats = zeros(size, 'f')\\n ByteBuffer.wrap(bytes).asFloatBuffer().get(floats)\\n return ArrayImgs.floats(floats, dimensions)\\n finally:\\n ra.close()\",\n \"def dataConvertToNumpy( self ):\\n self.featureNumpy = np.asarray( self.feature )\\n self.ClassNumpy = np.asarray( self.Class )\",\n \"def getFloatRow(self, int: int) -> typing.List[float]:\\n ...\",\n \"def j_sparse_vector_wrapper_to_scipy_spmatrix(j_obj: JavaObject):\\n indices = np.frombuffer(j_obj.getIndicesBytes(), dtype=\\\"<i4\\\")\\n values = np.frombuffer(j_obj.getValuesBytes(), dtype=\\\"<f8\\\")\\n size = j_obj.getSize()\\n indptr = np.array([0, indices.shape[0]], dtype=np.int32)\\n return csr_matrix((values, indices, indptr), shape=(1, size), dtype=np.float64).todok()\",\n \"def py2mat(myobj):\\n if isinstance(myobj, pandas.Series):\\n mat = r.matrix(myobj,\\n rownames=myobj.index,\\n dimnames=myobj.name)\\n else:\\n mat = r.matrix(myobj)\\n return mat\",\n \"def read_float(stream, writer_schema=None, reader_schema=None): # noqa\\n return unpack('<f', stream.read(4))[0]\",\n \"def fread_matrix(stream):\\n\\n A = fread_vector(stream)\\n while (True):\\n v = fread_vector(stream)\\n if (v.shape[0] == 0):\\n return (A)\\n A = numpy.vstack((A, v))\",\n \"def _convert_matrix(m):\\n\\n return [m[0][0], m[0][1], m[0][2], m[0][3],\\n m[2][0], m[2][1], m[2][2], m[2][3],\\n -m[1][0], -m[1][1], -m[1][2], -m[1][3],\\n m[3][0], m[3][1], m[3][2], m[3][3]]\",\n \"def decode_float(self, buf, pos):\\n return self.decode_struct(self._float_fmt, buf, pos)\",\n \"def _shorts2float(lo_byte_pair, hi_byte_pair):\\n\\tba = bytearray(struct.pack(\\\"HH\\\", lo_byte_pair, hi_byte_pair))\\n\\t[f] = struct.unpack('f', ba)\\n\\treturn f\",\n \"def __convert_two_cols(raw_array: list, num_rows: int):\\n\\n # no need to check extra row, we can go right into conversions\\n for i in range(num_rows):\\n\\n # float conversion\\n float_value = float(raw_array[i][1])\\n raw_array[i][1] = float_value\\n\\n # no need to delete an extra entry,\\n # we can just convert the existing string and assign it\\n timestamp = pd.Timestamp(raw_array[i][0])\\n raw_array[i][0] = timestamp\\n\\n return\",\n \"def strToFloatArray(line, delim=\\\",\\\"):\\n\\tarr = line.split(delim)\\n\\treturn [float(a) for a in arr]\",\n \"def data_convert2float32 (self, data):\\r\\n data = data.astype(np.float32)\\r\\n\\r\\n return data\",\n \"def parse_xml_matrix(mat_element):\\n rows = int(mat_element.find(\\\"rows\\\").text)\\n cols = int(mat_element.find(\\\"cols\\\").text)\\n type_flag = mat_element.find(\\\"dt\\\").text\\n if type_flag == \\\"f\\\":\\n dtype = np.float32\\n elif type_flag == \\\"d\\\":\\n dtype = np.float64\\n else:\\n raise ValueError(\\\"dtype flag \\\" + type_flag + \\\" not supported.\\\")\\n data_string = mat_element.find(\\\"data\\\").text\\n data = np.array([float(part) for part in data_string.strip().split(\\\" \\\") if len(part) > 0])\\n return data.reshape((rows, cols)).astype(dtype)\",\n \"def solution(array):\\n array1 = np.array(array)\\n array2 = array1.astype(float)\\n return array2\",\n \"def test_renderer_works_correctly_with_numpy_floating(self):\\n data = numpy.float32(0.0)\\n rendered = self.renderer.render(\\n data=data, media_type=\\\"application/json\\\", renderer_context={}\\n )\\n reloaded = orjson.loads(rendered)\\n\\n self.assertEqual(reloaded, data)\",\n \"def convertToFloat(boolean: bool) -> float:\\n ...\",\n \"def transform_mat(matrix):\\n delta = 1e-5\\n matrix = matrix + delta\\n return matrix\",\n \"def matrix2bytes(matrix):\\n return bytes(sum(matrix, []))\",\n \"def _image_to_vector(image):\\n return image.flatten().astype(float)\",\n \"def to_mat(self) -> np.matrix:\\n raise NotImplementedError\",\n \"def floats(self) -> List[NumericType]:\\n return [float(v) for v in self._record]\",\n \"def test_float(self):\\n htype = h5t.py_create('f')\\n self.assertIsInstance(htype, h5t.TypeFloatID)\",\n \"def process(self, mat):\",\n \"def _convert_data(self, data):\\n if isinstance(data, np.ndarray):\\n data = data.astype(float_save(), copy=False)\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return KaldiMatrix(data)\\n\\n if isinstance(data, KaldiMatrix):\\n if self.compress:\\n return KaldiCompressedMatrix.compress(data, self.compression_method)\\n return data\\n\\n if isinstance(data, KaldiCompressedMatrix):\\n if not self.compress:\\n return data.to_matrix()\\n return data\\n\\n raise ValueError(\\\"Data is not ndarray or KaldiMatrix\\\")\",\n \"def _decode_float(fp):\\n return struct.unpack('>f', fp.read(4))[0]\",\n \"def _values_number(self) -> ndarray:\\n if 'f' in self._data:\\n arr_dtype = 'float64'\\n else:\\n arr_dtype = 'int64'\\n\\n col_num: int = 0\\n for kind, arr in self._data.items():\\n if kind in 'ifb':\\n col_num += arr.shape[1]\\n shape: Tuple[int, int] = (len(self), col_num)\\n\\n v: ndarray = np.empty(shape, dtype=arr_dtype, order='F')\\n for i, (_, col_arr, dtype, _) in enumerate(self._col_info_iter(with_arr=True)):\\n if dtype in 'ifb':\\n v[:, i] = col_arr\\n return v\",\n \"def test_op_add_array_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n o = a + 1.0\\n\\n old_a = numpy.empty_like(a)\\n old_o = numpy.empty_like(o)\\n old_a[:] = a[:]\\n old_o[:] = o[:]\\n expect = a + o\\n\\n offl_a = stream.bind(a)\\n offl_r = offl_a + o\\n offl_a.update_host()\\n r = offl_r.update_host().array\\n stream.sync()\\n\\n self.assertEqual(r.shape, a.shape)\\n self.assertEqual(r.dtype, a.dtype)\\n self.assertTrue((a == old_a).all(),\\n \\\"Input array operand 1 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(a, old_a))\\n self.assertTrue((o == old_o).all(),\\n \\\"Input array operand 2 must not be modified: \\\"\\n \\\"{0} should be {1}\\\".format(o, old_o))\\n self.assertTrue((r == expect).all(),\\n \\\"Array contains unexpected values: \\\"\\n \\\"{0} should be {1}\\\".format(r, expect))\",\n \"def ts_float32(val):\\n return np.float64(val)\",\n \"def array_to_gf_array(data, m_gf = 1, floatprec = 64):\\n data_binary = []\\n for val in data:\\n data_binary.append(binary_string_to_gf_elements(float_to_binary(val, floatprec), m_gf))\\n return np.array(data_binary,dtype=int)\",\n \"def test_op_zero_float(self):\\n\\n device = pymic.devices[0]\\n stream = device.get_default_stream()\\n a = numpy.arange(1.0, 4711.0 * 1024, dtype=float)\\n offl_a = stream.bind(a)\\n offl_a.zero()\\n offl_a.update_host()\\n stream.sync()\\n self.assertEqual(sum(a), 0.0,\\n \\\"Array should be all zeros.\\\")\",\n \"def csr2tensor(self, matrix: sp.csr_matrix):\\n matrix = matrix.tocoo()\\n x = torch.sparse.FloatTensor(\\n torch.LongTensor(np.array([matrix.row, matrix.col])),\\n torch.FloatTensor(matrix.data.astype(np.float32)),\\n matrix.shape,\\n ).to(self.device)\\n return x\",\n \"def to_float32(elem):\\n return elem.astype(np.float32)\",\n \"def to_matrix(array):\\n return Matrix(array.tolist())\",\n \"def PLCTYPE_ARR_REAL(n: int) -> Type[Array]:\\n return c_float * n\",\n \"def save_float16_npy(data, path):\\n np.save(path, data.astype(np.float16))\",\n \"def load_file(file_name) -> np.ndarray:\\r\\n reader = csv.reader(open(file_name, \\\"r\\\"), delimiter=',')\\r\\n x_rdr = list(reader)\\r\\n return np.array(x_rdr).astype('float')\",\n \"def _resolve_float(self, item):\\n if not type(item) in (float, int, str, np.number) and not np.isscalar(item):\\n if isinstance(item, np.ndarray) and item.shape == ():\\n item = float(item)\\n else:\\n item = item[0]\\n\\n return item\",\n \"def write_float32(self, f: float) -> None:\\n self.buffer += struct.pack(\\\"<f\\\", f)\",\n \"def as_matrix(self):\\n return self._data\",\n \"def _fileToMatrix(file_name):\\r\\n # TODO: np.loadtxt() might be an alternative\\r\\n # try:\\r\\n if 1 < 3:\\r\\n lres = []\\r\\n for line in open(file_name, 'r').readlines():\\r\\n if len(line) > 0 and line[0] not in ('%', '#'):\\r\\n lres.append(list(map(float, line.split())))\\r\\n res = lres\\r\\n else:\\r\\n fil = open(file_name, 'r')\\r\\n fil.readline() # rudimentary, assume one comment line\\r\\n lineToRow = lambda line: list(map(float, line.split()))\\r\\n res = list(map(lineToRow, fil.readlines()))\\r\\n fil.close() # close file could be omitted, reference counting should do during garbage collection, but...\\r\\n\\r\\n while res != [] and res[0] == []: # remove further leading empty lines\\r\\n del res[0]\\r\\n return res\\r\\n # except:\\r\\n print('could not read file ' + file_name)\",\n \"def test_float_log(self):\\n htype = h5t.py_create('f', logical=True)\\n self.assertIsInstance(htype, h5t.TypeFloatID)\",\n \"def _frz(a):\\n if a.ndim == 0:\\n a.shape = (1,)\\n return a\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7274915","0.66002345","0.6478091","0.62763405","0.59943956","0.58819616","0.58508307","0.58382297","0.58107084","0.5799","0.57688916","0.57163143","0.5654517","0.56448877","0.56354064","0.5616841","0.5608916","0.55774224","0.5566227","0.5564168","0.5546812","0.5541829","0.5540548","0.55202895","0.5496594","0.54946804","0.5492568","0.54916364","0.547437","0.54563665","0.54563665","0.54470193","0.54364645","0.5393177","0.53895694","0.5387334","0.5363123","0.53520215","0.53430223","0.53384435","0.5334494","0.53341305","0.53245634","0.53146183","0.5285846","0.5273028","0.52642286","0.5238008","0.523765","0.5222317","0.5219979","0.52159363","0.5202647","0.51958007","0.51954144","0.5186917","0.5186651","0.51865774","0.5182645","0.5175491","0.5170876","0.5157403","0.51572406","0.5155768","0.5154924","0.51546043","0.51434004","0.5143276","0.5138756","0.51293","0.51280993","0.5124409","0.51206964","0.5115387","0.51124156","0.5104317","0.50838053","0.50823843","0.5079624","0.50768137","0.5067845","0.50668705","0.5057508","0.5054954","0.50480044","0.5047006","0.5043665","0.5042352","0.50407255","0.5035432","0.5033099","0.5026417","0.50236076","0.50227505","0.5020943","0.5014699","0.501064","0.5004021","0.500301","0.5002116"],"string":"[\n \"0.7274915\",\n \"0.66002345\",\n \"0.6478091\",\n \"0.62763405\",\n \"0.59943956\",\n \"0.58819616\",\n \"0.58508307\",\n \"0.58382297\",\n \"0.58107084\",\n \"0.5799\",\n \"0.57688916\",\n \"0.57163143\",\n \"0.5654517\",\n \"0.56448877\",\n \"0.56354064\",\n \"0.5616841\",\n \"0.5608916\",\n \"0.55774224\",\n \"0.5566227\",\n \"0.5564168\",\n \"0.5546812\",\n \"0.5541829\",\n \"0.5540548\",\n \"0.55202895\",\n \"0.5496594\",\n \"0.54946804\",\n \"0.5492568\",\n \"0.54916364\",\n \"0.547437\",\n \"0.54563665\",\n \"0.54563665\",\n \"0.54470193\",\n \"0.54364645\",\n \"0.5393177\",\n \"0.53895694\",\n \"0.5387334\",\n \"0.5363123\",\n \"0.53520215\",\n \"0.53430223\",\n \"0.53384435\",\n \"0.5334494\",\n \"0.53341305\",\n \"0.53245634\",\n \"0.53146183\",\n \"0.5285846\",\n \"0.5273028\",\n \"0.52642286\",\n \"0.5238008\",\n \"0.523765\",\n \"0.5222317\",\n \"0.5219979\",\n \"0.52159363\",\n \"0.5202647\",\n \"0.51958007\",\n \"0.51954144\",\n \"0.5186917\",\n \"0.5186651\",\n \"0.51865774\",\n \"0.5182645\",\n \"0.5175491\",\n \"0.5170876\",\n \"0.5157403\",\n \"0.51572406\",\n \"0.5155768\",\n \"0.5154924\",\n \"0.51546043\",\n \"0.51434004\",\n \"0.5143276\",\n \"0.5138756\",\n \"0.51293\",\n \"0.51280993\",\n \"0.5124409\",\n \"0.51206964\",\n \"0.5115387\",\n \"0.51124156\",\n \"0.5104317\",\n \"0.50838053\",\n \"0.50823843\",\n \"0.5079624\",\n \"0.50768137\",\n \"0.5067845\",\n \"0.50668705\",\n \"0.5057508\",\n \"0.5054954\",\n \"0.50480044\",\n \"0.5047006\",\n \"0.5043665\",\n \"0.5042352\",\n \"0.50407255\",\n \"0.5035432\",\n \"0.5033099\",\n \"0.5026417\",\n \"0.50236076\",\n \"0.50227505\",\n \"0.5020943\",\n \"0.5014699\",\n \"0.501064\",\n \"0.5004021\",\n \"0.500301\",\n \"0.5002116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7982614"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94590,"cells":{"query":{"kind":"string","value":"Set the parameters for the TensorCoFi"},"document":{"kind":"string","value":"def setParams(self,dim=20, nIter=5, lamb=0.05, alph=40):\n self._dim = dim\n self._nIter = nIter\n self._lamb = lamb\n self._alph = alph"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_parameters(self, **kwargs):\n self.__multi_layer_perceptron.set_params(**kwargs)","def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.gtg.set_parameters(self.parameters)\n self.avoidobstacles.set_parameters(self.parameters)\n self.wall.set_parameters(self.parameters)","def set_params(self, **parameters):\n for parameter, value in parameters.items():\n if parameter == 'predictor':\n if isinstance(value, chainer.Link):\n del self.predictor\n with self.init_scope():\n self.predictor = value\n else:\n assert False, 'predictor is not Chain instance'\n elif parameter in ['lossfun', 'accfun', 'device']:\n setattr(self, parameter, value)\n else:\n self.sk_params.update({parameter: value})\n return self","def set_params(self, params: Dict):\n\n if params['training_instances'] is not None:\n self.training_instances = params['training_instances']\n if params['n'] is not None:\n self.n = params['n']\n if params['lda'] is not None:\n self.lda = params['lda']\n if params['verbose'] is not None:\n self.verbose = params['verbose']\n\n self.num_features = self.training_instances[0].get_feature_count()\n self.w = None\n self.b = None","def _set_parameters(self, parameters):\n self.parameters = parameters\n self._set_points_and_weights()","def setParameters(self):\n\n # Set the parameters\n self.taux = 24.2\n self.mu = 0.23\n self.G = 33.75\n self.alpha_0 = 0.05\n self.delta = 0.0075\n self.p = 0.50\n self.I0 = 9500.0\n self.kparam = 0.55","def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.blending.set_parameters(self.parameters)","def set_hyperparams(self, params):","def set_parameters(self, new_param):\n\n current_idx = 0\n for idx, param in enumerate(self.__network.parameters()):\n temp_param = \\\n new_param[current_idx:current_idx + self.__net_sizes[idx]]\n temp_param = temp_param.reshape(self.__net_shapes[idx])\n param.data = tr.from_numpy(temp_param).float()\n current_idx += self.__net_sizes[idx]","def set_params(self, params):\n cpt = 0\n for param in self.parameters():\n tmp = np.product(param.size())\n\n if torch.cuda.is_available():\n param.data.copy_(torch.from_numpy(\n params[cpt:cpt + tmp]).view(param.size()).cuda())\n else:\n param.data.copy_(torch.from_numpy(\n params[cpt:cpt + tmp]).view(param.size()))\n cpt += tmp","def set_params(self):\n raise NotImplementedError","def set_training_parameters(\n self,\n config: ConfigDict,\n len_train: int,\n len_test: int,\n ):\n self.configure_steps(config, len_train, len_test)\n self.configure_reporting(config)\n self.configure_training_functions(config)","def define_parameters(self):\n self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))\n self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels))","def define_parameters(self):\n self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))\n self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels))","def set_parameters(self, **kwargs):\n self.__select_k_best.set_params(**kwargs)","def params(self,new):\n self._params = new\n self._config_set()\n self._make_model()","def set_params(self, new_params: torch.Tensor) -> None:\n assert new_params.size() == self.get_params().size()\n progress = 0\n for pp in list(self.net.parameters()):\n cand_params = new_params[progress: progress +\n torch.tensor(pp.size()).prod()].view(pp.size())\n progress += torch.tensor(pp.size()).prod()\n pp.data = cand_params","def _set_controller_parameters(self, P=None, I=None, D=None):\n pass","def init_params(self):\n self.conv = Conv(self.conv_layers[0][-1], self.out_channels, padding=self.padding,stride=self.stride)\n self.W = torch.randn(self.num_labels, self.cout_numel, requires_grad=True)\n self.T = torch.randn(self.num_labels, self.num_labels, requires_grad=True)","def set_params(self):\r\n pass","def set_parameters(self, par):\n try:\n for l in self.cell.layers:\n r_curve = cmf.VanGenuchtenMualem(\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\n )\n r_curve.w0 = r_curve.fit_w0()\n l.soil = r_curve\n self.cell.saturated_depth = 0.5\n self.gw.potential = self.cell.z - 0.5\n except RuntimeError as e:\n sys.stderr.write(\"Set parameters failed with:\\n\" + str(par) + \"\\n\" + str(e))\n raise","def setup_parameters(self):\n structure = self.ctx.structure_initial_primitive\n ecutwfc = []\n ecutrho = []\n\n for kind in structure.get_kind_names():\n try:\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\n cutrho = dual * cutoff\n ecutwfc.append(cutoff)\n ecutrho.append(cutrho)\n except KeyError as exception:\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\n\n natoms = len(structure.sites)\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\n\n self.ctx.inputs['parameters'] = {\n 'CONTROL': {\n 'restart_mode': 'from_scratch',\n 'tstress': self.ctx.protocol['tstress'],\n },\n 'SYSTEM': {\n 'ecutwfc': max(ecutwfc),\n 'ecutrho': max(ecutrho),\n 'smearing': self.ctx.protocol['smearing'],\n 'degauss': self.ctx.protocol['degauss'],\n 'occupations': self.ctx.protocol['occupations'],\n },\n 'ELECTRONS': {\n 'conv_thr': conv_thr,\n }\n }","def initialize_parameters():\n\n W1 = tf.get_variable('W1', [3,3,3,64], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W2 = tf.get_variable('W2', [3,3,64,128], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W3 = tf.get_variable('W3', [3,3,128,256], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W4 = tf.get_variable('W4', [3,3,256,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W5 = tf.get_variable('W5', [3,3,512,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n\n ### END CODE HERE ###\n\n parameters = {\"W1\": W1,\n \"W2\": W2,\n \"W3\": W3,\n \"W4\": W4,\n \"W5\": W5\n }\n\n return parameters","def setParams(self, tfInputGraph=None, inputMapping=None, outputMapping=None, tfHParms=None):\n super(TFTransformer, self).__init__()\n kwargs = self._input_kwargs\n # Further conanonicalization, e.g. converting dict to sorted str pairs happens here\n return self._set(**kwargs)","def __init__(self, graph=None, *args, **kwargs):\n # Set TensorFlow graph. All TF code will work on this graph.\n self.graph = graph or tf.Graph()\n self.SetParams(*args, **kwargs)","def __init__(self, tfInputGraph=None, inputMapping=None, outputMapping=None, tfHParms=None):\n super(TFTransformer, self).__init__()\n kwargs = self._input_kwargs\n self.setParams(**kwargs)","def set_parameters(self, We1,be1, We2, be2, We3, be3, Wmu, bmu, Wstd, bstd, Wd1, bd1, Wd2, bd2, Wd3, bd3):\r\n self.en_fc1.weight=nn.Parameter(We1)\r\n self.en_fc1.bias=nn.Parameter(be1)\r\n \r\n self.en_fc2.weight=nn.Parameter(We2)\r\n self.en_fc2.bias=nn.Parameter(be2)\r\n \r\n self.en_fc3.weight=nn.Parameter(We3)\r\n self.en_fc3.bias=nn.Parameter(be3)\r\n \r\n self.en_mu.weight=nn.Parameter(Wmu)\r\n self.en_mu.bias=nn.Parameter(bmu)\r\n \r\n self.en_log.weight=nn.Parameter(Wstd)\r\n self.en_log.bias=nn.Parameter(bstd)\r\n \r\n self.de_fc1.weight=nn.Parameter(Wd1)\r\n self.de_fc1.bias=nn.Parameter(bd1)\r\n \r\n self.de_fc2.weight=nn.Parameter(Wd2)\r\n self.de_fc2.bias=nn.Parameter(bd2)\r\n \r\n self.de_fc3.weight=nn.Parameter(Wd3)\r\n self.de_fc3.bias=nn.Parameter(bd3)\r\n \r\n return","def _set_params(self,x):\r\n self.k._set_params(x)","def set_params(self, **params):\n if('threshold' in params.keys()):\n self.threshold = params['threshold']\n if('subsample' in params.keys()):\n self.subsample = params['subsample']\n if('estimator' in params.keys()):\n self.estimator = params['estimator']\n if('n_folds' in params.keys()):\n self.n_folds = params['n_folds']\n if('stratify' in params.keys()):\n self.stratify = params['stratify']\n if('random_state' in params.keys()):\n self.random_state = params['random_state']\n if('n_jobs' in params.keys()):\n self.n_jobs = params['n_jobs']","def set_shape_params(self, params):\n self.alpha = params[0]\n self.beta = params[1]\n self.gamma = params[2]\n self.c500 = params[3]\n self.P0 = params[4]","def set_params(self, **kwargs):\n\n # We don't want non-functional arguments polluting kwargs\n params = kwargs.copy()\n for k in ['function', 'target']:\n params.pop(k, None)\n\n self.kwargs.update(params)\n BaseEstimator.set_params(self, **kwargs)","def _set_model_parameters(self, verbose=False):\n from scipy.special import gamma\n\n z0 = self.z0\n\n # set parameters that are constants\n p_v, d_v, cs0, sigma, vout0 = (1, 2, 6.7, 0.1, 25.0)\n p_vB, d_vB, Mach0, p_M, d_M = (4, 2, 0.5, 1, 3)\n\n # calculate amplitudes that make the pdf integrate to 1\n A_v = np.log(10)*p_v/gamma(d_v/p_v)\n A_cs = np.log(10)/np.sqrt(2*np.pi)/sigma\n A_vB = np.log(10)*p_vB/gamma(d_vB/p_vB)\n A_M = np.log(10)*p_M/gamma(d_M/p_M)\n\n # store them in dictionaries\n self.cool_params = dict(A_v=A_v, p_v=p_v, d_v=d_v,\n A_cs=A_cs, cs0=cs0, sigma=sigma, vout0=vout0)\n self.hot_params = dict(A_vB=A_vB, p_vB=p_vB, d_vB=d_vB,\n A_M=A_M, Mach0=Mach0,p_M=p_M,d_M=d_M)\n # SN related parameters that set the reference values for loading factors\n self.params = dict(Esn=1.e51*au.erg, mstar=95.5*au.M_sun, vcool=200*au.km/au.s,\n Mej=10.*au.M_sun, ZSN=0.2, ZISM0=0.02)\n self.params['vej'] = np.sqrt(2.0*self.params['Esn']/self.params['Mej']).to('km/s')\n self.ref_params = dict(Mref=self.params['mstar'],\n pref=self.params['Esn']/(2*self.params['vcool']),\n Eref=self.params['Esn'],\n Zref=self.params['Mej']*self.params['ZSN'])\n\n # coefficients used in conversion from mass to other PDFs\n self.vp = (self.ref_params['pref']/self.params['mstar']).to('km/s').value\n self.vE = np.sqrt(self.ref_params['Eref']/self.params['mstar']).to('km/s').value\n self.Ze = (self.ref_params['Zref']/self.params['mstar']).cgs.value\n\n # parameters for scaling relations from Paper~I\n a = np.array(fit_alpha[z0])\n b = np.array(fit_beta[z0])\n\n self.scaling_params = dict(a=a, b=b)\n if z0 == '2H':\n self.cool_params['vout0'] = 45\n self.cool_params['cs0'] = 7.5\n elif z0 == '500':\n self.cool_params['vout0'] = 45\n self.cool_params['cs0'] = 8.5\n elif z0 == '1000':\n self.cool_params['vout0'] = 60\n self.cool_params['cs0'] = 10.0\n self.scaling_params['A'] = np.round(10.**(np.array(self.scaling_params['a'])),2)\n self.scaling_params['p'] = 1.+np.array(self.scaling_params['b'])\n self.enum=dict(M_cool=0, M_int=1, M_hot=2, M_total=3,\n p_cool=4, p_int=5, p_hot=6, p_total=7,\n E_cool=8, E_int=9, E_hot=10, E_total=11,\n Z_cool=12, Z_int=13, Z_hot=14, Z_total=15)\n\n # print parameters\n if verbose:\n self.show_parameters()","def set_params(self, params):","def _initialize_parameters(self):\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k * 2],\n initializer=self.initializer)\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k * 2],\n initializer=self.initializer)","def _set_training_params(self, params):\n self.lyapunov_hybrid_system.lyapunov_relu.load_state_dict(\n params[\"lyap_relu_params\"])\n if not self.R_options.fixed_R:\n self.R_options._variables = params[\"R_params\"].clone()\n if isinstance(self.lyapunov_hybrid_system.system,\n feedback_system.FeedbackSystem):\n self.lyapunov_hybrid_system.system.controller_network.\\\n load_state_dict(params[\"controller_params\"])","def set_params(self, **params):\n return self.forest.set_params(**params)","def set_parameters(self, params):\n self.kp = params.pgain","def set_params(self, **kwargs):\n ...","def _initialize_parameters(self):\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k],\n initializer=self.initializer)\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k],\n initializer=self.initializer)","def set_tf_params(model, write_graph=False):\n\n with tf.gfile.FastGFile(os.path.join(\n MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')\n\n if write_graph:\n # Write graph to file so that it can be visualized using TensorBoard\n summary_writer = tf.summary.FileWriter('data', graph=graph_def)\n\n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True # Do not allocale the whole GPU mem\n with tf.Session(config=config) as sess:\n copy_inception(sess, model)","def set_params(self):\n \n lo, hi = self.R.get((self.h, self.w, self.m), (0.0, 0.0))\n params.update({\n 'gamma' : 1.0, # minesweeper is a finite horizon game\n 'epsilon': 0.0,\n 'K': 16,\n 'R_lo': lo,\n 'R_hi': hi,\n 'max_depth': self.h * self.w / 2,\n 'c':hi-lo\n })","def set_params(self, params):\n params = dict_to_namespace(params)\n\n # Set self.params\n self.params = Namespace()\n self.params.ndimx = params.ndimx\n self.params.model_str = getattr(params, 'model_str', 'optfixedsig')\n self.params.ig1 = getattr(params, 'ig1', 4.0)\n self.params.ig2 = getattr(params, 'ig2', 3.0)\n self.params.n1 = getattr(params, 'n1', 1.0)\n self.params.n2 = getattr(params, 'n2', 1.0)\n self.params.sigma = getattr(params, 'sigma', 1e-5)\n self.params.niter = getattr(params, 'niter', 70)\n self.params.kernel = getattr(params, 'kernel', kern_matern)\n self.params.trans_x = getattr(params, 'trans_x', False)","def set_parameters(targeted_flag='true',\r\n tv_flag='false',\r\n hinge_flag='true',\r\n cos_flag='false',\r\n interpolation='bilinear',\r\n model_type='small',\r\n loss_type='center',\r\n dataset_type='vgg',\r\n attack='CW',\r\n norm='2',\r\n epsilon=0.1,\r\n iterations=100,\r\n binary_steps=8,\r\n learning_rate=0.01,\r\n epsilon_steps=0.01,\r\n init_const=0.3,\r\n mean_loss='embeddingmean',\r\n batch_size=-1,\r\n margin=5.0,\r\n amplification=2.0):\r\n params = {}\r\n\r\n params['model_type'] = model_type\r\n params['loss_type'] = loss_type\r\n params['dataset_type'] = dataset_type\r\n params['attack'] = attack\r\n params['norm'] = norm\r\n params['epsilon'] = epsilon\r\n params['iterations'] = iterations\r\n params['binary_steps'] = binary_steps\r\n params['learning_rate'] = learning_rate\r\n params['epsilon_steps'] = epsilon_steps\r\n params['init_const'] = init_const\r\n params['mean_loss'] = mean_loss\r\n params['batch_size'] = batch_size\r\n params['targeted_flag'] = string_to_bool(targeted_flag)\r\n params['tv_flag'] = string_to_bool(tv_flag)\r\n params['hinge_flag'] = string_to_bool(hinge_flag)\r\n params['cos_flag'] = string_to_bool(cos_flag)\r\n params['margin'] = margin\r\n params['amp'] = amplification\r\n\r\n if model_type == 'small' and loss_type == 'center':\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = -1.0\r\n else:\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = 0.0\r\n\r\n if (dataset_type == 'vggsmall'):\r\n params['align_dir'] = VGG_ALIGN_160_DIR\r\n params['test_dir'] = VGG_TEST_DIR\r\n elif model_type == 'large' or dataset_type == 'casia':\r\n params['align_dir'] = ALIGN_160_DIR\r\n elif model_type == 'small':\r\n params['align_dir'] = ALIGN_96_DIR\r\n else:\r\n ValueError('ValueError: Argument must be either \"small\" or \"large\".')\r\n \r\n if interpolation == 'nearest':\r\n params['interpolation'] = cv2.INTER_NEAREST\r\n elif interpolation == 'bilinear':\r\n params['interpolation'] = cv2.INTER_LINEAR\r\n elif interpolation == 'bicubic':\r\n params['interpolation'] = cv2.INTER_CUBIC\r\n elif interpolation == 'lanczos':\r\n params['interpolation'] = cv2.INTER_LANCZOS4\r\n elif interpolation == 'super':\r\n print('finish later')\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\r\n\r\n if params['hinge_flag']:\r\n params['attack_loss'] = 'hinge'\r\n else:\r\n params['attack_loss'] = 'target'\r\n if not params['targeted_flag']:\r\n params['attack_loss'] = 'target'\r\n if norm == 'inf':\r\n norm_name = 'i'\r\n else:\r\n norm_name = '2'\r\n if params['tv_flag']:\r\n tv_name = '_tv'\r\n else:\r\n tv_name = ''\r\n if params['cos_flag']:\r\n cos_name = '_cos'\r\n else:\r\n cos_name = ''\r\n\r\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\r\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\r\n params['model_name'] = dataset_type\r\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\r\n\r\n return params","def setParameters(self, params):\n self.module._setParameters(params)\n # update parameters for learner\n self.learner.setModule(self.module)","def set_parameters(self, create_models=True, **parameters):\n flag_nn_opti = False\n\n # Set attributes\n for param, value in parameters.items():\n if param in self.DEFAULT_VALUES.keys():\n if getattr(self, param) != value:\n # We change param value\n setattr(self, param, value)\n if param in ['hidden_layers', 'lr']:\n flag_nn_opti = True\n\n else:\n raise Exception(f'Parameter {param} not known.')\n\n # Create torch instances\n if create_models and flag_nn_opti:\n self._create_networks_and_optimizer()","def define_parameters(act_func, dropout, fc_layer_units, labels, samples):\n\n\n\n # Define a training and test set\n test_size = 0.1 # training is set on 90%\n training_vec, test_vec, training_labels, test_labels = train_test_split(samples, labels, test_size=test_size)\n\n # Get the batch size\n batch_percentage = 0.1 # There is chosen to use a batch size of 10%\n batch_size = int(training_vec.shape[0] * batch_percentage)\n\n # Get the number of features\n feature_number = training_vec.shape[1]\n\n # Get the number of classes\n class_number = len(np.unique(labels))\n\n # Get the layer nodes in correct format\n int_layer_units = []\n units = fc_layer_units.split(',')\n for unit in units:\n int_layer_units.append(int(unit))\n\n # Get the dropout layers in correct format\n dropout_booleans = []\n dropouts = dropout.split(',')\n for layer in dropouts:\n layer = layer.lower()\n if layer == 'f' or layer == 'false':\n dropout_booleans.append(False)\n else:\n dropout_booleans.append(True)\n\n # Get the layer names of the neural network architecture\n layers = []\n for index, nodes in enumerate(int_layer_units):\n layers.append('fc ({})'.format(nodes))\n if dropout_booleans[index]:\n layers.append('do')\n layers = ' - '.join(layers)\n\n # Get the right activation function\n act_func = act_func.lower()\n if act_func == 'sigmoid' or act_func == 'sig' or act_func == 's':\n act_func = tf.nn.sigmoid\n act_title = 'sigmoid'\n elif act_func == 'relu' or act_func == 'r':\n act_func = tf.nn.relu\n act_title = 'ReLU'\n elif act_func == 'tanh' or act_func == 'tan' or act_func == 't':\n act_func = tf.tanh\n act_title = 'tanH'\n else:\n act_func = None\n act_title = 'none'\n\n return act_func, act_title, batch_size, class_number, feature_number, layers, dropout_booleans, int_layer_units, \\\n test_labels, test_vec, training_labels, training_vec","def set_tunable_params(self, params):\n self._tunable_params = params","def set_parameters(self):\n\n if self.model_with_set_params:\n return\n\n self._model_with_set_params = self._parameter_values.process_model(\n self._unprocessed_model, inplace=False\n )\n self._parameter_values.process_geometry(self.geometry)\n self.model = self._model_with_set_params","def set_params(self, **params):\n self.check_params(params)\n self.sk_params.update(params)\n return self","def __call__(self, params):\n logging.info('Running __call__ function...')\n batch_size = self._train_batch_size\n # For MCTS, the number of features for each trajecotry is unknown beforehand\n num_features = None\n\n if self._global_step_value % self._iterations_per_loop == 0:\n logging.info('Update iterator (gs=%d)...', self._global_step_value)\n # Feature/Labels Placeholders\n self.features_ph = {\n 'mcts_features':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_state_space],\n name='mcts_state_ph'),\n 'policy_features':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_state_space],\n name='policy_state_ph'),\n }\n self.labels_ph = {\n 'action_tensor':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_action_space],\n name='action_ph'),\n 'value_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='value_ph'),\n 'return_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='return_ph'),\n 'old_neg_logprob_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='old_neg'),\n 'mean_tensor':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_action_space],\n name='mean_ph'),\n 'logstd_tensor':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_action_space],\n name='logstd_ph'),\n 'mcts_enable_tensor':\n tf.placeholder(\n tf.bool, shape=[num_features], name='mcts_enable_ph'),\n 'policy_action_tensor':\n tf.placeholder(\n tf.float32,\n shape=[num_features, self._env_action_space],\n name='policy_action_ph'),\n 'policy_value_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='policy_value_ph'),\n 'policy_return_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='policy_return_ph'),\n 'policy_old_neg_logprob_tensor':\n tf.placeholder(\n tf.float32, shape=[num_features], name='policy_old_neg'),\n }\n # Create the dataset\n dataset = tf.data.Dataset.from_tensor_slices(\n (self.features_ph, self.labels_ph))\n dataset = dataset.shuffle(buffer_size=self._max_horizon)\n dataset = dataset.batch(batch_size, drop_remainder=True)\n\n # repeat until the loop is done\n dataset = dataset.repeat()\n if self._use_tpu:\n dataset = dataset.map(functools.partial(self._set_shapes, batch_size))\n dataset = dataset.prefetch(2)\n self._iterator = dataset.make_initializable_iterator()\n return self._iterator.get_next()\n else:\n return self._iterator.get_next()","def update(self, parameters):\n self.set_frequencies(parameters) # f_i\n self.set_coupling_weights(parameters) # w_ij\n self.set_phase_bias(parameters) # theta_i\n self.set_amplitudes_rate(parameters) # a_i\n self.set_nominal_amplitudes(parameters) # R_i","def update_parameters(self):\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\n self.update_gamma()\n self.update_gamma0()\n self.update_lambda()\n self.update_nu()\n if self.sample_xi:\n self.update_xi()","def set_params(self, **kwargs):\n\n kw_keys = list(kwargs)\n\n if 'alpha' in kw_keys:\n self.alpha = kwargs['alpha']\n\n if 'beta' in kw_keys:\n self.beta = kwargs['beta']\n\n if 'gamma' in kw_keys: \n \tself.gamma = kwargs['gamma']\n\n if 'epsilon' in kw_keys:\n self.epsilon = kwargs['epsilon']\n \n self.nact = self.highbound-self.lowbound\n self.actions = np.arange(self.nact)","def set_profile_params(self, params):\n self.alpha = params[0]\n self.beta = params[1]\n self.gamma = params[2]\n self.c500 = params[3]\n self.P0 = params[4]\n self.M500 = params[5]\n self.r500 = params[6]\n self.z = params[7]","def initializeParameters(self):\n\n self.params[2].value = False\n self.params[3].enabled = False\n self.params[7].value = True\n self.params[7].enabled = False\n self.params[8].value = None\n self.params[8].enabled = False","def initialise_parameters(self):\n # Weights\n init = select_w_init(self.w_init)\n if self.w_gain:\n gain = nn.init.calculate_gain('relu')\n init(self.relations, gain=gain)\n else:\n init(self.relations)\n\n # Biases\n if self.b_init:\n init = select_b_init(self.b_init)\n init(self.sbias)\n init(self.pbias)\n init(self.obias)","def reset_parameters(self):\n init_method = getattr(init, self.initialization)\n for layer in range(self.num_layers):\n fc = self.get_fc(layer)\n init_method(fc.weight.data)\n if self.use_bias:\n init.constant(fc.bias.data, val=0)\n init_method(self.out.weight.data)\n init.constant(self.out.bias.data, val=0)","def __init__(self, **kwargs):\n super().__init__()\n self.model_conv = models.resnet50(pretrained=True)\n for param in self.model_conv.parameters():\n param.requires_grad = False\n num_ftrs = self.model_conv.fc.in_features\n num_classes = 10\n self.model_conv.fc = nn.Linear(num_ftrs, num_classes)","def set_params(model, params): # type: ignore\n for p, p_new in zip(model.parameters(), params):\n p.data = p_new.data","def init_params(self):\n self.params = Parameters()\n self.params.add('qoff', self.qoff, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('yscale', self.yscale, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('int_bg', self.int_bg, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('Rc', self.Rc, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('sur_den', self.sur_den, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\n self.params.add('ion_depth', self.ion_depth, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)","def set_tunable_parameters(self, n_latent_features=1024, reduced_size=64, input_type='default', **kwargs):\n\n self.logger.info(\"Set parameters: n_latent_features=%s, reduced_size=%s, input_type=%s\", n_latent_features,\n reduced_size, input_type)\n\n self.n_latent_features = n_latent_features\n self.reduced_size = reduced_size\n self.input_type = input_type\n\n self._construct_model()","def transfer_parameters_call_optimization(self, mainCl, mainPt, consCall=None, consPt=None):","def set_parameter(self, output):\n self.model.set_parameter(output);","def define_parameters(self):","def init_parameters(obj, hyperparameters):\n # Initialize Global Configuration Parameter\n params = hyperparameters['global']\n setattr(obj, 'param', params)\n\n # Initialize Attributes (Pre-Checked Parameters)\n setattr(obj, 'learning_rate', params['learning_rate'])\n setattr(obj, 'loss', params['loss'])\n setattr(obj, 'max_iter', params['max_iter'])\n\n if params['loss'] == 'least_squares':\n setattr(obj, 'num_classes', 1)\n elif params['loss'] in ['binary_crossentropy', 'categorical_crossentropy', 'auto']:\n setattr(obj, 'num_classes', params['num_classes'])\n\n # Initialize Attributes (Optional Values - Based on Default Parameters)\n if 'l2_regularization' not in params or params['l2_regularization'] is None:\n setattr(obj, 'l2_regularization', 0)\n else:\n setattr(obj, 'l2_regularization', params['l2_regularization'])\n\n if 'max_bins' not in params:\n setattr(obj, 'max_bins', 255)\n else:\n setattr(obj, 'max_bins', params['max_bins'])\n\n if 'max_depth' not in params or params['max_depth'] is None:\n setattr(obj, 'max_depth', None)\n else:\n setattr(obj, 'max_depth', params['max_depth'])\n\n if 'max_leaf_nodes' not in params or params['max_leaf_nodes'] is None:\n setattr(obj, 'max_leaf_nodes', 31)\n else:\n setattr(obj, 'max_leaf_nodes', params['max_leaf_nodes'])\n\n if 'min_samples_leaf' not in params or params['min_samples_leaf'] is None:\n setattr(obj, 'min_samples_leaf', 20)\n else:\n setattr(obj, 'min_samples_leaf', params['min_samples_leaf'])\n\n if 'random_state' in params:\n setattr(obj, 'random_state', params['random_state'])\n else:\n setattr(obj, 'random_state', None)\n\n if 'scoring' in params:\n setattr(obj, 'scoring', params['scoring'])\n else:\n setattr(obj, 'scoring', None)\n\n if 'verbose' not in params or params['verbose'] is None:\n setattr(obj, 'verbose', False)\n else:\n setattr(obj, 'verbose', True)\n\n return obj","def test_set_params_2():\n tpot_obj = TPOTClassifier(generations=2)\n tpot_obj.set_params(generations=3)\n\n assert tpot_obj.generations == 3","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def set_fit_params(self):\n\n self.p0 = np.array([self.A_arr, self.T_a])\n # initial guess at A_arr and T_a\n\n self.popt, self.pcov = curve_fit(\n self.get_eta_fit, self.T_exp, self.eta_exp, p0=self.p0\n )\n\n self.A_arr = self.popt[0]\n self.T_a = self.popt[1]\n\n self.T_array = self.T_model","def Params(cls):\n p = super().Params()\n p.Define('train_task', None, 'Underlying task')\n p.Define('decode_task', None, 'Underlying task')\n p.Define('train_dataset_name', None, '')\n p.Define('decode_dataset_name', None, '')\n p.Define('train_steps_per_loop', 0, '')\n p.Define('decode_steps_per_loop', 0, '')\n return p","def _setup_misc(self, mode):\n self.lr_rate_ph = tf.Variable(0.0, name='lrn_rate', trainable=False)\n self.reuse = None if (mode == 'train') else True\n self.batch_size = self.hparams.batch_size\n if mode == 'eval':\n self.batch_size = 25","def set_params(self, **params):\n if not hasattr(self, \"_non_sklearn_base\"):\n return super().set_params(**params)\n if not (\n len(params) == 1 and\n (\"nthreads\" in params or \"n_jobs\" in params)\n ):\n self.is_fitted_ = False\n valid_params = self.get_params(deep=False)\n for k,v in params.items():\n if k not in valid_params:\n raise ValueError(\"Invalid parameter: \", k)\n setattr(self, k, v)\n return self","def __init__(self, encut, ldaul, Uparam, Jparam, name=\"DFTU_settings\"):\n\n dftu_settings = {\"LDAU\": \".TRUE.\" , \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LADAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIX\": 4}\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"ENCUT\", encut)","def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\"DFTCL_settings\"):\n\n cl_settings = {\"ISPIN\": 2, \"MAGMOM\": magmom, \"SAXIS\": None, \"LSORBIT\": None, \"LNONCOLLINEAR\": None}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIMX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"encut\", encut)","def set_default_params(self, opt):\n self.config.embed_dim = opt.embed_dim or 200\n self.config.rnn_size = opt.rnn_size or 512\n self.config.nrnn_layer = opt.nrnn_layer or 2\n self.config.rnn_dropout = opt.rnn_dropout or 0.5\n self.config.rnnout_dim = 2 * self.config.rnn_size * self.config.nrnn_layer\n ## MULTIMODAL (ATTENTION)\n self.config.cnnout_dim = opt.cnnout_dim or 512\n self.config.cnnout_w = opt.cnnout_w or 14\n self.config.cnnout_h = opt.cnnout_h or 14\n self.config.cnnout_spat = self.config.cnnout_w * self.config.cnnout_h\n self.config.multfeat_dim = opt.multfeat_dim or 512\n self.config.attfeat_dim = opt.attfeat_dim or 256\n self.config.netout_dim = opt.answer_size\n ## [attlstm] in: {2*multfeat_dim, att_rnn_s_dim} {att_rnn_size, att_rnn_s_dim}\n self.config.att_rnn_size = opt.att_rnn_size or 512\n self.config.att_rnn_nlayer = opt.att_rnn_nlayer or 1\n self.config.att_rnn_dropout = opt.att_rnn_dropout or 0.0\n # TODO: There could be a protential bugs if self.config.att_rnn_nlayer > 1\n assert(self.config.att_rnn_nlayer == 1)\n self.config.att_rnn_s_dim = self.config.att_rnn_size * self.config.att_rnn_nlayer\n\n # optimization\n self.config.max_grad_norm = opt.max_grad_norm or 0.1\n self.config.initializer_scale = 0.008","def __init__(self, train_x, train_y, test_x, test_y, Tunning_Cs=[0.001, 0.01, 0.1, 1, 10]): \n self.Cs = Tunning_Cs\n self.train_x = train_x\n self.train_y = train_y\n self.test_x = test_x \n self.test_y = test_y\n self.model = svm.SVR(kernel='rbf', gamma='auto')","def __init__(self, encut, name=\"scf_settings\"):\n InputParameters.__init__(self, name=name)\n self.update_electronic_settings(\"ENCUT\", encut)","def tuned_for_ec():\n # TODO(theosanderson): update these to true SOTA values\n hparams = contrib_training.HParams()\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\n hparams.add_hparam('batch_style', 'bucket')\n hparams.add_hparam('batch_size', 34)\n hparams.add_hparam('dilation_rate', 5)\n hparams.add_hparam('filters', 411)\n hparams.add_hparam('first_dilated_layer', 1) # This is 0-indexed\n hparams.add_hparam('kernel_size', 7)\n hparams.add_hparam('num_layers', 5)\n hparams.add_hparam('pooling', 'mean')\n hparams.add_hparam('resnet_bottleneck_factor', 0.88152)\n hparams.add_hparam('lr_decay_rate', 0.9977)\n hparams.add_hparam('learning_rate', 0.00028748)\n hparams.add_hparam('decision_threshold', 0.3746)\n hparams.add_hparam('denominator_power', 0.88)\n\n hparams.add_hparam('train_steps', 650000)\n return hparams","def set_fe_params(self, fe_params):\n self._params[0:self.k_fe] = fe_params","def set_params(self, params_):\n x_start, x_end = params_[\"lim_fit\"]\n self.find_idx_of_fit_limit(x_start, x_end)\n self.is_error_bar_for_fit = params_[\"use_error_bar\"]\n self.fitting_method1 = params_[\"method1\"]\n self.fitting_method2 = params_[\"method2\"]\n self.qty_to_min = params_[\"qty_to_min\"]\n\n for i, key in enumerate(self.params):\n # self.params[key].set(value=params_[\"val\"][i], min=params_[\"min\"][i], max=params_[\"max\"][i], vary=bool(params_[\"hold\"][i]), brute_step=params_[\"brute_step\"][i])\n if self.params[key].user_data is not None:\n if \"dontGenerate\" in self.params[key].user_data:\n continue\n self.params[key].set(value=params_[key][\"value\"], min=params_[key][\"min\"], max=params_[key][\"max\"], vary=params_[key][\"vary\"], brute_step=params_[key][\"b_step\"])","def _set_params(self, estimator_args, scaler_args, execution_args, metric_args=None, dim_reduction_args=None):\n \n # Set default values which will be used if execution arguments are not passed\n \n # Default parameters:\n self.model.overwrite = True\n self.model.debug = False\n self.model.test_size = 0.33\n self.model.cv = 0\n self.model.time_series_split = 0\n self.model.max_train_size = None\n self.model.random_state = 42\n self.model.compress = 3\n self.model.retain_data = False\n self.model.scale_hashed = True\n self.model.scale_vectors = True\n self.model.scaler = \"StandardScaler\"\n self.model.scaler_kwargs = {}\n self.model.estimator_kwargs = {}\n self.model.missing = \"zeros\"\n self.model.calc_feature_importances = False\n self.model.importances_n_repeats = 30\n self.model.lags= None\n self.model.lag_target = False\n self.model.scale_target = False\n self.model.scale_lag_target= True\n self.model.make_stationary = None\n self.model.stationarity_lags = [1]\n self.model.using_keras = False\n self.model.current_sample_as_input = True\n self.model.prediction_periods = 1\n \n # Default metric parameters:\n if metric_args is None:\n self.model.metric_args = {}\n \n # Set execution parameters\n \n # If the execution key word arguments were included in the request, get the parameters and values\n if len(execution_args) > 0:\n \n # Transform the string of arguments into a dictionary\n execution_args = utils.get_kwargs(execution_args)\n \n # Set the overwite parameter if any existing model with the specified name should be overwritten\n if 'overwrite' in execution_args:\n self.model.overwrite = 'true' == execution_args['overwrite'].lower()\n \n # Set the test_size parameter that will be used to split the samples into training and testing data sets\n # Default value is 0.33, i.e. we use 66% of the samples for training and 33% for testing\n if 'test_size' in execution_args:\n self.model.test_size = utils.atof(execution_args['test_size'])\n\n # Enable K-fold cross validation. For more information see: http://scikit-learn.org/stable/modules/cross_validation.html#multimetric-cross-validation\n # Default value is 0 in which case a simple holdout strategy based on the test_size parameter is used.\n # If cv > 0 then the model is validated used K = cv folds and the test_size parameter is ignored.\n if 'cv' in execution_args:\n self.model.cv = utils.atoi(execution_args['cv'])\n \n # Enable timeseries backtesting using TimeSeriesSplit. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html\n # This will select the a validation strategy appropriate for time series and sequential data.\n # The feature definitions must include an 'identifier' field which can be used to sort the series into the correct order.\n # The integer supplied in this parameter will split the data into the given number of subsets for training and testing.\n if 'time_series_split' in execution_args:\n self.model.time_series_split = utils.atoi(execution_args['time_series_split'])\n\n # This parameter can be used together with time_series_split.\n # It specifies the maximum samples to be used for training in each split, which allows for rolling/ walk forward validation.\n if 'max_train_size' in execution_args:\n self.model.max_train_size = utils.atoi(execution_args['max_train_size'])\n\n # Add lag observations to the feature matrix. Only applicable for Keras models.\n # An identifier field must be included in the feature definitions to correctly sort the data for this capability.\n # For e.g. if lags=2, features from the previous two samples will be concatenated as input features for the current sample.\n # This is useful for framing timeseries and sequence prediction problems into 3D or 4D data required for deep learning.\n if 'lags' in execution_args:\n self.model.lags = utils.atoi(execution_args['lags'])\n\n # Include targets in the lag observations\n # If True an additional feature will be created for each sample using the previous value of y \n if 'lag_target' in execution_args:\n self.model.lag_target = 'true' == execution_args['lag_target'].lower()\n \n # Scale the target before fitting\n # The scaling will be inversed before predictions so they are returned in the original scale \n if 'scale_target' in execution_args:\n self.model.scale_target = 'true' == execution_args['scale_target'].lower()\n\n # Scale lag values of the targets before fitting\n # Even if scale_target is set to false, the lag values of targets being used as features can be scaled by setting this to true \n if 'scale_lag_target' in execution_args:\n self.model.scale_lag_target = 'true' == execution_args['scale_lag_target'].lower()\n\n # Make the target series more stationary. This only applies to sequence prediction problems.\n # Valid values are 'log' in which case we apply a logarithm to the target values,\n # or 'difference' in which case we transform the targets into variance from the previous value.\n # The transformation will be reversed before returning predictions.\n if 'make_stationary' in execution_args:\n self.model.make_stationary = execution_args['make_stationary'].lower()\n\n # Provide lags periods for differencing\n # By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list of lags as a list.\n # e.g. 'stationarity_lags=1;12|list|int'\n if 'stationarity_lags' in execution_args:\n self.model.stationarity_lags = utils.get_kwargs_by_type({'stationarity_lags': execution_args['stationarity_lags']})['stationarity_lags']\n\n # Specify if the current sample should be used as input to the model\n # This is to allow for models that only use lag observations to make future predictions\n if 'current_sample_as_input' in execution_args:\n self.model.current_sample_as_input = 'true' == execution_args['current_sample_as_input'].lower()\n\n # Specify the number of predictions expected from the model\n # This can be used to get a model to predict the next m periods given inputs for the previous n periods.\n # This is only valid for Keras models which have a final output layer with more than one node\n if 'prediction_periods' in execution_args:\n self.model.prediction_periods = utils.atoi(execution_args['prediction_periods'])\n \n # Seed used by the random number generator when generating the training testing split\n if 'random_state' in execution_args:\n self.model.random_state = utils.atoi(execution_args['random_state'])\n \n # Compression level between 1-9 used by joblib when saving the model\n if 'compress' in execution_args:\n self.model.compress = utils.atoi(execution_args['compress'])\n \n # Flag to determine if the training and test data should be saved in the model\n if 'retain_data' in execution_args:\n self.model.retain_data = 'true' == execution_args['retain_data'].lower()\n\n # Flag to determine if feature importances should be calculated when the fit method is called\n if 'calculate_importances' in execution_args:\n self.model.calc_feature_importances = 'true' == execution_args['calculate_importances'].lower()\n\n # Sets the number of times a feature is randomly shuffled during the feature importance calculation\n if 'importances_n_repeats' in execution_args:\n self.model.importances_n_repeats = utils.atoi(execution_args['importances_n_repeats'])\n \n # Set the debug option for generating execution logs\n # Valid values are: true, false\n if 'debug' in execution_args:\n self.model.debug = 'true' == execution_args['debug'].lower()\n \n # Additional information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n # Increment log counter for the class. Each instance of the class generates a new log.\n self.__class__.log_no += 1\n\n # Create a log file for the instance\n # Logs will be stored in ..\\logs\\SKLearn Log <n>.txt\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\n \n # Create dictionary of parameters to display for debug\n self.exec_params = {\"overwrite\":self.model.overwrite, \"test_size\":self.model.test_size, \"cv\":self.model.cv,\\\n \"time_series_split\": self.model.time_series_split, \"max_train_size\":self.model.max_train_size, \"lags\":self.model.lags,\\\n \"lag_target\":self.model.lag_target, \"scale_target\":self.model.scale_target, \"make_stationary\":self.model.make_stationary,\\\n \"random_state\":self.model.random_state, \"compress\":self.model.compress, \"retain_data\":self.model.retain_data,\\\n \"calculate_importances\": self.model.calc_feature_importances, \"importances_n_repeats\": self.model.importances_n_repeats,\\\n \"debug\":self.model.debug}\n\n self._print_log(1)\n \n # If the scaler key word arguments were included in the request, get the parameters and values\n if len(scaler_args) > 0:\n \n # Transform the string of arguments into a dictionary\n scaler_args = utils.get_kwargs(scaler_args)\n \n # Set scaler arguments that will be used when preprocessing the data\n # Valid values are: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler and QuantileTransformer\n # More information here: http://scikit-learn.org/stable/modules/preprocessing.html\n if 'scaler' in scaler_args:\n self.model.scaler = scaler_args.pop('scaler')\n \n if 'missing' in scaler_args:\n self.model.missing = scaler_args.pop('missing').lower()\n \n if 'scale_hashed' in scaler_args:\n self.model.scale_hashed = 'true' == scaler_args.pop('scale_hashed').lower()\n \n if 'scale_vectors' in scaler_args:\n self.model.scale_vectors = 'true' == scaler_args.pop('scale_vectors').lower()\n \n # Get the rest of the scaler parameters, converting values to the correct data type\n self.model.scaler_kwargs = utils.get_kwargs_by_type(scaler_args) \n else:\n err = \"Arguments for scaling did not include the scaler name e.g StandardScaler\"\n raise Exception(err)\n \n # If the estimator key word arguments were included in the request, get the parameters and values\n if len(estimator_args) > 0:\n \n # Transform the string of arguments into a dictionary\n estimator_args = utils.get_kwargs(estimator_args)\n \n # Set estimator arguments that will be used when preprocessing the data\n # The parameters available will depend on the selected estimator\n # More information here: http://scikit-learn.org/stable/modules/classes.html#api-reference\n if 'estimator' in estimator_args:\n self.model.estimator = estimator_args.pop('estimator')\n \n # Set the estimator type for the model\n if self.model.estimator in self.classifiers:\n self.model.estimator_type = \"classifier\"\n elif self.model.estimator in self.regressors:\n self.model.estimator_type = \"regressor\"\n elif self.model.estimator in self.decomposers:\n self.model.estimator_type = \"decomposer\"\n elif self.model.estimator in self.clusterers:\n self.model.estimator_type = \"clusterer\"\n else:\n err = \"Unknown estimator class: {0}\".format(self.model.estimator)\n raise Exception(err)\n\n # Get the rest of the estimator parameters, converting values to the correct data type\n self.model.estimator_kwargs = utils.get_kwargs_by_type(estimator_args) \n else:\n err = \"Arguments for estimator did not include the estimator class e.g. RandomForestClassifier\"\n raise Exception(err)\n \n # If key word arguments for model evaluation metrics are included in the request, get the parameters and values\n if metric_args is not None and len(metric_args) > 0:\n # Transform the string of arguments into a dictionary\n metric_args = utils.get_kwargs(metric_args)\n \n # Get the metric parameters, converting values to the correct data type\n self.model.metric_args = utils.get_kwargs_by_type(metric_args) \n \n # If key word arguments for dimensionality reduction are included in the request, get the parameters and values\n if dim_reduction_args is not None and len(dim_reduction_args) > 0:\n # Transform the string of arguments into a dictionary\n dim_reduction_args = utils.get_kwargs(dim_reduction_args)\n \n # Set dim_reduction arguments that will be used after preprocessing the data\n # The parameters available will depend on the selected dimensionality reduction method\n # Acceptable classes are PCA, KernelPCA, IncrementalPCA, TruncatedSVD\n # More information here: http://scikit-learn.org/stable/modules/classes.html#api-reference\n if 'reduction' in dim_reduction_args:\n self.model.reduction = dim_reduction_args.pop('reduction')\n \n # Get the rest of the dim_reduction parameters, converting values to the correct data type\n self.model.dim_reduction_args = utils.get_kwargs_by_type(dim_reduction_args) \n else:\n err = \"Arguments for dimensionality reduction did not include the class e.g. PCA\"\n raise Exception(err)\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(2)","def define_parameters(all_features, act_func, dropout, fc_layer_units, training_samples, batch_perc):\n\n # Get the variables in the right format\n all_features = all_features.lower()\n if all_features == 'y' or all_features == 'yes':\n all_features = \"all conservation-based scores\"\n else:\n all_features = \"only PhastCon primate scores\"\n\n # Get the batch size\n batch_size = int(training_samples.shape[0] * batch_perc)\n\n # Get the layer nodes in correct format\n int_layer_units = []\n units = fc_layer_units.split(',')\n for unit in units:\n int_layer_units.append(int(unit))\n\n # Get the dropout layers in correct format\n dropout_booleans = []\n dropouts = dropout.split(',')\n for layer in dropouts:\n layer = layer.lower()\n if layer == 'f' or layer == 'false':\n dropout_booleans.append(False)\n else:\n dropout_booleans.append(True)\n\n # Get the layer names of the neural network architecture\n layers = []\n for index, nodes in enumerate(int_layer_units):\n layers.append('fc ({})'.format(nodes))\n if dropout_booleans[index]:\n layers.append('do')\n layers = ' - '.join(layers)\n\n # Get the right activation function\n act_func = act_func.lower()\n if act_func == 'sigmoid' or act_func == 'sig' or act_func == 's':\n act_func = tf.nn.sigmoid\n act_title = 'sigmoid'\n elif act_func == 'relu' or act_func == 'r':\n act_func = tf.nn.relu\n act_title = 'ReLU'\n elif act_func == 'tanh' or act_func == 'tan' or act_func == 't':\n act_func = tf.tanh\n act_title = 'tanH'\n else:\n act_func = None\n act_title = 'none'\n\n return all_features, act_func, act_title, batch_size, layers, dropout_booleans, int_layer_units","def set_params(self, **kwargs) -> NoReturn:\n pass","def resetParams(self):\n self.prediction = cons.init_pred # Classifier payoff - initialized to a constant initial payoff value\n self.error = cons.init_err # Classifier error - initialized to a constant initial error value\n self.fitness = cons.init_fit # Classifier fitness - initialized to a constant initial fitness value","def setParams(self, paramSet):\r\n pass","def _config_set(self):\n p = self._params\n self._config = tf.estimator.RunConfig(save_checkpoints_steps = p.save_checkpoints_steps,\n keep_checkpoint_max = p.keep_checkpoint_max,\n save_summary_steps = p.save_summary_steps\n )","def updateData(self, *args):\n # if self.move_next_option == \"R\":\n # self.restSampling()\n # elif self.move_next_option == \"A\":\n # self.addExtra()\n # else:\n # self.continueReview()\n for name, value in self.parameter_inputs.items():\n self.parameters[name] = value.value\n # directly change the value of class variables\n logMsg((\"update settings: \", self.ml_classifier_cls, name, value.value))\n setattr(self.ml_classifier_cls, name, value.value)\n\n pass","def set_parameters(self, mode, data):\n if mode == 'design' or self.local_design:\n self.new_design = True\n\n for key, dc in self.variables.items():\n if isinstance(dc, dc_cp):\n if ((mode == 'offdesign' and not self.local_design) or\n (mode == 'design' and self.local_offdesign)):\n self.get_attr(key).design = data[key]\n\n else:\n self.get_attr(key).design = np.nan","def set_model_parameters(self, cluster, model):\n\n\t\tmodel.maxnum=int(round(len(cluster)/(self.read_depth*0.9)))\n\t\tmodel.minnum=int(round(len(cluster)/(self.read_depth*1.1)))\n\t\tmodel.minsize=int(round(len(cluster)/(self.read_depth*0.9)))\n\t\tmodel.expcov=int(self.read_depth)\n\t\tmodel.maxcopy = self.max_copy","def set_params(self, params: Dict) -> None:\n self.leak.set_g(params[\"g_leak\"])\n self.kvhh.set_g(params[\"g_kvhh\"])\n self.cav.set_g(params[\"g_cav\"])\n self.kca.set_g(params[\"g_kca\"])\n self.nap.set_g(params[\"g_nap\"])\n self.tau_ca = params[\"t_ca\"]","def set_load_model_parameters(self):\n\n self.controller.set_new_model_test_input_path(self.test_input.get())\n self.controller.set_new_model_results_input_path(self.results_input.get())\n self.controller.set_new_model_running(False)","def setParams(self, inputCol=None, outputCol=None, graph=None,\n inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,\n outputMode=\"vector\"):\n kwargs = self._input_kwargs\n return self._set(**kwargs)","def _set_params_initializer(self, hparams, mode, scope):\n\t\tself.mode = mode\n\t\t# pre_train flag is used for distinguish with pre_train and fine tune\n\t\tif hparams.enable_vae:\n\t\t\t_info('Enable VAE')\n\t\t\tself.enable_vae = True\n\t\t\tself.pre_train = hparams.pre_train\n\t\telse:\n\t\t\tself.enable_vae = False\n\t\t\tself.pre_train = False\n\t\tself.dtype = tf.float32\n\t\tself.global_step = tf.Variable(0, trainable=False)\n\n\t\t# define the input for the model\n\t\tself.encoder_input_data = tf.placeholder(\n\t\t\ttf.int32, [None, None], name='encoder_input_data')\n\t\tself.decoder_input_data = tf.placeholder(\n\t\t\ttf.int32, [None, None], name='decoder_input_data')\n\t\tself.decoder_output_data = tf.placeholder(\n\t\t\ttf.int32, [None, None], name='decoder_output_data')\n\t\tself.seq_length_encoder_input_data = tf.placeholder(\n\t\t\ttf.int32, [None], name='seq_length_encoder_input_data')\n\t\tself.seq_length_decoder_input_data = tf.placeholder(\n\t\t\ttf.int32, [None], name='seq_length_decoder_input_data')\n\t\t\n\t\t# load some important hparamters\n\t\tself.unit_type = hparams.unit_type\n\t\tself.num_units = hparams.num_units\n\t\tself.num_encoder_layers = hparams.num_encoder_layers\n\t\tself.num_decoder_layers = hparams.num_decoder_layers\n\t\tself.num_encoder_residual_layers = self.num_encoder_layers - 1\n\t\tself.num_decoder_residual_layers = self.num_decoder_layers - 1\n\n\t\tself.batch_size = tf.size(self.seq_length_encoder_input_data)\n\n\t\t# set initializer\n\t\trandom_seed = hparams.random_seed\n\t\tinitializer = _mh.get_initializer(hparams.init_op, random_seed, hparams.init_weight)\n\t\ttf.get_variable_scope().set_initializer(initializer)\n\n\t\t# embeddings\n\t\tself.src_vocab_size = hparams.src_vocab_size\n\t\tself.tgt_vocab_size = hparams.tgt_vocab_size\n\t\tself.init_embeddings(hparams, scope)","def test_set_params():\n\n tpot_obj = TPOTClassifier()\n assert tpot_obj.set_params() is tpot_obj","def set_parameters(self, weight, bias=None):\n self.weight = torch.nn.Parameter(torch.as_tensor(weight))\n if bias is not None:\n self.bias = torch.nn.Parameter(torch.as_tensor(bias))","def set_parameters(self, weight, bias=None):\r\n self.weight = torch.nn.Parameter(torch.as_tensor(weight))\r\n if bias is not None:\r\n self.bias = torch.nn.Parameter(torch.as_tensor(bias))","def __init__(self, data, params):\n super(VBPR, self).__init__(data, params)\n self.embed_k = self.params.embed_k\n self.embed_d = self.params.embed_d\n self.learning_rate = self.params.lr\n\n self.directory_parameters = f'batch_{self.params.batch_size}' \\\n f'-D_{self.params.embed_d}' \\\n f'-K_{self.params.embed_k}' \\\n f'-lr_{self.params.lr}' \\\n f'-reg_{self.params.reg}'\n\n self.process_cnn_visual_features()\n\n # Initialize Model Parameters\n self.Bp = tf.Variable(\n self.initializer(shape=[self.dim_cnn_features, 1]), name='Bp', dtype=tf.float32)\n self.Tu = tf.Variable(\n self.initializer(shape=[self.num_users, self.embed_d]),\n name='Tu', dtype=tf.float32) # (users, low_embedding_size)\n self.F = tf.Variable(\n self.cnn_features,\n name='F', dtype=tf.float32, trainable=False)\n self.E = tf.Variable(\n self.initializer(shape=[self.dim_cnn_features, self.embed_d]),\n name='E', dtype=tf.float32) # (items, low_embedding_size)\n\n self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n self.saver_ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self)","def init_params(self, parameters):\r\n max_epoch = parameters['num_epoch']\r\n momentum_rate = parameters['momentum']\r\n loss = parameters['loss_function']\r\n accuracy = parameters['accuracy']\r\n regularization = parameters['regularization']\r\n batch_size = parameters['batch_size']\r\n optimizer = parameters['optimizer'] if parameters['optimizer'] is not None else 'batch'\r\n self.__init__(max_epoch, optimizer, loss, accuracy, momentum_rate, regularization, batch_size)","def params_init(self) -> None:\n # Initialize weights and biases with uniform distribution.\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\n for lyr in range(self.n_lyr):\n self.stack_rnn[2 * lyr].params_init()\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)","def copy_params(self):\n tf.get_default_session().run(self.copy_ops)","def set_parameters(self, f_ext=0):\n self.F_EXT = f_ext"],"string":"[\n \"def set_parameters(self, **kwargs):\\n self.__multi_layer_perceptron.set_params(**kwargs)\",\n \"def set_parameters(self,params):\\n K3Supervisor.set_parameters(self,params)\\n self.gtg.set_parameters(self.parameters)\\n self.avoidobstacles.set_parameters(self.parameters)\\n self.wall.set_parameters(self.parameters)\",\n \"def set_params(self, **parameters):\\n for parameter, value in parameters.items():\\n if parameter == 'predictor':\\n if isinstance(value, chainer.Link):\\n del self.predictor\\n with self.init_scope():\\n self.predictor = value\\n else:\\n assert False, 'predictor is not Chain instance'\\n elif parameter in ['lossfun', 'accfun', 'device']:\\n setattr(self, parameter, value)\\n else:\\n self.sk_params.update({parameter: value})\\n return self\",\n \"def set_params(self, params: Dict):\\n\\n if params['training_instances'] is not None:\\n self.training_instances = params['training_instances']\\n if params['n'] is not None:\\n self.n = params['n']\\n if params['lda'] is not None:\\n self.lda = params['lda']\\n if params['verbose'] is not None:\\n self.verbose = params['verbose']\\n\\n self.num_features = self.training_instances[0].get_feature_count()\\n self.w = None\\n self.b = None\",\n \"def _set_parameters(self, parameters):\\n self.parameters = parameters\\n self._set_points_and_weights()\",\n \"def setParameters(self):\\n\\n # Set the parameters\\n self.taux = 24.2\\n self.mu = 0.23\\n self.G = 33.75\\n self.alpha_0 = 0.05\\n self.delta = 0.0075\\n self.p = 0.50\\n self.I0 = 9500.0\\n self.kparam = 0.55\",\n \"def set_parameters(self,params):\\n K3Supervisor.set_parameters(self,params)\\n self.blending.set_parameters(self.parameters)\",\n \"def set_hyperparams(self, params):\",\n \"def set_parameters(self, new_param):\\n\\n current_idx = 0\\n for idx, param in enumerate(self.__network.parameters()):\\n temp_param = \\\\\\n new_param[current_idx:current_idx + self.__net_sizes[idx]]\\n temp_param = temp_param.reshape(self.__net_shapes[idx])\\n param.data = tr.from_numpy(temp_param).float()\\n current_idx += self.__net_sizes[idx]\",\n \"def set_params(self, params):\\n cpt = 0\\n for param in self.parameters():\\n tmp = np.product(param.size())\\n\\n if torch.cuda.is_available():\\n param.data.copy_(torch.from_numpy(\\n params[cpt:cpt + tmp]).view(param.size()).cuda())\\n else:\\n param.data.copy_(torch.from_numpy(\\n params[cpt:cpt + tmp]).view(param.size()))\\n cpt += tmp\",\n \"def set_params(self):\\n raise NotImplementedError\",\n \"def set_training_parameters(\\n self,\\n config: ConfigDict,\\n len_train: int,\\n len_test: int,\\n ):\\n self.configure_steps(config, len_train, len_test)\\n self.configure_reporting(config)\\n self.configure_training_functions(config)\",\n \"def define_parameters(self):\\n self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))\\n self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels))\",\n \"def define_parameters(self):\\n self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.in_channels, self.out_channels))\\n self.bias = torch.nn.Parameter(torch.Tensor(self.out_channels))\",\n \"def set_parameters(self, **kwargs):\\n self.__select_k_best.set_params(**kwargs)\",\n \"def params(self,new):\\n self._params = new\\n self._config_set()\\n self._make_model()\",\n \"def set_params(self, new_params: torch.Tensor) -> None:\\n assert new_params.size() == self.get_params().size()\\n progress = 0\\n for pp in list(self.net.parameters()):\\n cand_params = new_params[progress: progress +\\n torch.tensor(pp.size()).prod()].view(pp.size())\\n progress += torch.tensor(pp.size()).prod()\\n pp.data = cand_params\",\n \"def _set_controller_parameters(self, P=None, I=None, D=None):\\n pass\",\n \"def init_params(self):\\n self.conv = Conv(self.conv_layers[0][-1], self.out_channels, padding=self.padding,stride=self.stride)\\n self.W = torch.randn(self.num_labels, self.cout_numel, requires_grad=True)\\n self.T = torch.randn(self.num_labels, self.num_labels, requires_grad=True)\",\n \"def set_params(self):\\r\\n pass\",\n \"def set_parameters(self, par):\\n try:\\n for l in self.cell.layers:\\n r_curve = cmf.VanGenuchtenMualem(\\n Ksat=10**par.pKsat, phi=par.porosity, alpha=par.alpha, n=par.n\\n )\\n r_curve.w0 = r_curve.fit_w0()\\n l.soil = r_curve\\n self.cell.saturated_depth = 0.5\\n self.gw.potential = self.cell.z - 0.5\\n except RuntimeError as e:\\n sys.stderr.write(\\\"Set parameters failed with:\\\\n\\\" + str(par) + \\\"\\\\n\\\" + str(e))\\n raise\",\n \"def setup_parameters(self):\\n structure = self.ctx.structure_initial_primitive\\n ecutwfc = []\\n ecutrho = []\\n\\n for kind in structure.get_kind_names():\\n try:\\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\\n cutrho = dual * cutoff\\n ecutwfc.append(cutoff)\\n ecutrho.append(cutrho)\\n except KeyError as exception:\\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\\n\\n natoms = len(structure.sites)\\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\\n\\n self.ctx.inputs['parameters'] = {\\n 'CONTROL': {\\n 'restart_mode': 'from_scratch',\\n 'tstress': self.ctx.protocol['tstress'],\\n },\\n 'SYSTEM': {\\n 'ecutwfc': max(ecutwfc),\\n 'ecutrho': max(ecutrho),\\n 'smearing': self.ctx.protocol['smearing'],\\n 'degauss': self.ctx.protocol['degauss'],\\n 'occupations': self.ctx.protocol['occupations'],\\n },\\n 'ELECTRONS': {\\n 'conv_thr': conv_thr,\\n }\\n }\",\n \"def initialize_parameters():\\n\\n W1 = tf.get_variable('W1', [3,3,3,64], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W2 = tf.get_variable('W2', [3,3,64,128], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W3 = tf.get_variable('W3', [3,3,128,256], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W4 = tf.get_variable('W4', [3,3,256,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W5 = tf.get_variable('W5', [3,3,512,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n\\n ### END CODE HERE ###\\n\\n parameters = {\\\"W1\\\": W1,\\n \\\"W2\\\": W2,\\n \\\"W3\\\": W3,\\n \\\"W4\\\": W4,\\n \\\"W5\\\": W5\\n }\\n\\n return parameters\",\n \"def setParams(self, tfInputGraph=None, inputMapping=None, outputMapping=None, tfHParms=None):\\n super(TFTransformer, self).__init__()\\n kwargs = self._input_kwargs\\n # Further conanonicalization, e.g. converting dict to sorted str pairs happens here\\n return self._set(**kwargs)\",\n \"def __init__(self, graph=None, *args, **kwargs):\\n # Set TensorFlow graph. All TF code will work on this graph.\\n self.graph = graph or tf.Graph()\\n self.SetParams(*args, **kwargs)\",\n \"def __init__(self, tfInputGraph=None, inputMapping=None, outputMapping=None, tfHParms=None):\\n super(TFTransformer, self).__init__()\\n kwargs = self._input_kwargs\\n self.setParams(**kwargs)\",\n \"def set_parameters(self, We1,be1, We2, be2, We3, be3, Wmu, bmu, Wstd, bstd, Wd1, bd1, Wd2, bd2, Wd3, bd3):\\r\\n self.en_fc1.weight=nn.Parameter(We1)\\r\\n self.en_fc1.bias=nn.Parameter(be1)\\r\\n \\r\\n self.en_fc2.weight=nn.Parameter(We2)\\r\\n self.en_fc2.bias=nn.Parameter(be2)\\r\\n \\r\\n self.en_fc3.weight=nn.Parameter(We3)\\r\\n self.en_fc3.bias=nn.Parameter(be3)\\r\\n \\r\\n self.en_mu.weight=nn.Parameter(Wmu)\\r\\n self.en_mu.bias=nn.Parameter(bmu)\\r\\n \\r\\n self.en_log.weight=nn.Parameter(Wstd)\\r\\n self.en_log.bias=nn.Parameter(bstd)\\r\\n \\r\\n self.de_fc1.weight=nn.Parameter(Wd1)\\r\\n self.de_fc1.bias=nn.Parameter(bd1)\\r\\n \\r\\n self.de_fc2.weight=nn.Parameter(Wd2)\\r\\n self.de_fc2.bias=nn.Parameter(bd2)\\r\\n \\r\\n self.de_fc3.weight=nn.Parameter(Wd3)\\r\\n self.de_fc3.bias=nn.Parameter(bd3)\\r\\n \\r\\n return\",\n \"def _set_params(self,x):\\r\\n self.k._set_params(x)\",\n \"def set_params(self, **params):\\n if('threshold' in params.keys()):\\n self.threshold = params['threshold']\\n if('subsample' in params.keys()):\\n self.subsample = params['subsample']\\n if('estimator' in params.keys()):\\n self.estimator = params['estimator']\\n if('n_folds' in params.keys()):\\n self.n_folds = params['n_folds']\\n if('stratify' in params.keys()):\\n self.stratify = params['stratify']\\n if('random_state' in params.keys()):\\n self.random_state = params['random_state']\\n if('n_jobs' in params.keys()):\\n self.n_jobs = params['n_jobs']\",\n \"def set_shape_params(self, params):\\n self.alpha = params[0]\\n self.beta = params[1]\\n self.gamma = params[2]\\n self.c500 = params[3]\\n self.P0 = params[4]\",\n \"def set_params(self, **kwargs):\\n\\n # We don't want non-functional arguments polluting kwargs\\n params = kwargs.copy()\\n for k in ['function', 'target']:\\n params.pop(k, None)\\n\\n self.kwargs.update(params)\\n BaseEstimator.set_params(self, **kwargs)\",\n \"def _set_model_parameters(self, verbose=False):\\n from scipy.special import gamma\\n\\n z0 = self.z0\\n\\n # set parameters that are constants\\n p_v, d_v, cs0, sigma, vout0 = (1, 2, 6.7, 0.1, 25.0)\\n p_vB, d_vB, Mach0, p_M, d_M = (4, 2, 0.5, 1, 3)\\n\\n # calculate amplitudes that make the pdf integrate to 1\\n A_v = np.log(10)*p_v/gamma(d_v/p_v)\\n A_cs = np.log(10)/np.sqrt(2*np.pi)/sigma\\n A_vB = np.log(10)*p_vB/gamma(d_vB/p_vB)\\n A_M = np.log(10)*p_M/gamma(d_M/p_M)\\n\\n # store them in dictionaries\\n self.cool_params = dict(A_v=A_v, p_v=p_v, d_v=d_v,\\n A_cs=A_cs, cs0=cs0, sigma=sigma, vout0=vout0)\\n self.hot_params = dict(A_vB=A_vB, p_vB=p_vB, d_vB=d_vB,\\n A_M=A_M, Mach0=Mach0,p_M=p_M,d_M=d_M)\\n # SN related parameters that set the reference values for loading factors\\n self.params = dict(Esn=1.e51*au.erg, mstar=95.5*au.M_sun, vcool=200*au.km/au.s,\\n Mej=10.*au.M_sun, ZSN=0.2, ZISM0=0.02)\\n self.params['vej'] = np.sqrt(2.0*self.params['Esn']/self.params['Mej']).to('km/s')\\n self.ref_params = dict(Mref=self.params['mstar'],\\n pref=self.params['Esn']/(2*self.params['vcool']),\\n Eref=self.params['Esn'],\\n Zref=self.params['Mej']*self.params['ZSN'])\\n\\n # coefficients used in conversion from mass to other PDFs\\n self.vp = (self.ref_params['pref']/self.params['mstar']).to('km/s').value\\n self.vE = np.sqrt(self.ref_params['Eref']/self.params['mstar']).to('km/s').value\\n self.Ze = (self.ref_params['Zref']/self.params['mstar']).cgs.value\\n\\n # parameters for scaling relations from Paper~I\\n a = np.array(fit_alpha[z0])\\n b = np.array(fit_beta[z0])\\n\\n self.scaling_params = dict(a=a, b=b)\\n if z0 == '2H':\\n self.cool_params['vout0'] = 45\\n self.cool_params['cs0'] = 7.5\\n elif z0 == '500':\\n self.cool_params['vout0'] = 45\\n self.cool_params['cs0'] = 8.5\\n elif z0 == '1000':\\n self.cool_params['vout0'] = 60\\n self.cool_params['cs0'] = 10.0\\n self.scaling_params['A'] = np.round(10.**(np.array(self.scaling_params['a'])),2)\\n self.scaling_params['p'] = 1.+np.array(self.scaling_params['b'])\\n self.enum=dict(M_cool=0, M_int=1, M_hot=2, M_total=3,\\n p_cool=4, p_int=5, p_hot=6, p_total=7,\\n E_cool=8, E_int=9, E_hot=10, E_total=11,\\n Z_cool=12, Z_int=13, Z_hot=14, Z_total=15)\\n\\n # print parameters\\n if verbose:\\n self.show_parameters()\",\n \"def set_params(self, params):\",\n \"def _initialize_parameters(self):\\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k * 2],\\n initializer=self.initializer)\\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k * 2],\\n initializer=self.initializer)\",\n \"def _set_training_params(self, params):\\n self.lyapunov_hybrid_system.lyapunov_relu.load_state_dict(\\n params[\\\"lyap_relu_params\\\"])\\n if not self.R_options.fixed_R:\\n self.R_options._variables = params[\\\"R_params\\\"].clone()\\n if isinstance(self.lyapunov_hybrid_system.system,\\n feedback_system.FeedbackSystem):\\n self.lyapunov_hybrid_system.system.controller_network.\\\\\\n load_state_dict(params[\\\"controller_params\\\"])\",\n \"def set_params(self, **params):\\n return self.forest.set_params(**params)\",\n \"def set_parameters(self, params):\\n self.kp = params.pgain\",\n \"def set_params(self, **kwargs):\\n ...\",\n \"def _initialize_parameters(self):\\n self.ent_emb = tf.get_variable('ent_emb', shape=[len(self.ent_to_idx), self.k],\\n initializer=self.initializer)\\n self.rel_emb = tf.get_variable('rel_emb', shape=[len(self.rel_to_idx), self.k],\\n initializer=self.initializer)\",\n \"def set_tf_params(model, write_graph=False):\\n\\n with tf.gfile.FastGFile(os.path.join(\\n MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\\n\\n if write_graph:\\n # Write graph to file so that it can be visualized using TensorBoard\\n summary_writer = tf.summary.FileWriter('data', graph=graph_def)\\n\\n config = tf.ConfigProto()\\n config.gpu_options.allow_growth = True # Do not allocale the whole GPU mem\\n with tf.Session(config=config) as sess:\\n copy_inception(sess, model)\",\n \"def set_params(self):\\n \\n lo, hi = self.R.get((self.h, self.w, self.m), (0.0, 0.0))\\n params.update({\\n 'gamma' : 1.0, # minesweeper is a finite horizon game\\n 'epsilon': 0.0,\\n 'K': 16,\\n 'R_lo': lo,\\n 'R_hi': hi,\\n 'max_depth': self.h * self.w / 2,\\n 'c':hi-lo\\n })\",\n \"def set_params(self, params):\\n params = dict_to_namespace(params)\\n\\n # Set self.params\\n self.params = Namespace()\\n self.params.ndimx = params.ndimx\\n self.params.model_str = getattr(params, 'model_str', 'optfixedsig')\\n self.params.ig1 = getattr(params, 'ig1', 4.0)\\n self.params.ig2 = getattr(params, 'ig2', 3.0)\\n self.params.n1 = getattr(params, 'n1', 1.0)\\n self.params.n2 = getattr(params, 'n2', 1.0)\\n self.params.sigma = getattr(params, 'sigma', 1e-5)\\n self.params.niter = getattr(params, 'niter', 70)\\n self.params.kernel = getattr(params, 'kernel', kern_matern)\\n self.params.trans_x = getattr(params, 'trans_x', False)\",\n \"def set_parameters(targeted_flag='true',\\r\\n tv_flag='false',\\r\\n hinge_flag='true',\\r\\n cos_flag='false',\\r\\n interpolation='bilinear',\\r\\n model_type='small',\\r\\n loss_type='center',\\r\\n dataset_type='vgg',\\r\\n attack='CW',\\r\\n norm='2',\\r\\n epsilon=0.1,\\r\\n iterations=100,\\r\\n binary_steps=8,\\r\\n learning_rate=0.01,\\r\\n epsilon_steps=0.01,\\r\\n init_const=0.3,\\r\\n mean_loss='embeddingmean',\\r\\n batch_size=-1,\\r\\n margin=5.0,\\r\\n amplification=2.0):\\r\\n params = {}\\r\\n\\r\\n params['model_type'] = model_type\\r\\n params['loss_type'] = loss_type\\r\\n params['dataset_type'] = dataset_type\\r\\n params['attack'] = attack\\r\\n params['norm'] = norm\\r\\n params['epsilon'] = epsilon\\r\\n params['iterations'] = iterations\\r\\n params['binary_steps'] = binary_steps\\r\\n params['learning_rate'] = learning_rate\\r\\n params['epsilon_steps'] = epsilon_steps\\r\\n params['init_const'] = init_const\\r\\n params['mean_loss'] = mean_loss\\r\\n params['batch_size'] = batch_size\\r\\n params['targeted_flag'] = string_to_bool(targeted_flag)\\r\\n params['tv_flag'] = string_to_bool(tv_flag)\\r\\n params['hinge_flag'] = string_to_bool(hinge_flag)\\r\\n params['cos_flag'] = string_to_bool(cos_flag)\\r\\n params['margin'] = margin\\r\\n params['amp'] = amplification\\r\\n\\r\\n if model_type == 'small' and loss_type == 'center':\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = -1.0\\r\\n else:\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = 0.0\\r\\n\\r\\n if (dataset_type == 'vggsmall'):\\r\\n params['align_dir'] = VGG_ALIGN_160_DIR\\r\\n params['test_dir'] = VGG_TEST_DIR\\r\\n elif model_type == 'large' or dataset_type == 'casia':\\r\\n params['align_dir'] = ALIGN_160_DIR\\r\\n elif model_type == 'small':\\r\\n params['align_dir'] = ALIGN_96_DIR\\r\\n else:\\r\\n ValueError('ValueError: Argument must be either \\\"small\\\" or \\\"large\\\".')\\r\\n \\r\\n if interpolation == 'nearest':\\r\\n params['interpolation'] = cv2.INTER_NEAREST\\r\\n elif interpolation == 'bilinear':\\r\\n params['interpolation'] = cv2.INTER_LINEAR\\r\\n elif interpolation == 'bicubic':\\r\\n params['interpolation'] = cv2.INTER_CUBIC\\r\\n elif interpolation == 'lanczos':\\r\\n params['interpolation'] = cv2.INTER_LANCZOS4\\r\\n elif interpolation == 'super':\\r\\n print('finish later')\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\\r\\n\\r\\n if params['hinge_flag']:\\r\\n params['attack_loss'] = 'hinge'\\r\\n else:\\r\\n params['attack_loss'] = 'target'\\r\\n if not params['targeted_flag']:\\r\\n params['attack_loss'] = 'target'\\r\\n if norm == 'inf':\\r\\n norm_name = 'i'\\r\\n else:\\r\\n norm_name = '2'\\r\\n if params['tv_flag']:\\r\\n tv_name = '_tv'\\r\\n else:\\r\\n tv_name = ''\\r\\n if params['cos_flag']:\\r\\n cos_name = '_cos'\\r\\n else:\\r\\n cos_name = ''\\r\\n\\r\\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\\r\\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\\r\\n params['model_name'] = dataset_type\\r\\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\\r\\n\\r\\n return params\",\n \"def setParameters(self, params):\\n self.module._setParameters(params)\\n # update parameters for learner\\n self.learner.setModule(self.module)\",\n \"def set_parameters(self, create_models=True, **parameters):\\n flag_nn_opti = False\\n\\n # Set attributes\\n for param, value in parameters.items():\\n if param in self.DEFAULT_VALUES.keys():\\n if getattr(self, param) != value:\\n # We change param value\\n setattr(self, param, value)\\n if param in ['hidden_layers', 'lr']:\\n flag_nn_opti = True\\n\\n else:\\n raise Exception(f'Parameter {param} not known.')\\n\\n # Create torch instances\\n if create_models and flag_nn_opti:\\n self._create_networks_and_optimizer()\",\n \"def define_parameters(act_func, dropout, fc_layer_units, labels, samples):\\n\\n\\n\\n # Define a training and test set\\n test_size = 0.1 # training is set on 90%\\n training_vec, test_vec, training_labels, test_labels = train_test_split(samples, labels, test_size=test_size)\\n\\n # Get the batch size\\n batch_percentage = 0.1 # There is chosen to use a batch size of 10%\\n batch_size = int(training_vec.shape[0] * batch_percentage)\\n\\n # Get the number of features\\n feature_number = training_vec.shape[1]\\n\\n # Get the number of classes\\n class_number = len(np.unique(labels))\\n\\n # Get the layer nodes in correct format\\n int_layer_units = []\\n units = fc_layer_units.split(',')\\n for unit in units:\\n int_layer_units.append(int(unit))\\n\\n # Get the dropout layers in correct format\\n dropout_booleans = []\\n dropouts = dropout.split(',')\\n for layer in dropouts:\\n layer = layer.lower()\\n if layer == 'f' or layer == 'false':\\n dropout_booleans.append(False)\\n else:\\n dropout_booleans.append(True)\\n\\n # Get the layer names of the neural network architecture\\n layers = []\\n for index, nodes in enumerate(int_layer_units):\\n layers.append('fc ({})'.format(nodes))\\n if dropout_booleans[index]:\\n layers.append('do')\\n layers = ' - '.join(layers)\\n\\n # Get the right activation function\\n act_func = act_func.lower()\\n if act_func == 'sigmoid' or act_func == 'sig' or act_func == 's':\\n act_func = tf.nn.sigmoid\\n act_title = 'sigmoid'\\n elif act_func == 'relu' or act_func == 'r':\\n act_func = tf.nn.relu\\n act_title = 'ReLU'\\n elif act_func == 'tanh' or act_func == 'tan' or act_func == 't':\\n act_func = tf.tanh\\n act_title = 'tanH'\\n else:\\n act_func = None\\n act_title = 'none'\\n\\n return act_func, act_title, batch_size, class_number, feature_number, layers, dropout_booleans, int_layer_units, \\\\\\n test_labels, test_vec, training_labels, training_vec\",\n \"def set_tunable_params(self, params):\\n self._tunable_params = params\",\n \"def set_parameters(self):\\n\\n if self.model_with_set_params:\\n return\\n\\n self._model_with_set_params = self._parameter_values.process_model(\\n self._unprocessed_model, inplace=False\\n )\\n self._parameter_values.process_geometry(self.geometry)\\n self.model = self._model_with_set_params\",\n \"def set_params(self, **params):\\n self.check_params(params)\\n self.sk_params.update(params)\\n return self\",\n \"def __call__(self, params):\\n logging.info('Running __call__ function...')\\n batch_size = self._train_batch_size\\n # For MCTS, the number of features for each trajecotry is unknown beforehand\\n num_features = None\\n\\n if self._global_step_value % self._iterations_per_loop == 0:\\n logging.info('Update iterator (gs=%d)...', self._global_step_value)\\n # Feature/Labels Placeholders\\n self.features_ph = {\\n 'mcts_features':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_state_space],\\n name='mcts_state_ph'),\\n 'policy_features':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_state_space],\\n name='policy_state_ph'),\\n }\\n self.labels_ph = {\\n 'action_tensor':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_action_space],\\n name='action_ph'),\\n 'value_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='value_ph'),\\n 'return_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='return_ph'),\\n 'old_neg_logprob_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='old_neg'),\\n 'mean_tensor':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_action_space],\\n name='mean_ph'),\\n 'logstd_tensor':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_action_space],\\n name='logstd_ph'),\\n 'mcts_enable_tensor':\\n tf.placeholder(\\n tf.bool, shape=[num_features], name='mcts_enable_ph'),\\n 'policy_action_tensor':\\n tf.placeholder(\\n tf.float32,\\n shape=[num_features, self._env_action_space],\\n name='policy_action_ph'),\\n 'policy_value_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='policy_value_ph'),\\n 'policy_return_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='policy_return_ph'),\\n 'policy_old_neg_logprob_tensor':\\n tf.placeholder(\\n tf.float32, shape=[num_features], name='policy_old_neg'),\\n }\\n # Create the dataset\\n dataset = tf.data.Dataset.from_tensor_slices(\\n (self.features_ph, self.labels_ph))\\n dataset = dataset.shuffle(buffer_size=self._max_horizon)\\n dataset = dataset.batch(batch_size, drop_remainder=True)\\n\\n # repeat until the loop is done\\n dataset = dataset.repeat()\\n if self._use_tpu:\\n dataset = dataset.map(functools.partial(self._set_shapes, batch_size))\\n dataset = dataset.prefetch(2)\\n self._iterator = dataset.make_initializable_iterator()\\n return self._iterator.get_next()\\n else:\\n return self._iterator.get_next()\",\n \"def update(self, parameters):\\n self.set_frequencies(parameters) # f_i\\n self.set_coupling_weights(parameters) # w_ij\\n self.set_phase_bias(parameters) # theta_i\\n self.set_amplitudes_rate(parameters) # a_i\\n self.set_nominal_amplitudes(parameters) # R_i\",\n \"def update_parameters(self):\\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\\n self.update_gamma()\\n self.update_gamma0()\\n self.update_lambda()\\n self.update_nu()\\n if self.sample_xi:\\n self.update_xi()\",\n \"def set_params(self, **kwargs):\\n\\n kw_keys = list(kwargs)\\n\\n if 'alpha' in kw_keys:\\n self.alpha = kwargs['alpha']\\n\\n if 'beta' in kw_keys:\\n self.beta = kwargs['beta']\\n\\n if 'gamma' in kw_keys: \\n \\tself.gamma = kwargs['gamma']\\n\\n if 'epsilon' in kw_keys:\\n self.epsilon = kwargs['epsilon']\\n \\n self.nact = self.highbound-self.lowbound\\n self.actions = np.arange(self.nact)\",\n \"def set_profile_params(self, params):\\n self.alpha = params[0]\\n self.beta = params[1]\\n self.gamma = params[2]\\n self.c500 = params[3]\\n self.P0 = params[4]\\n self.M500 = params[5]\\n self.r500 = params[6]\\n self.z = params[7]\",\n \"def initializeParameters(self):\\n\\n self.params[2].value = False\\n self.params[3].enabled = False\\n self.params[7].value = True\\n self.params[7].enabled = False\\n self.params[8].value = None\\n self.params[8].enabled = False\",\n \"def initialise_parameters(self):\\n # Weights\\n init = select_w_init(self.w_init)\\n if self.w_gain:\\n gain = nn.init.calculate_gain('relu')\\n init(self.relations, gain=gain)\\n else:\\n init(self.relations)\\n\\n # Biases\\n if self.b_init:\\n init = select_b_init(self.b_init)\\n init(self.sbias)\\n init(self.pbias)\\n init(self.obias)\",\n \"def reset_parameters(self):\\n init_method = getattr(init, self.initialization)\\n for layer in range(self.num_layers):\\n fc = self.get_fc(layer)\\n init_method(fc.weight.data)\\n if self.use_bias:\\n init.constant(fc.bias.data, val=0)\\n init_method(self.out.weight.data)\\n init.constant(self.out.bias.data, val=0)\",\n \"def __init__(self, **kwargs):\\n super().__init__()\\n self.model_conv = models.resnet50(pretrained=True)\\n for param in self.model_conv.parameters():\\n param.requires_grad = False\\n num_ftrs = self.model_conv.fc.in_features\\n num_classes = 10\\n self.model_conv.fc = nn.Linear(num_ftrs, num_classes)\",\n \"def set_params(model, params): # type: ignore\\n for p, p_new in zip(model.parameters(), params):\\n p.data = p_new.data\",\n \"def init_params(self):\\n self.params = Parameters()\\n self.params.add('qoff', self.qoff, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('yscale', self.yscale, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('int_bg', self.int_bg, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('Rc', self.Rc, vary=0, min=-np.inf, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('sur_den', self.sur_den, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\\n self.params.add('ion_depth', self.ion_depth, vary=0, min=0, max=np.inf, expr=None, brute_step=0.1)\",\n \"def set_tunable_parameters(self, n_latent_features=1024, reduced_size=64, input_type='default', **kwargs):\\n\\n self.logger.info(\\\"Set parameters: n_latent_features=%s, reduced_size=%s, input_type=%s\\\", n_latent_features,\\n reduced_size, input_type)\\n\\n self.n_latent_features = n_latent_features\\n self.reduced_size = reduced_size\\n self.input_type = input_type\\n\\n self._construct_model()\",\n \"def transfer_parameters_call_optimization(self, mainCl, mainPt, consCall=None, consPt=None):\",\n \"def set_parameter(self, output):\\n self.model.set_parameter(output);\",\n \"def define_parameters(self):\",\n \"def init_parameters(obj, hyperparameters):\\n # Initialize Global Configuration Parameter\\n params = hyperparameters['global']\\n setattr(obj, 'param', params)\\n\\n # Initialize Attributes (Pre-Checked Parameters)\\n setattr(obj, 'learning_rate', params['learning_rate'])\\n setattr(obj, 'loss', params['loss'])\\n setattr(obj, 'max_iter', params['max_iter'])\\n\\n if params['loss'] == 'least_squares':\\n setattr(obj, 'num_classes', 1)\\n elif params['loss'] in ['binary_crossentropy', 'categorical_crossentropy', 'auto']:\\n setattr(obj, 'num_classes', params['num_classes'])\\n\\n # Initialize Attributes (Optional Values - Based on Default Parameters)\\n if 'l2_regularization' not in params or params['l2_regularization'] is None:\\n setattr(obj, 'l2_regularization', 0)\\n else:\\n setattr(obj, 'l2_regularization', params['l2_regularization'])\\n\\n if 'max_bins' not in params:\\n setattr(obj, 'max_bins', 255)\\n else:\\n setattr(obj, 'max_bins', params['max_bins'])\\n\\n if 'max_depth' not in params or params['max_depth'] is None:\\n setattr(obj, 'max_depth', None)\\n else:\\n setattr(obj, 'max_depth', params['max_depth'])\\n\\n if 'max_leaf_nodes' not in params or params['max_leaf_nodes'] is None:\\n setattr(obj, 'max_leaf_nodes', 31)\\n else:\\n setattr(obj, 'max_leaf_nodes', params['max_leaf_nodes'])\\n\\n if 'min_samples_leaf' not in params or params['min_samples_leaf'] is None:\\n setattr(obj, 'min_samples_leaf', 20)\\n else:\\n setattr(obj, 'min_samples_leaf', params['min_samples_leaf'])\\n\\n if 'random_state' in params:\\n setattr(obj, 'random_state', params['random_state'])\\n else:\\n setattr(obj, 'random_state', None)\\n\\n if 'scoring' in params:\\n setattr(obj, 'scoring', params['scoring'])\\n else:\\n setattr(obj, 'scoring', None)\\n\\n if 'verbose' not in params or params['verbose'] is None:\\n setattr(obj, 'verbose', False)\\n else:\\n setattr(obj, 'verbose', True)\\n\\n return obj\",\n \"def test_set_params_2():\\n tpot_obj = TPOTClassifier(generations=2)\\n tpot_obj.set_params(generations=3)\\n\\n assert tpot_obj.generations == 3\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def set_fit_params(self):\\n\\n self.p0 = np.array([self.A_arr, self.T_a])\\n # initial guess at A_arr and T_a\\n\\n self.popt, self.pcov = curve_fit(\\n self.get_eta_fit, self.T_exp, self.eta_exp, p0=self.p0\\n )\\n\\n self.A_arr = self.popt[0]\\n self.T_a = self.popt[1]\\n\\n self.T_array = self.T_model\",\n \"def Params(cls):\\n p = super().Params()\\n p.Define('train_task', None, 'Underlying task')\\n p.Define('decode_task', None, 'Underlying task')\\n p.Define('train_dataset_name', None, '')\\n p.Define('decode_dataset_name', None, '')\\n p.Define('train_steps_per_loop', 0, '')\\n p.Define('decode_steps_per_loop', 0, '')\\n return p\",\n \"def _setup_misc(self, mode):\\n self.lr_rate_ph = tf.Variable(0.0, name='lrn_rate', trainable=False)\\n self.reuse = None if (mode == 'train') else True\\n self.batch_size = self.hparams.batch_size\\n if mode == 'eval':\\n self.batch_size = 25\",\n \"def set_params(self, **params):\\n if not hasattr(self, \\\"_non_sklearn_base\\\"):\\n return super().set_params(**params)\\n if not (\\n len(params) == 1 and\\n (\\\"nthreads\\\" in params or \\\"n_jobs\\\" in params)\\n ):\\n self.is_fitted_ = False\\n valid_params = self.get_params(deep=False)\\n for k,v in params.items():\\n if k not in valid_params:\\n raise ValueError(\\\"Invalid parameter: \\\", k)\\n setattr(self, k, v)\\n return self\",\n \"def __init__(self, encut, ldaul, Uparam, Jparam, name=\\\"DFTU_settings\\\"):\\n\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\" , \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LADAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIX\\\": 4}\\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\\\"DFTCL_settings\\\"):\\n\\n cl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": magmom, \\\"SAXIS\\\": None, \\\"LSORBIT\\\": None, \\\"LNONCOLLINEAR\\\": None}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIMX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"encut\\\", encut)\",\n \"def set_default_params(self, opt):\\n self.config.embed_dim = opt.embed_dim or 200\\n self.config.rnn_size = opt.rnn_size or 512\\n self.config.nrnn_layer = opt.nrnn_layer or 2\\n self.config.rnn_dropout = opt.rnn_dropout or 0.5\\n self.config.rnnout_dim = 2 * self.config.rnn_size * self.config.nrnn_layer\\n ## MULTIMODAL (ATTENTION)\\n self.config.cnnout_dim = opt.cnnout_dim or 512\\n self.config.cnnout_w = opt.cnnout_w or 14\\n self.config.cnnout_h = opt.cnnout_h or 14\\n self.config.cnnout_spat = self.config.cnnout_w * self.config.cnnout_h\\n self.config.multfeat_dim = opt.multfeat_dim or 512\\n self.config.attfeat_dim = opt.attfeat_dim or 256\\n self.config.netout_dim = opt.answer_size\\n ## [attlstm] in: {2*multfeat_dim, att_rnn_s_dim} {att_rnn_size, att_rnn_s_dim}\\n self.config.att_rnn_size = opt.att_rnn_size or 512\\n self.config.att_rnn_nlayer = opt.att_rnn_nlayer or 1\\n self.config.att_rnn_dropout = opt.att_rnn_dropout or 0.0\\n # TODO: There could be a protential bugs if self.config.att_rnn_nlayer > 1\\n assert(self.config.att_rnn_nlayer == 1)\\n self.config.att_rnn_s_dim = self.config.att_rnn_size * self.config.att_rnn_nlayer\\n\\n # optimization\\n self.config.max_grad_norm = opt.max_grad_norm or 0.1\\n self.config.initializer_scale = 0.008\",\n \"def __init__(self, train_x, train_y, test_x, test_y, Tunning_Cs=[0.001, 0.01, 0.1, 1, 10]): \\n self.Cs = Tunning_Cs\\n self.train_x = train_x\\n self.train_y = train_y\\n self.test_x = test_x \\n self.test_y = test_y\\n self.model = svm.SVR(kernel='rbf', gamma='auto')\",\n \"def __init__(self, encut, name=\\\"scf_settings\\\"):\\n InputParameters.__init__(self, name=name)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def tuned_for_ec():\\n # TODO(theosanderson): update these to true SOTA values\\n hparams = contrib_training.HParams()\\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\\n hparams.add_hparam('batch_style', 'bucket')\\n hparams.add_hparam('batch_size', 34)\\n hparams.add_hparam('dilation_rate', 5)\\n hparams.add_hparam('filters', 411)\\n hparams.add_hparam('first_dilated_layer', 1) # This is 0-indexed\\n hparams.add_hparam('kernel_size', 7)\\n hparams.add_hparam('num_layers', 5)\\n hparams.add_hparam('pooling', 'mean')\\n hparams.add_hparam('resnet_bottleneck_factor', 0.88152)\\n hparams.add_hparam('lr_decay_rate', 0.9977)\\n hparams.add_hparam('learning_rate', 0.00028748)\\n hparams.add_hparam('decision_threshold', 0.3746)\\n hparams.add_hparam('denominator_power', 0.88)\\n\\n hparams.add_hparam('train_steps', 650000)\\n return hparams\",\n \"def set_fe_params(self, fe_params):\\n self._params[0:self.k_fe] = fe_params\",\n \"def set_params(self, params_):\\n x_start, x_end = params_[\\\"lim_fit\\\"]\\n self.find_idx_of_fit_limit(x_start, x_end)\\n self.is_error_bar_for_fit = params_[\\\"use_error_bar\\\"]\\n self.fitting_method1 = params_[\\\"method1\\\"]\\n self.fitting_method2 = params_[\\\"method2\\\"]\\n self.qty_to_min = params_[\\\"qty_to_min\\\"]\\n\\n for i, key in enumerate(self.params):\\n # self.params[key].set(value=params_[\\\"val\\\"][i], min=params_[\\\"min\\\"][i], max=params_[\\\"max\\\"][i], vary=bool(params_[\\\"hold\\\"][i]), brute_step=params_[\\\"brute_step\\\"][i])\\n if self.params[key].user_data is not None:\\n if \\\"dontGenerate\\\" in self.params[key].user_data:\\n continue\\n self.params[key].set(value=params_[key][\\\"value\\\"], min=params_[key][\\\"min\\\"], max=params_[key][\\\"max\\\"], vary=params_[key][\\\"vary\\\"], brute_step=params_[key][\\\"b_step\\\"])\",\n \"def _set_params(self, estimator_args, scaler_args, execution_args, metric_args=None, dim_reduction_args=None):\\n \\n # Set default values which will be used if execution arguments are not passed\\n \\n # Default parameters:\\n self.model.overwrite = True\\n self.model.debug = False\\n self.model.test_size = 0.33\\n self.model.cv = 0\\n self.model.time_series_split = 0\\n self.model.max_train_size = None\\n self.model.random_state = 42\\n self.model.compress = 3\\n self.model.retain_data = False\\n self.model.scale_hashed = True\\n self.model.scale_vectors = True\\n self.model.scaler = \\\"StandardScaler\\\"\\n self.model.scaler_kwargs = {}\\n self.model.estimator_kwargs = {}\\n self.model.missing = \\\"zeros\\\"\\n self.model.calc_feature_importances = False\\n self.model.importances_n_repeats = 30\\n self.model.lags= None\\n self.model.lag_target = False\\n self.model.scale_target = False\\n self.model.scale_lag_target= True\\n self.model.make_stationary = None\\n self.model.stationarity_lags = [1]\\n self.model.using_keras = False\\n self.model.current_sample_as_input = True\\n self.model.prediction_periods = 1\\n \\n # Default metric parameters:\\n if metric_args is None:\\n self.model.metric_args = {}\\n \\n # Set execution parameters\\n \\n # If the execution key word arguments were included in the request, get the parameters and values\\n if len(execution_args) > 0:\\n \\n # Transform the string of arguments into a dictionary\\n execution_args = utils.get_kwargs(execution_args)\\n \\n # Set the overwite parameter if any existing model with the specified name should be overwritten\\n if 'overwrite' in execution_args:\\n self.model.overwrite = 'true' == execution_args['overwrite'].lower()\\n \\n # Set the test_size parameter that will be used to split the samples into training and testing data sets\\n # Default value is 0.33, i.e. we use 66% of the samples for training and 33% for testing\\n if 'test_size' in execution_args:\\n self.model.test_size = utils.atof(execution_args['test_size'])\\n\\n # Enable K-fold cross validation. For more information see: http://scikit-learn.org/stable/modules/cross_validation.html#multimetric-cross-validation\\n # Default value is 0 in which case a simple holdout strategy based on the test_size parameter is used.\\n # If cv > 0 then the model is validated used K = cv folds and the test_size parameter is ignored.\\n if 'cv' in execution_args:\\n self.model.cv = utils.atoi(execution_args['cv'])\\n \\n # Enable timeseries backtesting using TimeSeriesSplit. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html\\n # This will select the a validation strategy appropriate for time series and sequential data.\\n # The feature definitions must include an 'identifier' field which can be used to sort the series into the correct order.\\n # The integer supplied in this parameter will split the data into the given number of subsets for training and testing.\\n if 'time_series_split' in execution_args:\\n self.model.time_series_split = utils.atoi(execution_args['time_series_split'])\\n\\n # This parameter can be used together with time_series_split.\\n # It specifies the maximum samples to be used for training in each split, which allows for rolling/ walk forward validation.\\n if 'max_train_size' in execution_args:\\n self.model.max_train_size = utils.atoi(execution_args['max_train_size'])\\n\\n # Add lag observations to the feature matrix. Only applicable for Keras models.\\n # An identifier field must be included in the feature definitions to correctly sort the data for this capability.\\n # For e.g. if lags=2, features from the previous two samples will be concatenated as input features for the current sample.\\n # This is useful for framing timeseries and sequence prediction problems into 3D or 4D data required for deep learning.\\n if 'lags' in execution_args:\\n self.model.lags = utils.atoi(execution_args['lags'])\\n\\n # Include targets in the lag observations\\n # If True an additional feature will be created for each sample using the previous value of y \\n if 'lag_target' in execution_args:\\n self.model.lag_target = 'true' == execution_args['lag_target'].lower()\\n \\n # Scale the target before fitting\\n # The scaling will be inversed before predictions so they are returned in the original scale \\n if 'scale_target' in execution_args:\\n self.model.scale_target = 'true' == execution_args['scale_target'].lower()\\n\\n # Scale lag values of the targets before fitting\\n # Even if scale_target is set to false, the lag values of targets being used as features can be scaled by setting this to true \\n if 'scale_lag_target' in execution_args:\\n self.model.scale_lag_target = 'true' == execution_args['scale_lag_target'].lower()\\n\\n # Make the target series more stationary. This only applies to sequence prediction problems.\\n # Valid values are 'log' in which case we apply a logarithm to the target values,\\n # or 'difference' in which case we transform the targets into variance from the previous value.\\n # The transformation will be reversed before returning predictions.\\n if 'make_stationary' in execution_args:\\n self.model.make_stationary = execution_args['make_stationary'].lower()\\n\\n # Provide lags periods for differencing\\n # By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list of lags as a list.\\n # e.g. 'stationarity_lags=1;12|list|int'\\n if 'stationarity_lags' in execution_args:\\n self.model.stationarity_lags = utils.get_kwargs_by_type({'stationarity_lags': execution_args['stationarity_lags']})['stationarity_lags']\\n\\n # Specify if the current sample should be used as input to the model\\n # This is to allow for models that only use lag observations to make future predictions\\n if 'current_sample_as_input' in execution_args:\\n self.model.current_sample_as_input = 'true' == execution_args['current_sample_as_input'].lower()\\n\\n # Specify the number of predictions expected from the model\\n # This can be used to get a model to predict the next m periods given inputs for the previous n periods.\\n # This is only valid for Keras models which have a final output layer with more than one node\\n if 'prediction_periods' in execution_args:\\n self.model.prediction_periods = utils.atoi(execution_args['prediction_periods'])\\n \\n # Seed used by the random number generator when generating the training testing split\\n if 'random_state' in execution_args:\\n self.model.random_state = utils.atoi(execution_args['random_state'])\\n \\n # Compression level between 1-9 used by joblib when saving the model\\n if 'compress' in execution_args:\\n self.model.compress = utils.atoi(execution_args['compress'])\\n \\n # Flag to determine if the training and test data should be saved in the model\\n if 'retain_data' in execution_args:\\n self.model.retain_data = 'true' == execution_args['retain_data'].lower()\\n\\n # Flag to determine if feature importances should be calculated when the fit method is called\\n if 'calculate_importances' in execution_args:\\n self.model.calc_feature_importances = 'true' == execution_args['calculate_importances'].lower()\\n\\n # Sets the number of times a feature is randomly shuffled during the feature importance calculation\\n if 'importances_n_repeats' in execution_args:\\n self.model.importances_n_repeats = utils.atoi(execution_args['importances_n_repeats'])\\n \\n # Set the debug option for generating execution logs\\n # Valid values are: true, false\\n if 'debug' in execution_args:\\n self.model.debug = 'true' == execution_args['debug'].lower()\\n \\n # Additional information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n # Increment log counter for the class. Each instance of the class generates a new log.\\n self.__class__.log_no += 1\\n\\n # Create a log file for the instance\\n # Logs will be stored in ..\\\\logs\\\\SKLearn Log <n>.txt\\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\\n \\n # Create dictionary of parameters to display for debug\\n self.exec_params = {\\\"overwrite\\\":self.model.overwrite, \\\"test_size\\\":self.model.test_size, \\\"cv\\\":self.model.cv,\\\\\\n \\\"time_series_split\\\": self.model.time_series_split, \\\"max_train_size\\\":self.model.max_train_size, \\\"lags\\\":self.model.lags,\\\\\\n \\\"lag_target\\\":self.model.lag_target, \\\"scale_target\\\":self.model.scale_target, \\\"make_stationary\\\":self.model.make_stationary,\\\\\\n \\\"random_state\\\":self.model.random_state, \\\"compress\\\":self.model.compress, \\\"retain_data\\\":self.model.retain_data,\\\\\\n \\\"calculate_importances\\\": self.model.calc_feature_importances, \\\"importances_n_repeats\\\": self.model.importances_n_repeats,\\\\\\n \\\"debug\\\":self.model.debug}\\n\\n self._print_log(1)\\n \\n # If the scaler key word arguments were included in the request, get the parameters and values\\n if len(scaler_args) > 0:\\n \\n # Transform the string of arguments into a dictionary\\n scaler_args = utils.get_kwargs(scaler_args)\\n \\n # Set scaler arguments that will be used when preprocessing the data\\n # Valid values are: StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler and QuantileTransformer\\n # More information here: http://scikit-learn.org/stable/modules/preprocessing.html\\n if 'scaler' in scaler_args:\\n self.model.scaler = scaler_args.pop('scaler')\\n \\n if 'missing' in scaler_args:\\n self.model.missing = scaler_args.pop('missing').lower()\\n \\n if 'scale_hashed' in scaler_args:\\n self.model.scale_hashed = 'true' == scaler_args.pop('scale_hashed').lower()\\n \\n if 'scale_vectors' in scaler_args:\\n self.model.scale_vectors = 'true' == scaler_args.pop('scale_vectors').lower()\\n \\n # Get the rest of the scaler parameters, converting values to the correct data type\\n self.model.scaler_kwargs = utils.get_kwargs_by_type(scaler_args) \\n else:\\n err = \\\"Arguments for scaling did not include the scaler name e.g StandardScaler\\\"\\n raise Exception(err)\\n \\n # If the estimator key word arguments were included in the request, get the parameters and values\\n if len(estimator_args) > 0:\\n \\n # Transform the string of arguments into a dictionary\\n estimator_args = utils.get_kwargs(estimator_args)\\n \\n # Set estimator arguments that will be used when preprocessing the data\\n # The parameters available will depend on the selected estimator\\n # More information here: http://scikit-learn.org/stable/modules/classes.html#api-reference\\n if 'estimator' in estimator_args:\\n self.model.estimator = estimator_args.pop('estimator')\\n \\n # Set the estimator type for the model\\n if self.model.estimator in self.classifiers:\\n self.model.estimator_type = \\\"classifier\\\"\\n elif self.model.estimator in self.regressors:\\n self.model.estimator_type = \\\"regressor\\\"\\n elif self.model.estimator in self.decomposers:\\n self.model.estimator_type = \\\"decomposer\\\"\\n elif self.model.estimator in self.clusterers:\\n self.model.estimator_type = \\\"clusterer\\\"\\n else:\\n err = \\\"Unknown estimator class: {0}\\\".format(self.model.estimator)\\n raise Exception(err)\\n\\n # Get the rest of the estimator parameters, converting values to the correct data type\\n self.model.estimator_kwargs = utils.get_kwargs_by_type(estimator_args) \\n else:\\n err = \\\"Arguments for estimator did not include the estimator class e.g. RandomForestClassifier\\\"\\n raise Exception(err)\\n \\n # If key word arguments for model evaluation metrics are included in the request, get the parameters and values\\n if metric_args is not None and len(metric_args) > 0:\\n # Transform the string of arguments into a dictionary\\n metric_args = utils.get_kwargs(metric_args)\\n \\n # Get the metric parameters, converting values to the correct data type\\n self.model.metric_args = utils.get_kwargs_by_type(metric_args) \\n \\n # If key word arguments for dimensionality reduction are included in the request, get the parameters and values\\n if dim_reduction_args is not None and len(dim_reduction_args) > 0:\\n # Transform the string of arguments into a dictionary\\n dim_reduction_args = utils.get_kwargs(dim_reduction_args)\\n \\n # Set dim_reduction arguments that will be used after preprocessing the data\\n # The parameters available will depend on the selected dimensionality reduction method\\n # Acceptable classes are PCA, KernelPCA, IncrementalPCA, TruncatedSVD\\n # More information here: http://scikit-learn.org/stable/modules/classes.html#api-reference\\n if 'reduction' in dim_reduction_args:\\n self.model.reduction = dim_reduction_args.pop('reduction')\\n \\n # Get the rest of the dim_reduction parameters, converting values to the correct data type\\n self.model.dim_reduction_args = utils.get_kwargs_by_type(dim_reduction_args) \\n else:\\n err = \\\"Arguments for dimensionality reduction did not include the class e.g. PCA\\\"\\n raise Exception(err)\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(2)\",\n \"def define_parameters(all_features, act_func, dropout, fc_layer_units, training_samples, batch_perc):\\n\\n # Get the variables in the right format\\n all_features = all_features.lower()\\n if all_features == 'y' or all_features == 'yes':\\n all_features = \\\"all conservation-based scores\\\"\\n else:\\n all_features = \\\"only PhastCon primate scores\\\"\\n\\n # Get the batch size\\n batch_size = int(training_samples.shape[0] * batch_perc)\\n\\n # Get the layer nodes in correct format\\n int_layer_units = []\\n units = fc_layer_units.split(',')\\n for unit in units:\\n int_layer_units.append(int(unit))\\n\\n # Get the dropout layers in correct format\\n dropout_booleans = []\\n dropouts = dropout.split(',')\\n for layer in dropouts:\\n layer = layer.lower()\\n if layer == 'f' or layer == 'false':\\n dropout_booleans.append(False)\\n else:\\n dropout_booleans.append(True)\\n\\n # Get the layer names of the neural network architecture\\n layers = []\\n for index, nodes in enumerate(int_layer_units):\\n layers.append('fc ({})'.format(nodes))\\n if dropout_booleans[index]:\\n layers.append('do')\\n layers = ' - '.join(layers)\\n\\n # Get the right activation function\\n act_func = act_func.lower()\\n if act_func == 'sigmoid' or act_func == 'sig' or act_func == 's':\\n act_func = tf.nn.sigmoid\\n act_title = 'sigmoid'\\n elif act_func == 'relu' or act_func == 'r':\\n act_func = tf.nn.relu\\n act_title = 'ReLU'\\n elif act_func == 'tanh' or act_func == 'tan' or act_func == 't':\\n act_func = tf.tanh\\n act_title = 'tanH'\\n else:\\n act_func = None\\n act_title = 'none'\\n\\n return all_features, act_func, act_title, batch_size, layers, dropout_booleans, int_layer_units\",\n \"def set_params(self, **kwargs) -> NoReturn:\\n pass\",\n \"def resetParams(self):\\n self.prediction = cons.init_pred # Classifier payoff - initialized to a constant initial payoff value\\n self.error = cons.init_err # Classifier error - initialized to a constant initial error value\\n self.fitness = cons.init_fit # Classifier fitness - initialized to a constant initial fitness value\",\n \"def setParams(self, paramSet):\\r\\n pass\",\n \"def _config_set(self):\\n p = self._params\\n self._config = tf.estimator.RunConfig(save_checkpoints_steps = p.save_checkpoints_steps,\\n keep_checkpoint_max = p.keep_checkpoint_max,\\n save_summary_steps = p.save_summary_steps\\n )\",\n \"def updateData(self, *args):\\n # if self.move_next_option == \\\"R\\\":\\n # self.restSampling()\\n # elif self.move_next_option == \\\"A\\\":\\n # self.addExtra()\\n # else:\\n # self.continueReview()\\n for name, value in self.parameter_inputs.items():\\n self.parameters[name] = value.value\\n # directly change the value of class variables\\n logMsg((\\\"update settings: \\\", self.ml_classifier_cls, name, value.value))\\n setattr(self.ml_classifier_cls, name, value.value)\\n\\n pass\",\n \"def set_parameters(self, mode, data):\\n if mode == 'design' or self.local_design:\\n self.new_design = True\\n\\n for key, dc in self.variables.items():\\n if isinstance(dc, dc_cp):\\n if ((mode == 'offdesign' and not self.local_design) or\\n (mode == 'design' and self.local_offdesign)):\\n self.get_attr(key).design = data[key]\\n\\n else:\\n self.get_attr(key).design = np.nan\",\n \"def set_model_parameters(self, cluster, model):\\n\\n\\t\\tmodel.maxnum=int(round(len(cluster)/(self.read_depth*0.9)))\\n\\t\\tmodel.minnum=int(round(len(cluster)/(self.read_depth*1.1)))\\n\\t\\tmodel.minsize=int(round(len(cluster)/(self.read_depth*0.9)))\\n\\t\\tmodel.expcov=int(self.read_depth)\\n\\t\\tmodel.maxcopy = self.max_copy\",\n \"def set_params(self, params: Dict) -> None:\\n self.leak.set_g(params[\\\"g_leak\\\"])\\n self.kvhh.set_g(params[\\\"g_kvhh\\\"])\\n self.cav.set_g(params[\\\"g_cav\\\"])\\n self.kca.set_g(params[\\\"g_kca\\\"])\\n self.nap.set_g(params[\\\"g_nap\\\"])\\n self.tau_ca = params[\\\"t_ca\\\"]\",\n \"def set_load_model_parameters(self):\\n\\n self.controller.set_new_model_test_input_path(self.test_input.get())\\n self.controller.set_new_model_results_input_path(self.results_input.get())\\n self.controller.set_new_model_running(False)\",\n \"def setParams(self, inputCol=None, outputCol=None, graph=None,\\n inputTensor=utils.IMAGE_INPUT_PLACEHOLDER_NAME, outputTensor=None,\\n outputMode=\\\"vector\\\"):\\n kwargs = self._input_kwargs\\n return self._set(**kwargs)\",\n \"def _set_params_initializer(self, hparams, mode, scope):\\n\\t\\tself.mode = mode\\n\\t\\t# pre_train flag is used for distinguish with pre_train and fine tune\\n\\t\\tif hparams.enable_vae:\\n\\t\\t\\t_info('Enable VAE')\\n\\t\\t\\tself.enable_vae = True\\n\\t\\t\\tself.pre_train = hparams.pre_train\\n\\t\\telse:\\n\\t\\t\\tself.enable_vae = False\\n\\t\\t\\tself.pre_train = False\\n\\t\\tself.dtype = tf.float32\\n\\t\\tself.global_step = tf.Variable(0, trainable=False)\\n\\n\\t\\t# define the input for the model\\n\\t\\tself.encoder_input_data = tf.placeholder(\\n\\t\\t\\ttf.int32, [None, None], name='encoder_input_data')\\n\\t\\tself.decoder_input_data = tf.placeholder(\\n\\t\\t\\ttf.int32, [None, None], name='decoder_input_data')\\n\\t\\tself.decoder_output_data = tf.placeholder(\\n\\t\\t\\ttf.int32, [None, None], name='decoder_output_data')\\n\\t\\tself.seq_length_encoder_input_data = tf.placeholder(\\n\\t\\t\\ttf.int32, [None], name='seq_length_encoder_input_data')\\n\\t\\tself.seq_length_decoder_input_data = tf.placeholder(\\n\\t\\t\\ttf.int32, [None], name='seq_length_decoder_input_data')\\n\\t\\t\\n\\t\\t# load some important hparamters\\n\\t\\tself.unit_type = hparams.unit_type\\n\\t\\tself.num_units = hparams.num_units\\n\\t\\tself.num_encoder_layers = hparams.num_encoder_layers\\n\\t\\tself.num_decoder_layers = hparams.num_decoder_layers\\n\\t\\tself.num_encoder_residual_layers = self.num_encoder_layers - 1\\n\\t\\tself.num_decoder_residual_layers = self.num_decoder_layers - 1\\n\\n\\t\\tself.batch_size = tf.size(self.seq_length_encoder_input_data)\\n\\n\\t\\t# set initializer\\n\\t\\trandom_seed = hparams.random_seed\\n\\t\\tinitializer = _mh.get_initializer(hparams.init_op, random_seed, hparams.init_weight)\\n\\t\\ttf.get_variable_scope().set_initializer(initializer)\\n\\n\\t\\t# embeddings\\n\\t\\tself.src_vocab_size = hparams.src_vocab_size\\n\\t\\tself.tgt_vocab_size = hparams.tgt_vocab_size\\n\\t\\tself.init_embeddings(hparams, scope)\",\n \"def test_set_params():\\n\\n tpot_obj = TPOTClassifier()\\n assert tpot_obj.set_params() is tpot_obj\",\n \"def set_parameters(self, weight, bias=None):\\n self.weight = torch.nn.Parameter(torch.as_tensor(weight))\\n if bias is not None:\\n self.bias = torch.nn.Parameter(torch.as_tensor(bias))\",\n \"def set_parameters(self, weight, bias=None):\\r\\n self.weight = torch.nn.Parameter(torch.as_tensor(weight))\\r\\n if bias is not None:\\r\\n self.bias = torch.nn.Parameter(torch.as_tensor(bias))\",\n \"def __init__(self, data, params):\\n super(VBPR, self).__init__(data, params)\\n self.embed_k = self.params.embed_k\\n self.embed_d = self.params.embed_d\\n self.learning_rate = self.params.lr\\n\\n self.directory_parameters = f'batch_{self.params.batch_size}' \\\\\\n f'-D_{self.params.embed_d}' \\\\\\n f'-K_{self.params.embed_k}' \\\\\\n f'-lr_{self.params.lr}' \\\\\\n f'-reg_{self.params.reg}'\\n\\n self.process_cnn_visual_features()\\n\\n # Initialize Model Parameters\\n self.Bp = tf.Variable(\\n self.initializer(shape=[self.dim_cnn_features, 1]), name='Bp', dtype=tf.float32)\\n self.Tu = tf.Variable(\\n self.initializer(shape=[self.num_users, self.embed_d]),\\n name='Tu', dtype=tf.float32) # (users, low_embedding_size)\\n self.F = tf.Variable(\\n self.cnn_features,\\n name='F', dtype=tf.float32, trainable=False)\\n self.E = tf.Variable(\\n self.initializer(shape=[self.dim_cnn_features, self.embed_d]),\\n name='E', dtype=tf.float32) # (items, low_embedding_size)\\n\\n self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\\n self.saver_ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self)\",\n \"def init_params(self, parameters):\\r\\n max_epoch = parameters['num_epoch']\\r\\n momentum_rate = parameters['momentum']\\r\\n loss = parameters['loss_function']\\r\\n accuracy = parameters['accuracy']\\r\\n regularization = parameters['regularization']\\r\\n batch_size = parameters['batch_size']\\r\\n optimizer = parameters['optimizer'] if parameters['optimizer'] is not None else 'batch'\\r\\n self.__init__(max_epoch, optimizer, loss, accuracy, momentum_rate, regularization, batch_size)\",\n \"def params_init(self) -> None:\\n # Initialize weights and biases with uniform distribution.\\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\\n for lyr in range(self.n_lyr):\\n self.stack_rnn[2 * lyr].params_init()\\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)\",\n \"def copy_params(self):\\n tf.get_default_session().run(self.copy_ops)\",\n \"def set_parameters(self, f_ext=0):\\n self.F_EXT = f_ext\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7162809","0.6975917","0.69572663","0.69175005","0.67739886","0.6729018","0.67094076","0.66425633","0.6621363","0.65588087","0.6556437","0.6530796","0.6530384","0.6530384","0.6520774","0.65118974","0.6503824","0.64620966","0.6427614","0.64271015","0.6417769","0.6396897","0.6351569","0.6344188","0.63168454","0.63034314","0.62854767","0.6283417","0.62797713","0.6279722","0.6272628","0.626377","0.6255415","0.6246718","0.62423754","0.6228951","0.6218327","0.62118715","0.62002796","0.61763453","0.6153377","0.61399597","0.613271","0.61031556","0.6090692","0.60744464","0.6041494","0.6039239","0.60335094","0.60253125","0.60199755","0.6014778","0.5998157","0.59933126","0.5988451","0.5985132","0.5981576","0.5980487","0.59720945","0.5949943","0.59272164","0.59145474","0.59137845","0.59106916","0.5909916","0.59040624","0.5903571","0.58800256","0.58785504","0.58782417","0.58773035","0.5875145","0.5871596","0.5864489","0.5857905","0.58430725","0.58423537","0.5833399","0.5830582","0.5828668","0.58236337","0.5823317","0.5818473","0.581799","0.58178204","0.58163154","0.5805598","0.5805457","0.5801032","0.5796173","0.5794922","0.5792861","0.5789535","0.57882583","0.5786319","0.57789224","0.57772034","0.57748497","0.5757419","0.575577"],"string":"[\n \"0.7162809\",\n \"0.6975917\",\n \"0.69572663\",\n \"0.69175005\",\n \"0.67739886\",\n \"0.6729018\",\n \"0.67094076\",\n \"0.66425633\",\n \"0.6621363\",\n \"0.65588087\",\n \"0.6556437\",\n \"0.6530796\",\n \"0.6530384\",\n \"0.6530384\",\n \"0.6520774\",\n \"0.65118974\",\n \"0.6503824\",\n \"0.64620966\",\n \"0.6427614\",\n \"0.64271015\",\n \"0.6417769\",\n \"0.6396897\",\n \"0.6351569\",\n \"0.6344188\",\n \"0.63168454\",\n \"0.63034314\",\n \"0.62854767\",\n \"0.6283417\",\n \"0.62797713\",\n \"0.6279722\",\n \"0.6272628\",\n \"0.626377\",\n \"0.6255415\",\n \"0.6246718\",\n \"0.62423754\",\n \"0.6228951\",\n \"0.6218327\",\n \"0.62118715\",\n \"0.62002796\",\n \"0.61763453\",\n \"0.6153377\",\n \"0.61399597\",\n \"0.613271\",\n \"0.61031556\",\n \"0.6090692\",\n \"0.60744464\",\n \"0.6041494\",\n \"0.6039239\",\n \"0.60335094\",\n \"0.60253125\",\n \"0.60199755\",\n \"0.6014778\",\n \"0.5998157\",\n \"0.59933126\",\n \"0.5988451\",\n \"0.5985132\",\n \"0.5981576\",\n \"0.5980487\",\n \"0.59720945\",\n \"0.5949943\",\n \"0.59272164\",\n \"0.59145474\",\n \"0.59137845\",\n \"0.59106916\",\n \"0.5909916\",\n \"0.59040624\",\n \"0.5903571\",\n \"0.58800256\",\n \"0.58785504\",\n \"0.58782417\",\n \"0.58773035\",\n \"0.5875145\",\n \"0.5871596\",\n \"0.5864489\",\n \"0.5857905\",\n \"0.58430725\",\n \"0.58423537\",\n \"0.5833399\",\n \"0.5830582\",\n \"0.5828668\",\n \"0.58236337\",\n \"0.5823317\",\n \"0.5818473\",\n \"0.581799\",\n \"0.58178204\",\n \"0.58163154\",\n \"0.5805598\",\n \"0.5805457\",\n \"0.5801032\",\n \"0.5796173\",\n \"0.5794922\",\n \"0.5792861\",\n \"0.5789535\",\n \"0.57882583\",\n \"0.5786319\",\n \"0.57789224\",\n \"0.57772034\",\n \"0.57748497\",\n \"0.5757419\",\n \"0.575577\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58768946"},"document_rank":{"kind":"string","value":"71"}}},{"rowIdx":94591,"cells":{"query":{"kind":"string","value":"Instantiate this submodule with the given UI."},"document":{"kind":"string","value":"def __init__(self, UI):\n super(COTInjectConfig, self).__init__(UI)\n self._config_file = None\n self._secondary_config_file = None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, ui: UI):\n super().__init__(ui)","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def test_ui_class_instantiates_ui_object(self):\n self.assertEqual(type(self.ui), UI, \"Does not create a ui object\")","def ui(self):\n return ui","def __init__(self):\n self.view = GuiView(self)\n return","def __init__(self, ui):\n QObject.__init__(self, ui)\n self.__ui = ui\n \n self.__translator = None\n self.__loadTranslator()\n \n self.__initMenu()\n \n self.__editors = {}\n self.__mainActions = []","def __init__(self, animPickerUI, modulesToAdd, parent=None):\n super(ART_AddModuleToCanvas, self).__init__()\n # get the directory path of the tools\n settings = QtCore.QSettings(\"Epic Games\", \"ARTv2\")\n self.toolsPath = settings.value(\"toolsPath\")\n self.iconsPath = settings.value(\"iconPath\")\n self.scriptPath = settings.value(\"scriptPath\")\n self.projectPath = settings.value(\"projectPath\")\n\n self.pickerUI = animPickerUI\n self.modules = []\n self.modulesToAdd = modulesToAdd\n\n # assign close event\n self.closeEvent = self.closeWin\n\n # build the UI\n self.buildUI()","def init_ui(self):\n self.master.title(\"Backbone\")\n self.master.geometry(\"300x150\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=90, y=10)\n\n self.btn_create_training_file = Button(self, text=\"Create & upload training file\",\n command=self.create_training_file)\n self.btn_create_training_file.place(x=30, y=40)\n\n self.btn_run_algorithm = Button(self, text=\"Run algorithm\", command=self.run_algorithm)\n self.btn_run_algorithm.place(x=80, y=70)\n\n self.btn_view_results = Button(self, text=\"View Results\", command=self.view_results)\n self.btn_view_results.place(x=85, y=100)","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def init_ui(self):\n self.parent.title(\"Roku Player Controller\")\n self.style.theme_use(\"default\")","def showUI(cls):\r\n win = cls(uiFile)\r\n win.create()\r\n return win","def __init__(self, parent=None):\n super(Representative, self).__init__(parent)\n self.setupUi(self)","def __init__(self, ui):\n QObject.__init__(self, ui)\n self.__ui = ui\n self.__initialize()\n \n self.__defaults = {\n \"VirtualEnvironmentPy2\": \"\",\n \"VirtualEnvironmentPy3\": \"\",\n \n \"UsePlainPythonPy2\": False,\n \"UsePlainPythonPy3\": False,\n \n \"ServerAddress\": \"\",\n \n \"RecentNumberApps\": 10,\n \"UseIPv6\": False,\n \n \"TranslationsEditor\": \"\",\n \n \"UseExternalBrowser\": False,\n \n \"CheckDeployMode\": False,\n }\n if isWindowsPlatform():\n self.__defaults[\"ConsoleCommandNoClose\"] = \"cmd.exe /k\"\n self.__defaults[\"ConsoleCommand\"] = \"cmd.exe /c\"\n elif isMacPlatform():\n self.__defaults[\"ConsoleCommandNoClose\"] = \"xterm -hold -e\"\n self.__defaults[\"ConsoleCommand\"] = \"xterm -e\"\n else:\n self.__defaults[\"ConsoleCommandNoClose\"] = \"konsole --noclose -e\"\n self.__defaults[\"ConsoleCommand\"] = \"konsole -e\"\n \n self.__translator = None\n self.__loadTranslator()","def setUp(self):\n self.ui = UI()","def ui(self, ui):\n\n self._ui = ui","def showUI(cls):\r\n win = cls()\r\n win.create()\r\n return win","def __init__(self, ui, inp):\n self.ui = ui\n if isinstance(inp, Buffer):\n self.display_name += \"({})\".format(inp.display_name)\n self.widget = HelpBufferView(ui, inp, self.global_keybinds)\n elif isinstance(inp, Command):\n self.display_name += \"({})\".format(inp.name)\n self.widget = HelpCommandView(ui, inp)\n\n super().__init__()","def __init__(self, scenario_controller):\n super(Ui, self).__init__()\n\n self.scenarioController = scenario_controller\n\n\n # determine if application is a script file or frozen exe\n\n if getattr(sys, 'frozen', False):\n relative_ui_path = 'AssetsV1/MainUI.ui'\n main_ui_path = os.path.dirname(sys.executable)\n\n elif __file__:\n relative_ui_path = 'MainUI.ui'\n main_ui_path = os.path.dirname(__file__)\n\n # needs to point to MainUi.ui file\n complete_ui_path = os.path.join(main_ui_path, relative_ui_path)\n\n uic.loadUi(complete_ui_path, self)\n\n self.timeline = MyTimelineWidget(self, self.scenarioController)\n self.tabWidget.setTabsClosable(True)\n self.tabs = self.tabWidget\n self.tabs.currentChanged.connect(self.current_tab_changed) \n self.tabs.tabCloseRequested.connect(self.close_current_tab)\n\n self.showMaximized()\n\n all_subsystem_names = self.scenarioController.getAvailableSubsystemNames()\n\n self.clearMenuOptions(self.menuOpen)\n self.clearMenuOptions(self.menuFile)\n self.setMenuOptions(self.menuFile, ['Save', 'Save As', 'Save As Scenario'], self.saveMenuHandler)\n self.setMenuOptionsWithParams(self.menuNew, all_subsystem_names, self.newSubsystemHandler)\n self.setMenuOptions(self.menuOpen, ['Open Command File', 'Open Scenario'], self.openMenuHandler)\n\n self.show()\n self.timeline.show()","def __init__(self, cfg):\n\t\t\n\t\tself.menu = False\n\t\t\n\t\tself.cfg = cfg\n\t\tself.run()","def __init__(self):\n self.label = \"CDU-PI\"\n self.description = \"da scrivere\"\n self.canRunInBackground = False","def __init__(self, ui):\n super(WakaTimePlugin, self).__init__(ui)\n self._ui = ui\n\n try:\n e5App().registerPluginObject(pluginTypename, self)\n except KeyError:\n pass # ignore duplicate registration","def loadUi(uifile, baseinstance=None, workingDirectory=None):\n\n # We parse the UI file and import any required custom widgets\n customWidgets = _get_custom_widgets(uifile)\n\n loader = UiLoader(baseinstance, customWidgets)\n\n if workingDirectory is not None:\n loader.setWorkingDirectory(workingDirectory)\n\n widget = loader.load(uifile)\n QMetaObject.connectSlotsByName(widget)\n return widget","def __init__(self, output=cli_output, ui=cli_ui, **kwargs):\n self.name = kwargs['name']\n self.greeting = kwargs.get('greeting', '')\n self.entry_frame = kwargs.get('entry', 'entry')\n self.frames = kwargs['frames']\n self.ui = ui\n self.output = output\n\n self.current_frame = self.entry_frame\n self.inventory = []","def __init__(self, parent=None):\n super(Inj, self).__init__(parent)\n self.setupUi(self)","def __init__(self, handle):\r\n self.context = {}\r\n self.enyo = None\r\n self.showconsole = False\r\n activity.Activity.__init__(self, handle)\r\n\r\n self.max_participants = 1\r\n\r\n self.make_toolbar()\r\n self.make_mainview()","def __init__(self):\n super(QTUIProject, self).__init__()\n self.setupUi(self)\n self.assignWidgets()\n self.show()\n self.SlotsJsonName = \"Slots Assets Folder\"\n self.BingoJsonName = \"Bingo Assets Folder\"","def gui(self):\n return gui","def initGui(self):\n\n icon_path = ':/plugins/new_raptor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Add New Raptor nest'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(self, gui, path, *args):\n pass","def init_app(self):\n \n tk_multi_depedencies = self.import_module(\"tk_multi_depedencies\")\n \n # register commands:\n display_name = self.get_setting(\"display_name\")\n \n # \"Publish Render\" ---> publish_render\n command_name = display_name.lower().replace(\" \", \"_\")\n if command_name.endswith(\"...\"):\n command_name = command_name[:-3]\n params = {\"short_name\": command_name, \n \"title\": \"%s...\" % display_name,\n \"description\": \"Publishing dependencies into Shotgun\"}\n\n show_dialog_callback = lambda: self.engine.show_dialog(display_name, self, tk_multi_depedencies.Dialog, self)\n \n self.log_debug(\"Registering command for tk-multi-dependencies\")\n self.engine.register_command(\"%s...\" % display_name, \n show_dialog_callback, \n params)","def __init__(self, ggui):\n self.gui: gamegui = ggui\n super().__init__(self.gui.top, \"New Game\")","def init_ui(self):\n\n self.master.title(\"Upload file\")\n self.master.geometry(\"300x200\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_select_file = Button(self, text=\"Select file\", command=self.on_open)\n self.btn_select_file.place(x=80, y=50)\n\n self.selected_file_name = Label(self, text=\"<Selected file name>\")\n self.selected_file_name.place(x=60, y=90)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=80, y=130)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.place(x=10, y=10)","def _setup_ui(self):\n\n self.window = ui.Widget()\n self.window.dimensions = ui.normalize_dimension((\n 0, 0,\n self.normalized_screen_resolution[0],\n self.normalized_screen_resolution[1]\n ))\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\n\n interface_frame = ui.Widget(parent=self.window)\n interface_frame.dimensions = ui.normalize_dimension((\n self.preview_renderer.window[2],\n 0,\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\n self.normalized_screen_resolution[1]\n ))\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\n\n number = ui.LabelWidget(\"\",\n name=NAME_GET_STARTED,\n parent=interface_frame,\n align=\"center\",\n font_color=(0, 0, 0, 255))\n number.dimensions = (\n 5, 5,\n interface_frame.width - 10,\n interface_frame.height - 10\n )","def __init__(self):\n # Passing the class make this Python 2 and Python 3 compatible\n super(MayaSceneLevelGeneratorUI, self).__init__(parent=maya_main_window())\n\n # Create the generators needed\n self._level_gen = level.LevelGenerator([blocks.BlockFile(\"\", blk_type) for blk_type in VALID_BLOCK_TYPES])\n self._scene_gen = MayaSceneLevelGenerator(None) # Fill in level at button press time\n\n # Window things\n self.setWindowTitle(\"Maya Scene Level Generator\")\n self.resize(500, 200)\n self.setWindowFlags(self.windowFlags() ^ PySide2.QtCore.Qt.WindowContextHelpButtonHint)\n\n # Set up for the first time\n self._create_widgets()\n self._create_layout()\n self._refresh_view()\n self._create_connections() # Order matters, since refreshing triggers connections\n\n print(self._level_gen.block_list) # TODO delete","def __init__(self, parent=None):\n\t\tScriptedLoadableModuleWidget.__init__(self, parent)\n\t\tVTKObservationMixin.__init__(self) # needed for parameter node observation\n\t\tself.logic = None\n\t\tself._parameterNode = None\n\t\tself._updatingGUIFromParameterNode = False\n\t\t\n\t\tself.elecModel = None\n\t\tself.elecModelLastButton = None\n\t\tself.elecModelButton = 0\n\t\tself.elecChanLastButton = None\n\t\tself.elecChanButton = 0\n\t\tself.lastPolButton=0\n\t\tself.active = False","def prepare_UI(self):","def __init__(self):\n self.label = \"CDU-CS\"\n self.description = \"da scrivere\"\n self.canRunInBackground = False","def InitUI(self):\n\t\tself._initMenuBar()\n\t\tself._initLayout()\n\t\t\n\t\t# Bindings\n\t\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\n\t\t\n\t\t# We can't even start without an input file\n\t\tself.OnOpen(None)","def __init__(self, parent=None):\n super(RobotSelection, self).__init__(parent)\n self.parent = parent\n self.initUI()","def initGui(self):\n\n icon_path = ':/plugins/LidarProcessor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'LiDAR Operations'),\n callback=self.run,\n parent=self.iface.mainWindow())","def __init__(self, parent=None):\n self._window = None\n\n self.setup_ui()","def create(self, parent):\n self.widget = QtCore.QObject(parent)","def init_ui(self):\n # Create GUI elements, set them in dict structure\n labelwidth = 150\n\n # Add parameter line edit for Factor Tm to Tp\n self.input_elements['factor Tm Tp'] = widgets.ParameterInputLine(\n label='Factor Tm naar Tp:',\n labelwidth=labelwidth,\n unitlabel='(NVT: Tp aanwezig)' if 'Tp' in self.hydraulic_loads.columns else '',\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\n )\n\n if 'Tp' in self.hydraulic_loads.columns or self.parent_tab.step != 'I1':\n self.input_elements['factor Tm Tp'].set_enabled(False)\n\n # Add line edit with browsebutton for Master template\n self.input_elements['mastertemplate'] = widgets.ExtendedLineEdit(\n label='Master template bestand:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_master_template)\n )\n\n # Add line edit with browsebutton for depth file\n self.input_elements['depthfile'] = widgets.ExtendedLineEdit(\n label='Bathymetry bestand:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_bathymetry_file)\n )\n\n # Add line edit with browsebutton for swan result folder\n self.input_elements['swanfolder'] = widgets.ExtendedLineEdit(\n label='SWAN uitvoer folder:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_swan_folder)\n )\n\n\n self.setLayout(QtWidgets.QVBoxLayout())\n self.layout().setSpacing(10)\n\n for _, item in self.input_elements.items():\n self.layout().addWidget(item)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.layout().addWidget(line)\n\n # OK and Cancel buttons\n self.generateButton = QtWidgets.QPushButton('Genereer invoer')\n self.generateButton.setDefault(True)\n self.generateButton.clicked.connect(self.generate)\n\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\n self.cancelButton.setAutoDefault(False)\n self.cancelButton.clicked.connect(self.cancel)\n\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\n button_box.accepted.connect(QtWidgets.QDialog.accept)\n\n self.layout().addWidget(button_box)","def __init__(self, interface):\n self.interface = interface\n self.interface.setNewWindowCallback(self.addMenu)\n self.action = QtGui.QAction('E&xport to Treepad...',\n self.interface.mainControl(),\n statusTip='Exports entire file to Treepad text format')\n self.action.triggered.connect(self.exportToTreepad)\n self.addMenu()","def __init__(self, parent=None):\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def __init__(self, ui_file, controller):\n super(InputDeviceView, self).__init__()\n self._controller = controller\n self._controller.accepted_cb = self._accepted_cb\n self._controller.finished_cb = self._finished_cb\n self._controller.rejected_cb = self._rejected_cb\n self._controller.current_gait_cb = self._current_gait_cb\n\n self._always_enabled_buttons = []\n\n # Extend the widget with all attributes and children from UI file\n loadUi(ui_file, self)\n\n self.refresh_button.clicked.connect(self._update_possible_gaits)\n\n self._create_buttons()\n self._update_possible_gaits()","def __init__(self, parent=None):\n ScriptedLoadableModuleWidget.__init__(self, parent)\n VTKObservationMixin.__init__(self) # needed for parameter node observation\n self.logic = None\n self._parameterNode = None\n self._updatingGUIFromParameterNode = False","def create_app(self):\n\n template_folder = T_SYSTEM_PATH + \"/remote_ui/www\"\n static_folder = template_folder + \"/static\"\n\n remote_ui = RemoteUI(args={\"host\": \"localhost\", \"port\": \"5000\", \"debug\": True, \"mode\": \"testing\"}, template_folder=template_folder, static_folder=static_folder)\n\n return remote_ui.app","def populateUI():\n \n # Main form layout\n form = cmds.formLayout()\n\n # Tab Layout\n tabs = cmds.tabLayout(innerMarginWidth=5, innerMarginHeight=5)\n # Form attachment config\n cmds.formLayout( form, edit=True, attachForm=((tabs, 'top', 0), (tabs, 'left', 0), (tabs, 'bottom', 0), (tabs, 'right', 0)) )\n\n # The different Tabs on the window\n spawnTab = SpawnObjectsTab()\n roadTab = RoadRiverTab()\n environmentTab = EnvironmentTab()\n\n # Tab creation\n cmds.tabLayout( tabs, edit=True, tabLabel=((spawnTab, 'Spawn Buildings'), (roadTab, 'Create Roads / Rivers'), (environmentTab, \"Create Environment\") ))","def __init__(self, parent):\r\n Frame.__init__(self, parent) \r\n \r\n self.parent = parent\r\n self.initUI()","def __init__(self):\n\n self.ai_list = []\n self.master = Tk()\n self.ui_draw = IntVar()\n\n self.populate_ai_list(self.ai_list)\n self.frame = Frame(self.master)\n self.build_ui()\n\n self.master.mainloop()","def buildUI(self):\n\n if cmds.window(\"pyART_AddToCanvasWIN\", exists=True):\n cmds.deleteUI(\"pyART_AddToCanvasWIN\", wnd=True)\n\n # create the main window\n self.mainWin = QtWidgets.QMainWindow(self.pickerUI)\n\n # create the main widget\n self.mainWidget = QtWidgets.QWidget()\n self.mainWin.setCentralWidget(self.mainWidget)\n\n # create the mainLayout\n self.layout = QtWidgets.QVBoxLayout(self.mainWidget)\n\n # load stylesheet\n styleSheetFile = utils.returnNicePath(self.toolsPath, \"Core/Scripts/Interfaces/StyleSheets/animPicker.qss\")\n f = open(styleSheetFile, \"r\")\n self.style = f.read()\n f.close()\n\n self.mainWin.setStyleSheet(self.style)\n\n self.mainWin.setMinimumSize(QtCore.QSize(250, 400))\n self.mainWin.setMaximumSize(QtCore.QSize(250, 400))\n self.mainWin.resize(250, 400)\n\n # set qt object name\n self.mainWin.setObjectName(\"pyART_AddToCanvasWIN\")\n self.mainWin.setWindowTitle(\"Add Module To Canvas\")\n\n # label, listWidget, button\n label = QtWidgets.QLabel(\"Available Modules:\")\n label.setProperty(\"boldFont\", True)\n self.layout.addWidget(label)\n\n self.moduleList = QtWidgets.QListWidget()\n self.moduleList.setMaximumSize(230, 300)\n self.moduleList.setMinimumSize(230, 300)\n self.layout.addWidget(self.moduleList)\n\n # add modules to listWidget\n self.addModulesToList()\n\n # create add button\n button = QtWidgets.QPushButton(\"Add Selected To Canvas\")\n self.layout.addWidget(button)\n button.setObjectName(\"blueButton\")\n button.clicked.connect(self.addSelectedToCanvas)\n\n # show ui\n self.mainWin.show()","def init_UI(self):\n\n self.master.title(\"Create and upload training file\")\n self.master.geometry('400x400')\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n self.user_input = Entry(self, width=10)\n self.user_input.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.create_uncertain_pairs_file()\n\n self.console_label = ConsoleLabel(self.get_uncertain_pairs_file())\n self.current_record_pair = self.console_label.get_uncertain_pair()\n\n self.btn_next = Button(self, text=\"Next\", bg=\"green\", command=self.get_input)\n self.btn_next.pack()\n\n self.back = Button(self, text=\"Back\", command=self.go_back)\n self.back.pack()","def initGui(self):\n\n icon_path = ':/plugins/buienradar_plugin/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Buienradar Plugin'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def create_app(self):\n\n template_folder = T_SYSTEM_PATH + \"/remote_ui/www\"\n static_folder = template_folder + \"/static\"\n\n remote_ui = RemoteUI(args={\"host\": \"localhost\", \"port\": \"5000\", \"debug\": True, \"mode\": \"testing\"})\n\n return remote_ui.app","def create_widget(self):\n self.widget = UILabel()","def __init__(self):\n self.label = \"CDU-PAT\"\n self.description = \"da scrivere\"\n self.canRunInBackground = False","def _ui_module(self, name, module):\n raise NotImplementedError()","def __init__(self, config=None, parent=None, catchall=True, autojson=True):\n\n Bottle.__init__(self, catchall, autojson)\n RonObject.__init__(self, config=config)\n\n if not self.view:\n self.view = View(config = {'module':self})\n\n if isinstance(config, dict):\n self.__namespace = self._get_module_namespace()\n if not self.base_path:\n self.__package = import_module(self.__namespace)\n self.base_path = os.path.dirname(self.__package.__file__)\n self.parent = parent\n\n self.load_components()","def __init__(self: object) -> None:\n super().__init__()\n self.title(\"dnazip\")\n self.configure(bg='#ebebeb')\n self.create_main()\n self.create_menu()\n self.create_buttons()\n self.file = None","def loadUi(uifile, baseinstance=None, custom_widgets=None):\n return _loadUi(uifile, baseinstance, custom_widgets)","def __init__(self, type='GUI', args='', wm=None):\n Module.__init__(self)\n\n # set a few instance variables:\n self.runProgress = 0\n\n # Keep track of all plots created:\n self.plots = dict()","def start_ui(self):\n\t\tself.start_animation()\n\t\tself.app.exec()","def init_ui(self):\n # Create GUI elements, set them in dict structure\n labelwidth = 150\n\n # Add parameter line edit for Factor Tm to Tp\n\n # Add line edit with browsebutton for swan result folder\n self.input_elements['hares folder'] = widgets.ExtendedLineEdit(\n label='HARES uitvoerbestanden folder:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_hares_folder)\n )\n\n\n self.setLayout(QtWidgets.QVBoxLayout())\n self.layout().setSpacing(10)\n\n for _, item in self.input_elements.items():\n self.layout().addWidget(item)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.layout().addWidget(line)\n\n # OK and Cancel buttons\n self.generateButton = QtWidgets.QPushButton('Start lezen uitvoerbestanden')\n self.generateButton.setDefault(True)\n self.generateButton.clicked.connect(self.generate)\n\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\n self.cancelButton.setAutoDefault(False)\n self.cancelButton.clicked.connect(self.cancel)\n\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\n button_box.accepted.connect(QtWidgets.QDialog.accept)\n\n self.layout().addWidget(button_box)","def initGui(self):\n from p4_view import Gui\n self.updateStatus(\"Launching GUI...\")\n self.gui = Gui(self, self.lmap)\n self.gui.setStart(self.cfg[\"START\"])\n self.gui.setGoal(self.cfg[\"GOAL\"])\n self.gui.setPossGoals(self.cfg[\"POSS_GOALS\"])\n #GHD\n self.gui.setMapName(self.cfg[\"MAP_FILE\"])\n self.updateStatus(\"OK\")\n self.gui.mainloop()","def initGui(self):\n\n icon_path = ':/plugins/Integracion/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\n self.action.triggered.connect(self.run)\n self.iface.addToolBarIcon(self.action)\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)","def __init__(self, main_win, parent=None):\n super(Tabs, self).__init__(parent)\n self.main_win = main_win\n\n if self.main_win.beamline is not None:\n try:\n beam = importlib.import_module('beamlines.' + self.main_win.beamline + '.beam_tabs')\n except Exception as e:\n print(e)\n msg_window('cannot import beamlines.' + self.main_win.beamline + ' module')\n raise\n self.prep_tab = beam.PrepTab()\n self.format_tab = DataTab()\n self.rec_tab = RecTab()\n self.display_tab = beam.DispTab()\n self.tabs = [self.prep_tab, self.format_tab, self.rec_tab, self.display_tab]\n else:\n self.format_tab = DataTab()\n self.rec_tab = RecTab()\n self.tabs = [self.format_tab, self.rec_tab]\n\n for tab in self.tabs:\n self.addTab(tab, tab.name)\n tab.init(self, main_win)","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def __init__(self, parent):\n super(DummyStageInterface, self).__init__(parent)\n\n open_bmp = wx.ArtProvider.GetBitmap(wx.ART_FILE_OPEN, wx.ART_TOOLBAR,\n self.tb_size)\n self.tb.AddLabelTool(wx.ID_OPEN, \"Open\", open_bmp)\n\n self.tb.Realize()","def initializeUI(self):\n self.setStyleSheet(abstyle)\n self.setGeometry(140, 100, 860, 484)\n self.setWindowTitle('Emotions Data View')\n self.setupModelView()","def create_ui(self, parent):\n view = View(\n Item(name=\"text\",\n show_label=False,\n editor=ImageTraitEditor(\n image=ImageResource(self.obj.name,\n search_path=[self.obj.parent.absolute_path]) )),\n id=\"puddle.image_editor.image_editor\",\n kind=\"live\", resizable=True)\n\n ui = self.edit_traits(view=view, parent=parent, kind=\"subpanel\")\n\n return ui","def __init__(self, inner_widget=None):\n super(GameEngineUI, self).__init__(parent=qtutils.get_maya_window())\n self.setupUi(self)\n\n self.start_btn = StartButton(self)\n self.inner_widget_vlay.addWidget(self.start_btn)\n\n self.inner_widget = inner_widget\n if inner_widget is not None:\n self.inner_widget_vlay.addWidget(inner_widget)\n # end if\n\n self.game_engine = GameEngine()\n\n # Maximize\n desktop = QtGui.QApplication.instance().desktop()\n available_geometry = desktop.screenGeometry(QtGui.QCursor().pos())\n self.setGeometry(available_geometry.x(), 0, available_geometry.width(), available_geometry.height())","def init_UI(self):\n\n self.master.title(\"Search for different companies\")\n self.master.geometry(\"400x400\")\n\n self.label_combobox = Label(self, text=\"Search by\")\n self.label_combobox.pack()\n\n self.combo_searching_options = Combobox(self, state=\"readonly\")\n self.combo_searching_options['values'] = self.combobox_values\n self.combo_searching_options.pack()\n\n self.label_input = Label(self, text=\"Entry the value\")\n self.label_input.pack()\n\n self.user_input = Entry(self, width=40)\n self.user_input.pack()\n\n self.btn_submit = Button(self, text=\"Submit\", command=self.submit)\n self.btn_submit.pack()\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.pack()","def build_ui(self):\n self.ui = UI_procstep.Ui_Form()#.Ui_USGSContactInfoWidgetMain()\n self.ui.setupUi(self)\n self.setup_dragdrop(self)\n\n self.proc_step = RepeatingElement(which='tab',\n tab_label='Step', add_text='Additional Step',\n widget=ProcessStep, remove_text='Remove Step', italic_text='Processing Steps Taken')\n\n #self.proc_step = RepeatingElement(params=params, which='tab', tab_label='Source',)\n self.proc_step.add_another()\n self.ui.widget_procstep.layout().addWidget(self.proc_step)","def __init__(self):\n super().__init__()\n self.setWindowTitle(\"Ciné Club\")\n self.setup_ui() # Ajout des Widgets.\n self.populate_movies()\n self.setup_connexions() # Création des connexion entre widgets.","def loadUI(*args, listTypes: bool=True, uiFile: AnyStr=\"\", uiString: AnyStr=\"\", verbose:\n bool=True, workingDirectory: AnyStr=\"\", **kwargs)->AnyStr:\n pass","def __init__(self, baseinstance, customWidgets=None):\n\n QUiLoader.__init__(self, baseinstance)\n\n self.baseinstance = baseinstance\n\n if customWidgets is None:\n self.customWidgets = {}\n else:\n self.customWidgets = customWidgets","def __init__(self):\n\n self.plugin_name = 'Yum'","def ui_setup(self):\n loader = QUiLoader()\n file = QFile('./user_interface/form/main_window.ui')\n file.open(QFile.ReadOnly)\n self._window = loader.load(file)\n file.close()\n\n status_bar = QStatusBar(self._window)\n status_bar.showMessage(__copyright__)\n self._window.setStatusBar(status_bar)\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\n\n self._option_panel = OptionPanel()\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\n\n # Add widget to main layout\n main_layout = self._window.main_layout\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\n\n # Add page widget to stack\n self._pages['item'] = ItemWidget()\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\n self._pages['text2'] = TextPage(text=STOP_TEXT)\n\n for index, name in enumerate(self._pages):\n print('pages {} : {} page'.format(index, name))\n self._window.widget_stack.addWidget(self._pages[name].widget)\n\n self._window.widget_stack.setCurrentIndex(0)\n\n # Build up signal / slot\n self._option_panel.currentItemChanged.connect(self.set_page)","def __init__(self, *args, **kwargs):\r\n if args:\r\n self.addonId = args[0]\r\n else:\r\n posIni = len('plugin://')\r\n posFin = sys.argv[0].find('/', posIni)\r\n addonId = sys.argv[0][posIni:posFin]\r\n self.addonId = kwargs.get('id',None) or addonId","def create(self):\n self.add_handlers({\"^T\": self.change_forms,\"^Q\": self.exit})\n self.add(npyscreen.TitleFixedText, name='Inventory items:', value='')\n self.inventory_mle = self.add(npyscreen.Pager,\n values=['Checking for plugins in the inventory, please wait...'])","def __init__(self, master=None):\r\n \r\n tk.Frame.__init__(self, master)\r\n self.master.title('AQUAMI')\r\n module_path, this_filename = os.path.split(__file__)\r\n try:\r\n self.master.iconbitmap(''.join((module_path, '/icon.ico')))\r\n except:\r\n pass\r\n\r\n self.initGUI()","def create(self, parent):\n self.widget = _QMainWindow(parent)","def __init__(self):\n self.label = \"DBT-2-DXF\"\n self.description = \"da scrivere\"\n self.canRunInBackground = False","def initGui(self):\n\n icon_path = ':/plugins/Hybriddekning/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Hybriddekning'),\n callback=self.run,\n parent=self.iface.mainWindow())","def __init__(self, vlc_player, drone_gui):\n QMainWindow.__init__(self)\n self.setWindowTitle(\"VLC Drone Video Player\")\n\n # save the media player\n self.mediaplayer = vlc_player\n\n # need a reference to the main drone vision class\n self.drone_vision = drone_gui\n\n # create the GUI\n self.createUI()","def __init__(self):\n super().__init__()\n # Main UI code goes here\n lineEdit = qtw.QLineEdit(\"A Line edit!\", self)\n label = qtw.QLabel(\"Hello Everybody\", self)\n \n # End main UI code\n self.show()","def __init__(self, controller):\n global gui\n print(\"Upload folder: \" + UPLOAD_FOLDER)\n gui = self\n self.controller = controller\n try:\n run()\n finally:\n self.controller.quit()","def __init__(self, parent):\n self.name = \"Base.View\"\n self.parent = parent\n self.Main = parent.Main","def __init__(self, gui = None):\n\n QtCore.QThread.__init__(self)\n self.gui = gui\n\n return None","def __init__(self):\n _hypre.HypreILU_swiginit(self, _hypre.new_HypreILU())","def __init__(self):\n self.model = self.load_model()\n self.form_html = self.create_form_html()","def __init__(self, *args):\n this = _libsbml.new_CompSBasePlugin(*args)\n try: self.this.append(this)\n except: self.this = this","def create_submodule(self, *args: Any, **kwargs: Any) -> Submodule:\n return Submodule.add(self, *args, **kwargs)","def __init__(self, controller):\n super().__init__(controller)\n\n # The hovered input when entering this View.\n self.first_inp = \"s\"\n\n # Initialize selected variable.\n self.selected = None\n\n # Make background graphics.\n self.make_background_graphics()\n\n # Make Buttons.\n self.make_buttons()\n\n # Make the information box. This explains each Button.\n self.make_info_box()\n\n # Initializes popup.\n self.make_popup()\n\n # Map of input to functions.\n enter = self.graphics.ENTER_KEY\n self.controls = {\n # Pressing \"q\" will go back to the main menu.\n \"q\": lambda: Action(\"goto main menu view\", []),\n\n # Movement keys.\n \"w\": lambda: self.move_cursor(Direction.U),\n \"a\": lambda: self.move_cursor(Direction.L),\n \"s\": lambda: self.move_cursor(Direction.D),\n \"d\": lambda: self.move_cursor(Direction.R),\n\n # Repeat the last valid input.\n enter: self.repeat_last_valid_input,\n\n # Click the selected UIElement.\n \"m\": self.click\n }"],"string":"[\n \"def __init__(self, ui: UI):\\n super().__init__(ui)\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def test_ui_class_instantiates_ui_object(self):\\n self.assertEqual(type(self.ui), UI, \\\"Does not create a ui object\\\")\",\n \"def ui(self):\\n return ui\",\n \"def __init__(self):\\n self.view = GuiView(self)\\n return\",\n \"def __init__(self, ui):\\n QObject.__init__(self, ui)\\n self.__ui = ui\\n \\n self.__translator = None\\n self.__loadTranslator()\\n \\n self.__initMenu()\\n \\n self.__editors = {}\\n self.__mainActions = []\",\n \"def __init__(self, animPickerUI, modulesToAdd, parent=None):\\n super(ART_AddModuleToCanvas, self).__init__()\\n # get the directory path of the tools\\n settings = QtCore.QSettings(\\\"Epic Games\\\", \\\"ARTv2\\\")\\n self.toolsPath = settings.value(\\\"toolsPath\\\")\\n self.iconsPath = settings.value(\\\"iconPath\\\")\\n self.scriptPath = settings.value(\\\"scriptPath\\\")\\n self.projectPath = settings.value(\\\"projectPath\\\")\\n\\n self.pickerUI = animPickerUI\\n self.modules = []\\n self.modulesToAdd = modulesToAdd\\n\\n # assign close event\\n self.closeEvent = self.closeWin\\n\\n # build the UI\\n self.buildUI()\",\n \"def init_ui(self):\\n self.master.title(\\\"Backbone\\\")\\n self.master.geometry(\\\"300x150\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=90, y=10)\\n\\n self.btn_create_training_file = Button(self, text=\\\"Create & upload training file\\\",\\n command=self.create_training_file)\\n self.btn_create_training_file.place(x=30, y=40)\\n\\n self.btn_run_algorithm = Button(self, text=\\\"Run algorithm\\\", command=self.run_algorithm)\\n self.btn_run_algorithm.place(x=80, y=70)\\n\\n self.btn_view_results = Button(self, text=\\\"View Results\\\", command=self.view_results)\\n self.btn_view_results.place(x=85, y=100)\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def init_ui(self):\\n self.parent.title(\\\"Roku Player Controller\\\")\\n self.style.theme_use(\\\"default\\\")\",\n \"def showUI(cls):\\r\\n win = cls(uiFile)\\r\\n win.create()\\r\\n return win\",\n \"def __init__(self, parent=None):\\n super(Representative, self).__init__(parent)\\n self.setupUi(self)\",\n \"def __init__(self, ui):\\n QObject.__init__(self, ui)\\n self.__ui = ui\\n self.__initialize()\\n \\n self.__defaults = {\\n \\\"VirtualEnvironmentPy2\\\": \\\"\\\",\\n \\\"VirtualEnvironmentPy3\\\": \\\"\\\",\\n \\n \\\"UsePlainPythonPy2\\\": False,\\n \\\"UsePlainPythonPy3\\\": False,\\n \\n \\\"ServerAddress\\\": \\\"\\\",\\n \\n \\\"RecentNumberApps\\\": 10,\\n \\\"UseIPv6\\\": False,\\n \\n \\\"TranslationsEditor\\\": \\\"\\\",\\n \\n \\\"UseExternalBrowser\\\": False,\\n \\n \\\"CheckDeployMode\\\": False,\\n }\\n if isWindowsPlatform():\\n self.__defaults[\\\"ConsoleCommandNoClose\\\"] = \\\"cmd.exe /k\\\"\\n self.__defaults[\\\"ConsoleCommand\\\"] = \\\"cmd.exe /c\\\"\\n elif isMacPlatform():\\n self.__defaults[\\\"ConsoleCommandNoClose\\\"] = \\\"xterm -hold -e\\\"\\n self.__defaults[\\\"ConsoleCommand\\\"] = \\\"xterm -e\\\"\\n else:\\n self.__defaults[\\\"ConsoleCommandNoClose\\\"] = \\\"konsole --noclose -e\\\"\\n self.__defaults[\\\"ConsoleCommand\\\"] = \\\"konsole -e\\\"\\n \\n self.__translator = None\\n self.__loadTranslator()\",\n \"def setUp(self):\\n self.ui = UI()\",\n \"def ui(self, ui):\\n\\n self._ui = ui\",\n \"def showUI(cls):\\r\\n win = cls()\\r\\n win.create()\\r\\n return win\",\n \"def __init__(self, ui, inp):\\n self.ui = ui\\n if isinstance(inp, Buffer):\\n self.display_name += \\\"({})\\\".format(inp.display_name)\\n self.widget = HelpBufferView(ui, inp, self.global_keybinds)\\n elif isinstance(inp, Command):\\n self.display_name += \\\"({})\\\".format(inp.name)\\n self.widget = HelpCommandView(ui, inp)\\n\\n super().__init__()\",\n \"def __init__(self, scenario_controller):\\n super(Ui, self).__init__()\\n\\n self.scenarioController = scenario_controller\\n\\n\\n # determine if application is a script file or frozen exe\\n\\n if getattr(sys, 'frozen', False):\\n relative_ui_path = 'AssetsV1/MainUI.ui'\\n main_ui_path = os.path.dirname(sys.executable)\\n\\n elif __file__:\\n relative_ui_path = 'MainUI.ui'\\n main_ui_path = os.path.dirname(__file__)\\n\\n # needs to point to MainUi.ui file\\n complete_ui_path = os.path.join(main_ui_path, relative_ui_path)\\n\\n uic.loadUi(complete_ui_path, self)\\n\\n self.timeline = MyTimelineWidget(self, self.scenarioController)\\n self.tabWidget.setTabsClosable(True)\\n self.tabs = self.tabWidget\\n self.tabs.currentChanged.connect(self.current_tab_changed) \\n self.tabs.tabCloseRequested.connect(self.close_current_tab)\\n\\n self.showMaximized()\\n\\n all_subsystem_names = self.scenarioController.getAvailableSubsystemNames()\\n\\n self.clearMenuOptions(self.menuOpen)\\n self.clearMenuOptions(self.menuFile)\\n self.setMenuOptions(self.menuFile, ['Save', 'Save As', 'Save As Scenario'], self.saveMenuHandler)\\n self.setMenuOptionsWithParams(self.menuNew, all_subsystem_names, self.newSubsystemHandler)\\n self.setMenuOptions(self.menuOpen, ['Open Command File', 'Open Scenario'], self.openMenuHandler)\\n\\n self.show()\\n self.timeline.show()\",\n \"def __init__(self, cfg):\\n\\t\\t\\n\\t\\tself.menu = False\\n\\t\\t\\n\\t\\tself.cfg = cfg\\n\\t\\tself.run()\",\n \"def __init__(self):\\n self.label = \\\"CDU-PI\\\"\\n self.description = \\\"da scrivere\\\"\\n self.canRunInBackground = False\",\n \"def __init__(self, ui):\\n super(WakaTimePlugin, self).__init__(ui)\\n self._ui = ui\\n\\n try:\\n e5App().registerPluginObject(pluginTypename, self)\\n except KeyError:\\n pass # ignore duplicate registration\",\n \"def loadUi(uifile, baseinstance=None, workingDirectory=None):\\n\\n # We parse the UI file and import any required custom widgets\\n customWidgets = _get_custom_widgets(uifile)\\n\\n loader = UiLoader(baseinstance, customWidgets)\\n\\n if workingDirectory is not None:\\n loader.setWorkingDirectory(workingDirectory)\\n\\n widget = loader.load(uifile)\\n QMetaObject.connectSlotsByName(widget)\\n return widget\",\n \"def __init__(self, output=cli_output, ui=cli_ui, **kwargs):\\n self.name = kwargs['name']\\n self.greeting = kwargs.get('greeting', '')\\n self.entry_frame = kwargs.get('entry', 'entry')\\n self.frames = kwargs['frames']\\n self.ui = ui\\n self.output = output\\n\\n self.current_frame = self.entry_frame\\n self.inventory = []\",\n \"def __init__(self, parent=None):\\n super(Inj, self).__init__(parent)\\n self.setupUi(self)\",\n \"def __init__(self, handle):\\r\\n self.context = {}\\r\\n self.enyo = None\\r\\n self.showconsole = False\\r\\n activity.Activity.__init__(self, handle)\\r\\n\\r\\n self.max_participants = 1\\r\\n\\r\\n self.make_toolbar()\\r\\n self.make_mainview()\",\n \"def __init__(self):\\n super(QTUIProject, self).__init__()\\n self.setupUi(self)\\n self.assignWidgets()\\n self.show()\\n self.SlotsJsonName = \\\"Slots Assets Folder\\\"\\n self.BingoJsonName = \\\"Bingo Assets Folder\\\"\",\n \"def gui(self):\\n return gui\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/new_raptor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Add New Raptor nest'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(self, gui, path, *args):\\n pass\",\n \"def init_app(self):\\n \\n tk_multi_depedencies = self.import_module(\\\"tk_multi_depedencies\\\")\\n \\n # register commands:\\n display_name = self.get_setting(\\\"display_name\\\")\\n \\n # \\\"Publish Render\\\" ---> publish_render\\n command_name = display_name.lower().replace(\\\" \\\", \\\"_\\\")\\n if command_name.endswith(\\\"...\\\"):\\n command_name = command_name[:-3]\\n params = {\\\"short_name\\\": command_name, \\n \\\"title\\\": \\\"%s...\\\" % display_name,\\n \\\"description\\\": \\\"Publishing dependencies into Shotgun\\\"}\\n\\n show_dialog_callback = lambda: self.engine.show_dialog(display_name, self, tk_multi_depedencies.Dialog, self)\\n \\n self.log_debug(\\\"Registering command for tk-multi-dependencies\\\")\\n self.engine.register_command(\\\"%s...\\\" % display_name, \\n show_dialog_callback, \\n params)\",\n \"def __init__(self, ggui):\\n self.gui: gamegui = ggui\\n super().__init__(self.gui.top, \\\"New Game\\\")\",\n \"def init_ui(self):\\n\\n self.master.title(\\\"Upload file\\\")\\n self.master.geometry(\\\"300x200\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_select_file = Button(self, text=\\\"Select file\\\", command=self.on_open)\\n self.btn_select_file.place(x=80, y=50)\\n\\n self.selected_file_name = Label(self, text=\\\"<Selected file name>\\\")\\n self.selected_file_name.place(x=60, y=90)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=80, y=130)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.place(x=10, y=10)\",\n \"def _setup_ui(self):\\n\\n self.window = ui.Widget()\\n self.window.dimensions = ui.normalize_dimension((\\n 0, 0,\\n self.normalized_screen_resolution[0],\\n self.normalized_screen_resolution[1]\\n ))\\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\\n\\n interface_frame = ui.Widget(parent=self.window)\\n interface_frame.dimensions = ui.normalize_dimension((\\n self.preview_renderer.window[2],\\n 0,\\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\\n self.normalized_screen_resolution[1]\\n ))\\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\\n\\n number = ui.LabelWidget(\\\"\\\",\\n name=NAME_GET_STARTED,\\n parent=interface_frame,\\n align=\\\"center\\\",\\n font_color=(0, 0, 0, 255))\\n number.dimensions = (\\n 5, 5,\\n interface_frame.width - 10,\\n interface_frame.height - 10\\n )\",\n \"def __init__(self):\\n # Passing the class make this Python 2 and Python 3 compatible\\n super(MayaSceneLevelGeneratorUI, self).__init__(parent=maya_main_window())\\n\\n # Create the generators needed\\n self._level_gen = level.LevelGenerator([blocks.BlockFile(\\\"\\\", blk_type) for blk_type in VALID_BLOCK_TYPES])\\n self._scene_gen = MayaSceneLevelGenerator(None) # Fill in level at button press time\\n\\n # Window things\\n self.setWindowTitle(\\\"Maya Scene Level Generator\\\")\\n self.resize(500, 200)\\n self.setWindowFlags(self.windowFlags() ^ PySide2.QtCore.Qt.WindowContextHelpButtonHint)\\n\\n # Set up for the first time\\n self._create_widgets()\\n self._create_layout()\\n self._refresh_view()\\n self._create_connections() # Order matters, since refreshing triggers connections\\n\\n print(self._level_gen.block_list) # TODO delete\",\n \"def __init__(self, parent=None):\\n\\t\\tScriptedLoadableModuleWidget.__init__(self, parent)\\n\\t\\tVTKObservationMixin.__init__(self) # needed for parameter node observation\\n\\t\\tself.logic = None\\n\\t\\tself._parameterNode = None\\n\\t\\tself._updatingGUIFromParameterNode = False\\n\\t\\t\\n\\t\\tself.elecModel = None\\n\\t\\tself.elecModelLastButton = None\\n\\t\\tself.elecModelButton = 0\\n\\t\\tself.elecChanLastButton = None\\n\\t\\tself.elecChanButton = 0\\n\\t\\tself.lastPolButton=0\\n\\t\\tself.active = False\",\n \"def prepare_UI(self):\",\n \"def __init__(self):\\n self.label = \\\"CDU-CS\\\"\\n self.description = \\\"da scrivere\\\"\\n self.canRunInBackground = False\",\n \"def InitUI(self):\\n\\t\\tself._initMenuBar()\\n\\t\\tself._initLayout()\\n\\t\\t\\n\\t\\t# Bindings\\n\\t\\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\\n\\t\\t\\n\\t\\t# We can't even start without an input file\\n\\t\\tself.OnOpen(None)\",\n \"def __init__(self, parent=None):\\n super(RobotSelection, self).__init__(parent)\\n self.parent = parent\\n self.initUI()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/LidarProcessor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'LiDAR Operations'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def __init__(self, parent=None):\\n self._window = None\\n\\n self.setup_ui()\",\n \"def create(self, parent):\\n self.widget = QtCore.QObject(parent)\",\n \"def init_ui(self):\\n # Create GUI elements, set them in dict structure\\n labelwidth = 150\\n\\n # Add parameter line edit for Factor Tm to Tp\\n self.input_elements['factor Tm Tp'] = widgets.ParameterInputLine(\\n label='Factor Tm naar Tp:',\\n labelwidth=labelwidth,\\n unitlabel='(NVT: Tp aanwezig)' if 'Tp' in self.hydraulic_loads.columns else '',\\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\\n )\\n\\n if 'Tp' in self.hydraulic_loads.columns or self.parent_tab.step != 'I1':\\n self.input_elements['factor Tm Tp'].set_enabled(False)\\n\\n # Add line edit with browsebutton for Master template\\n self.input_elements['mastertemplate'] = widgets.ExtendedLineEdit(\\n label='Master template bestand:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_master_template)\\n )\\n\\n # Add line edit with browsebutton for depth file\\n self.input_elements['depthfile'] = widgets.ExtendedLineEdit(\\n label='Bathymetry bestand:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_bathymetry_file)\\n )\\n\\n # Add line edit with browsebutton for swan result folder\\n self.input_elements['swanfolder'] = widgets.ExtendedLineEdit(\\n label='SWAN uitvoer folder:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_swan_folder)\\n )\\n\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n self.layout().setSpacing(10)\\n\\n for _, item in self.input_elements.items():\\n self.layout().addWidget(item)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.layout().addWidget(line)\\n\\n # OK and Cancel buttons\\n self.generateButton = QtWidgets.QPushButton('Genereer invoer')\\n self.generateButton.setDefault(True)\\n self.generateButton.clicked.connect(self.generate)\\n\\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\\n self.cancelButton.setAutoDefault(False)\\n self.cancelButton.clicked.connect(self.cancel)\\n\\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\\n button_box.accepted.connect(QtWidgets.QDialog.accept)\\n\\n self.layout().addWidget(button_box)\",\n \"def __init__(self, interface):\\n self.interface = interface\\n self.interface.setNewWindowCallback(self.addMenu)\\n self.action = QtGui.QAction('E&xport to Treepad...',\\n self.interface.mainControl(),\\n statusTip='Exports entire file to Treepad text format')\\n self.action.triggered.connect(self.exportToTreepad)\\n self.addMenu()\",\n \"def __init__(self, parent=None):\\n ScriptedLoadableModuleWidget.__init__(self, parent)\\n VTKObservationMixin.__init__(self) # needed for parameter node observation\\n self.logic = None\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def __init__(self, ui_file, controller):\\n super(InputDeviceView, self).__init__()\\n self._controller = controller\\n self._controller.accepted_cb = self._accepted_cb\\n self._controller.finished_cb = self._finished_cb\\n self._controller.rejected_cb = self._rejected_cb\\n self._controller.current_gait_cb = self._current_gait_cb\\n\\n self._always_enabled_buttons = []\\n\\n # Extend the widget with all attributes and children from UI file\\n loadUi(ui_file, self)\\n\\n self.refresh_button.clicked.connect(self._update_possible_gaits)\\n\\n self._create_buttons()\\n self._update_possible_gaits()\",\n \"def __init__(self, parent=None):\\n ScriptedLoadableModuleWidget.__init__(self, parent)\\n VTKObservationMixin.__init__(self) # needed for parameter node observation\\n self.logic = None\\n self._parameterNode = None\\n self._updatingGUIFromParameterNode = False\",\n \"def create_app(self):\\n\\n template_folder = T_SYSTEM_PATH + \\\"/remote_ui/www\\\"\\n static_folder = template_folder + \\\"/static\\\"\\n\\n remote_ui = RemoteUI(args={\\\"host\\\": \\\"localhost\\\", \\\"port\\\": \\\"5000\\\", \\\"debug\\\": True, \\\"mode\\\": \\\"testing\\\"}, template_folder=template_folder, static_folder=static_folder)\\n\\n return remote_ui.app\",\n \"def populateUI():\\n \\n # Main form layout\\n form = cmds.formLayout()\\n\\n # Tab Layout\\n tabs = cmds.tabLayout(innerMarginWidth=5, innerMarginHeight=5)\\n # Form attachment config\\n cmds.formLayout( form, edit=True, attachForm=((tabs, 'top', 0), (tabs, 'left', 0), (tabs, 'bottom', 0), (tabs, 'right', 0)) )\\n\\n # The different Tabs on the window\\n spawnTab = SpawnObjectsTab()\\n roadTab = RoadRiverTab()\\n environmentTab = EnvironmentTab()\\n\\n # Tab creation\\n cmds.tabLayout( tabs, edit=True, tabLabel=((spawnTab, 'Spawn Buildings'), (roadTab, 'Create Roads / Rivers'), (environmentTab, \\\"Create Environment\\\") ))\",\n \"def __init__(self, parent):\\r\\n Frame.__init__(self, parent) \\r\\n \\r\\n self.parent = parent\\r\\n self.initUI()\",\n \"def __init__(self):\\n\\n self.ai_list = []\\n self.master = Tk()\\n self.ui_draw = IntVar()\\n\\n self.populate_ai_list(self.ai_list)\\n self.frame = Frame(self.master)\\n self.build_ui()\\n\\n self.master.mainloop()\",\n \"def buildUI(self):\\n\\n if cmds.window(\\\"pyART_AddToCanvasWIN\\\", exists=True):\\n cmds.deleteUI(\\\"pyART_AddToCanvasWIN\\\", wnd=True)\\n\\n # create the main window\\n self.mainWin = QtWidgets.QMainWindow(self.pickerUI)\\n\\n # create the main widget\\n self.mainWidget = QtWidgets.QWidget()\\n self.mainWin.setCentralWidget(self.mainWidget)\\n\\n # create the mainLayout\\n self.layout = QtWidgets.QVBoxLayout(self.mainWidget)\\n\\n # load stylesheet\\n styleSheetFile = utils.returnNicePath(self.toolsPath, \\\"Core/Scripts/Interfaces/StyleSheets/animPicker.qss\\\")\\n f = open(styleSheetFile, \\\"r\\\")\\n self.style = f.read()\\n f.close()\\n\\n self.mainWin.setStyleSheet(self.style)\\n\\n self.mainWin.setMinimumSize(QtCore.QSize(250, 400))\\n self.mainWin.setMaximumSize(QtCore.QSize(250, 400))\\n self.mainWin.resize(250, 400)\\n\\n # set qt object name\\n self.mainWin.setObjectName(\\\"pyART_AddToCanvasWIN\\\")\\n self.mainWin.setWindowTitle(\\\"Add Module To Canvas\\\")\\n\\n # label, listWidget, button\\n label = QtWidgets.QLabel(\\\"Available Modules:\\\")\\n label.setProperty(\\\"boldFont\\\", True)\\n self.layout.addWidget(label)\\n\\n self.moduleList = QtWidgets.QListWidget()\\n self.moduleList.setMaximumSize(230, 300)\\n self.moduleList.setMinimumSize(230, 300)\\n self.layout.addWidget(self.moduleList)\\n\\n # add modules to listWidget\\n self.addModulesToList()\\n\\n # create add button\\n button = QtWidgets.QPushButton(\\\"Add Selected To Canvas\\\")\\n self.layout.addWidget(button)\\n button.setObjectName(\\\"blueButton\\\")\\n button.clicked.connect(self.addSelectedToCanvas)\\n\\n # show ui\\n self.mainWin.show()\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Create and upload training file\\\")\\n self.master.geometry('400x400')\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n self.user_input = Entry(self, width=10)\\n self.user_input.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.create_uncertain_pairs_file()\\n\\n self.console_label = ConsoleLabel(self.get_uncertain_pairs_file())\\n self.current_record_pair = self.console_label.get_uncertain_pair()\\n\\n self.btn_next = Button(self, text=\\\"Next\\\", bg=\\\"green\\\", command=self.get_input)\\n self.btn_next.pack()\\n\\n self.back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.back.pack()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/buienradar_plugin/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Buienradar Plugin'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def create_app(self):\\n\\n template_folder = T_SYSTEM_PATH + \\\"/remote_ui/www\\\"\\n static_folder = template_folder + \\\"/static\\\"\\n\\n remote_ui = RemoteUI(args={\\\"host\\\": \\\"localhost\\\", \\\"port\\\": \\\"5000\\\", \\\"debug\\\": True, \\\"mode\\\": \\\"testing\\\"})\\n\\n return remote_ui.app\",\n \"def create_widget(self):\\n self.widget = UILabel()\",\n \"def __init__(self):\\n self.label = \\\"CDU-PAT\\\"\\n self.description = \\\"da scrivere\\\"\\n self.canRunInBackground = False\",\n \"def _ui_module(self, name, module):\\n raise NotImplementedError()\",\n \"def __init__(self, config=None, parent=None, catchall=True, autojson=True):\\n\\n Bottle.__init__(self, catchall, autojson)\\n RonObject.__init__(self, config=config)\\n\\n if not self.view:\\n self.view = View(config = {'module':self})\\n\\n if isinstance(config, dict):\\n self.__namespace = self._get_module_namespace()\\n if not self.base_path:\\n self.__package = import_module(self.__namespace)\\n self.base_path = os.path.dirname(self.__package.__file__)\\n self.parent = parent\\n\\n self.load_components()\",\n \"def __init__(self: object) -> None:\\n super().__init__()\\n self.title(\\\"dnazip\\\")\\n self.configure(bg='#ebebeb')\\n self.create_main()\\n self.create_menu()\\n self.create_buttons()\\n self.file = None\",\n \"def loadUi(uifile, baseinstance=None, custom_widgets=None):\\n return _loadUi(uifile, baseinstance, custom_widgets)\",\n \"def __init__(self, type='GUI', args='', wm=None):\\n Module.__init__(self)\\n\\n # set a few instance variables:\\n self.runProgress = 0\\n\\n # Keep track of all plots created:\\n self.plots = dict()\",\n \"def start_ui(self):\\n\\t\\tself.start_animation()\\n\\t\\tself.app.exec()\",\n \"def init_ui(self):\\n # Create GUI elements, set them in dict structure\\n labelwidth = 150\\n\\n # Add parameter line edit for Factor Tm to Tp\\n\\n # Add line edit with browsebutton for swan result folder\\n self.input_elements['hares folder'] = widgets.ExtendedLineEdit(\\n label='HARES uitvoerbestanden folder:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_hares_folder)\\n )\\n\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n self.layout().setSpacing(10)\\n\\n for _, item in self.input_elements.items():\\n self.layout().addWidget(item)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.layout().addWidget(line)\\n\\n # OK and Cancel buttons\\n self.generateButton = QtWidgets.QPushButton('Start lezen uitvoerbestanden')\\n self.generateButton.setDefault(True)\\n self.generateButton.clicked.connect(self.generate)\\n\\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\\n self.cancelButton.setAutoDefault(False)\\n self.cancelButton.clicked.connect(self.cancel)\\n\\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\\n button_box.accepted.connect(QtWidgets.QDialog.accept)\\n\\n self.layout().addWidget(button_box)\",\n \"def initGui(self):\\n from p4_view import Gui\\n self.updateStatus(\\\"Launching GUI...\\\")\\n self.gui = Gui(self, self.lmap)\\n self.gui.setStart(self.cfg[\\\"START\\\"])\\n self.gui.setGoal(self.cfg[\\\"GOAL\\\"])\\n self.gui.setPossGoals(self.cfg[\\\"POSS_GOALS\\\"])\\n #GHD\\n self.gui.setMapName(self.cfg[\\\"MAP_FILE\\\"])\\n self.updateStatus(\\\"OK\\\")\\n self.gui.mainloop()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Integracion/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\\n self.action.triggered.connect(self.run)\\n self.iface.addToolBarIcon(self.action)\\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)\",\n \"def __init__(self, main_win, parent=None):\\n super(Tabs, self).__init__(parent)\\n self.main_win = main_win\\n\\n if self.main_win.beamline is not None:\\n try:\\n beam = importlib.import_module('beamlines.' + self.main_win.beamline + '.beam_tabs')\\n except Exception as e:\\n print(e)\\n msg_window('cannot import beamlines.' + self.main_win.beamline + ' module')\\n raise\\n self.prep_tab = beam.PrepTab()\\n self.format_tab = DataTab()\\n self.rec_tab = RecTab()\\n self.display_tab = beam.DispTab()\\n self.tabs = [self.prep_tab, self.format_tab, self.rec_tab, self.display_tab]\\n else:\\n self.format_tab = DataTab()\\n self.rec_tab = RecTab()\\n self.tabs = [self.format_tab, self.rec_tab]\\n\\n for tab in self.tabs:\\n self.addTab(tab, tab.name)\\n tab.init(self, main_win)\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def __init__(self, parent):\\n super(DummyStageInterface, self).__init__(parent)\\n\\n open_bmp = wx.ArtProvider.GetBitmap(wx.ART_FILE_OPEN, wx.ART_TOOLBAR,\\n self.tb_size)\\n self.tb.AddLabelTool(wx.ID_OPEN, \\\"Open\\\", open_bmp)\\n\\n self.tb.Realize()\",\n \"def initializeUI(self):\\n self.setStyleSheet(abstyle)\\n self.setGeometry(140, 100, 860, 484)\\n self.setWindowTitle('Emotions Data View')\\n self.setupModelView()\",\n \"def create_ui(self, parent):\\n view = View(\\n Item(name=\\\"text\\\",\\n show_label=False,\\n editor=ImageTraitEditor(\\n image=ImageResource(self.obj.name,\\n search_path=[self.obj.parent.absolute_path]) )),\\n id=\\\"puddle.image_editor.image_editor\\\",\\n kind=\\\"live\\\", resizable=True)\\n\\n ui = self.edit_traits(view=view, parent=parent, kind=\\\"subpanel\\\")\\n\\n return ui\",\n \"def __init__(self, inner_widget=None):\\n super(GameEngineUI, self).__init__(parent=qtutils.get_maya_window())\\n self.setupUi(self)\\n\\n self.start_btn = StartButton(self)\\n self.inner_widget_vlay.addWidget(self.start_btn)\\n\\n self.inner_widget = inner_widget\\n if inner_widget is not None:\\n self.inner_widget_vlay.addWidget(inner_widget)\\n # end if\\n\\n self.game_engine = GameEngine()\\n\\n # Maximize\\n desktop = QtGui.QApplication.instance().desktop()\\n available_geometry = desktop.screenGeometry(QtGui.QCursor().pos())\\n self.setGeometry(available_geometry.x(), 0, available_geometry.width(), available_geometry.height())\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Search for different companies\\\")\\n self.master.geometry(\\\"400x400\\\")\\n\\n self.label_combobox = Label(self, text=\\\"Search by\\\")\\n self.label_combobox.pack()\\n\\n self.combo_searching_options = Combobox(self, state=\\\"readonly\\\")\\n self.combo_searching_options['values'] = self.combobox_values\\n self.combo_searching_options.pack()\\n\\n self.label_input = Label(self, text=\\\"Entry the value\\\")\\n self.label_input.pack()\\n\\n self.user_input = Entry(self, width=40)\\n self.user_input.pack()\\n\\n self.btn_submit = Button(self, text=\\\"Submit\\\", command=self.submit)\\n self.btn_submit.pack()\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.pack()\",\n \"def build_ui(self):\\n self.ui = UI_procstep.Ui_Form()#.Ui_USGSContactInfoWidgetMain()\\n self.ui.setupUi(self)\\n self.setup_dragdrop(self)\\n\\n self.proc_step = RepeatingElement(which='tab',\\n tab_label='Step', add_text='Additional Step',\\n widget=ProcessStep, remove_text='Remove Step', italic_text='Processing Steps Taken')\\n\\n #self.proc_step = RepeatingElement(params=params, which='tab', tab_label='Source',)\\n self.proc_step.add_another()\\n self.ui.widget_procstep.layout().addWidget(self.proc_step)\",\n \"def __init__(self):\\n super().__init__()\\n self.setWindowTitle(\\\"Ciné Club\\\")\\n self.setup_ui() # Ajout des Widgets.\\n self.populate_movies()\\n self.setup_connexions() # Création des connexion entre widgets.\",\n \"def loadUI(*args, listTypes: bool=True, uiFile: AnyStr=\\\"\\\", uiString: AnyStr=\\\"\\\", verbose:\\n bool=True, workingDirectory: AnyStr=\\\"\\\", **kwargs)->AnyStr:\\n pass\",\n \"def __init__(self, baseinstance, customWidgets=None):\\n\\n QUiLoader.__init__(self, baseinstance)\\n\\n self.baseinstance = baseinstance\\n\\n if customWidgets is None:\\n self.customWidgets = {}\\n else:\\n self.customWidgets = customWidgets\",\n \"def __init__(self):\\n\\n self.plugin_name = 'Yum'\",\n \"def ui_setup(self):\\n loader = QUiLoader()\\n file = QFile('./user_interface/form/main_window.ui')\\n file.open(QFile.ReadOnly)\\n self._window = loader.load(file)\\n file.close()\\n\\n status_bar = QStatusBar(self._window)\\n status_bar.showMessage(__copyright__)\\n self._window.setStatusBar(status_bar)\\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\\n\\n self._option_panel = OptionPanel()\\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\\n\\n # Add widget to main layout\\n main_layout = self._window.main_layout\\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\\n\\n # Add page widget to stack\\n self._pages['item'] = ItemWidget()\\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\\n self._pages['text2'] = TextPage(text=STOP_TEXT)\\n\\n for index, name in enumerate(self._pages):\\n print('pages {} : {} page'.format(index, name))\\n self._window.widget_stack.addWidget(self._pages[name].widget)\\n\\n self._window.widget_stack.setCurrentIndex(0)\\n\\n # Build up signal / slot\\n self._option_panel.currentItemChanged.connect(self.set_page)\",\n \"def __init__(self, *args, **kwargs):\\r\\n if args:\\r\\n self.addonId = args[0]\\r\\n else:\\r\\n posIni = len('plugin://')\\r\\n posFin = sys.argv[0].find('/', posIni)\\r\\n addonId = sys.argv[0][posIni:posFin]\\r\\n self.addonId = kwargs.get('id',None) or addonId\",\n \"def create(self):\\n self.add_handlers({\\\"^T\\\": self.change_forms,\\\"^Q\\\": self.exit})\\n self.add(npyscreen.TitleFixedText, name='Inventory items:', value='')\\n self.inventory_mle = self.add(npyscreen.Pager,\\n values=['Checking for plugins in the inventory, please wait...'])\",\n \"def __init__(self, master=None):\\r\\n \\r\\n tk.Frame.__init__(self, master)\\r\\n self.master.title('AQUAMI')\\r\\n module_path, this_filename = os.path.split(__file__)\\r\\n try:\\r\\n self.master.iconbitmap(''.join((module_path, '/icon.ico')))\\r\\n except:\\r\\n pass\\r\\n\\r\\n self.initGUI()\",\n \"def create(self, parent):\\n self.widget = _QMainWindow(parent)\",\n \"def __init__(self):\\n self.label = \\\"DBT-2-DXF\\\"\\n self.description = \\\"da scrivere\\\"\\n self.canRunInBackground = False\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Hybriddekning/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Hybriddekning'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def __init__(self, vlc_player, drone_gui):\\n QMainWindow.__init__(self)\\n self.setWindowTitle(\\\"VLC Drone Video Player\\\")\\n\\n # save the media player\\n self.mediaplayer = vlc_player\\n\\n # need a reference to the main drone vision class\\n self.drone_vision = drone_gui\\n\\n # create the GUI\\n self.createUI()\",\n \"def __init__(self):\\n super().__init__()\\n # Main UI code goes here\\n lineEdit = qtw.QLineEdit(\\\"A Line edit!\\\", self)\\n label = qtw.QLabel(\\\"Hello Everybody\\\", self)\\n \\n # End main UI code\\n self.show()\",\n \"def __init__(self, controller):\\n global gui\\n print(\\\"Upload folder: \\\" + UPLOAD_FOLDER)\\n gui = self\\n self.controller = controller\\n try:\\n run()\\n finally:\\n self.controller.quit()\",\n \"def __init__(self, parent):\\n self.name = \\\"Base.View\\\"\\n self.parent = parent\\n self.Main = parent.Main\",\n \"def __init__(self, gui = None):\\n\\n QtCore.QThread.__init__(self)\\n self.gui = gui\\n\\n return None\",\n \"def __init__(self):\\n _hypre.HypreILU_swiginit(self, _hypre.new_HypreILU())\",\n \"def __init__(self):\\n self.model = self.load_model()\\n self.form_html = self.create_form_html()\",\n \"def __init__(self, *args):\\n this = _libsbml.new_CompSBasePlugin(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def create_submodule(self, *args: Any, **kwargs: Any) -> Submodule:\\n return Submodule.add(self, *args, **kwargs)\",\n \"def __init__(self, controller):\\n super().__init__(controller)\\n\\n # The hovered input when entering this View.\\n self.first_inp = \\\"s\\\"\\n\\n # Initialize selected variable.\\n self.selected = None\\n\\n # Make background graphics.\\n self.make_background_graphics()\\n\\n # Make Buttons.\\n self.make_buttons()\\n\\n # Make the information box. This explains each Button.\\n self.make_info_box()\\n\\n # Initializes popup.\\n self.make_popup()\\n\\n # Map of input to functions.\\n enter = self.graphics.ENTER_KEY\\n self.controls = {\\n # Pressing \\\"q\\\" will go back to the main menu.\\n \\\"q\\\": lambda: Action(\\\"goto main menu view\\\", []),\\n\\n # Movement keys.\\n \\\"w\\\": lambda: self.move_cursor(Direction.U),\\n \\\"a\\\": lambda: self.move_cursor(Direction.L),\\n \\\"s\\\": lambda: self.move_cursor(Direction.D),\\n \\\"d\\\": lambda: self.move_cursor(Direction.R),\\n\\n # Repeat the last valid input.\\n enter: self.repeat_last_valid_input,\\n\\n # Click the selected UIElement.\\n \\\"m\\\": self.click\\n }\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71629465","0.6812497","0.65096986","0.65096986","0.6493672","0.6486345","0.63511","0.63335776","0.62438047","0.62001234","0.6143851","0.6113456","0.60962325","0.60550797","0.6031183","0.60106504","0.5976868","0.5973087","0.59472775","0.59101176","0.58899575","0.58865273","0.5866452","0.58392423","0.58220094","0.58057153","0.57989323","0.5781516","0.57678896","0.5764244","0.5757002","0.57568467","0.5755934","0.5731385","0.5728821","0.57177484","0.57071984","0.5663977","0.5662925","0.5656067","0.56504285","0.5649472","0.5629112","0.56236404","0.5614723","0.5598098","0.55949146","0.55944705","0.5587896","0.5587184","0.55862135","0.55854064","0.5582007","0.5576374","0.557186","0.55700314","0.5567016","0.55622727","0.5556154","0.5553663","0.55508345","0.5550594","0.5544302","0.5538368","0.55145866","0.551424","0.5511341","0.55105567","0.55103016","0.54992926","0.54841024","0.54839027","0.54819375","0.54775125","0.54714006","0.5469788","0.54663706","0.5464437","0.54580104","0.54548675","0.5437491","0.5433511","0.54326254","0.54280186","0.54247326","0.5424213","0.54214734","0.5419595","0.5418093","0.5417688","0.5411518","0.5404528","0.5400381","0.5396812","0.53927493","0.53861195","0.5385397","0.5373551","0.53726286","0.53697664","0.53691584"],"string":"[\n \"0.71629465\",\n \"0.6812497\",\n \"0.65096986\",\n \"0.65096986\",\n \"0.6493672\",\n \"0.6486345\",\n \"0.63511\",\n \"0.63335776\",\n \"0.62438047\",\n \"0.62001234\",\n \"0.6143851\",\n \"0.6113456\",\n \"0.60962325\",\n \"0.60550797\",\n \"0.6031183\",\n \"0.60106504\",\n \"0.5976868\",\n \"0.5973087\",\n \"0.59472775\",\n \"0.59101176\",\n \"0.58899575\",\n \"0.58865273\",\n \"0.5866452\",\n \"0.58392423\",\n \"0.58220094\",\n \"0.58057153\",\n \"0.57989323\",\n \"0.5781516\",\n \"0.57678896\",\n \"0.5764244\",\n \"0.5757002\",\n \"0.57568467\",\n \"0.5755934\",\n \"0.5731385\",\n \"0.5728821\",\n \"0.57177484\",\n \"0.57071984\",\n \"0.5663977\",\n \"0.5662925\",\n \"0.5656067\",\n \"0.56504285\",\n \"0.5649472\",\n \"0.5629112\",\n \"0.56236404\",\n \"0.5614723\",\n \"0.5598098\",\n \"0.55949146\",\n \"0.55944705\",\n \"0.5587896\",\n \"0.5587184\",\n \"0.55862135\",\n \"0.55854064\",\n \"0.5582007\",\n \"0.5576374\",\n \"0.557186\",\n \"0.55700314\",\n \"0.5567016\",\n \"0.55622727\",\n \"0.5556154\",\n \"0.5553663\",\n \"0.55508345\",\n \"0.5550594\",\n \"0.5544302\",\n \"0.5538368\",\n \"0.55145866\",\n \"0.551424\",\n \"0.5511341\",\n \"0.55105567\",\n \"0.55103016\",\n \"0.54992926\",\n \"0.54841024\",\n \"0.54839027\",\n \"0.54819375\",\n \"0.54775125\",\n \"0.54714006\",\n \"0.5469788\",\n \"0.54663706\",\n \"0.5464437\",\n \"0.54580104\",\n \"0.54548675\",\n \"0.5437491\",\n \"0.5433511\",\n \"0.54326254\",\n \"0.54280186\",\n \"0.54247326\",\n \"0.5424213\",\n \"0.54214734\",\n \"0.5419595\",\n \"0.5418093\",\n \"0.5417688\",\n \"0.5411518\",\n \"0.5404528\",\n \"0.5400381\",\n \"0.5396812\",\n \"0.53927493\",\n \"0.53861195\",\n \"0.5385397\",\n \"0.5373551\",\n \"0.53726286\",\n \"0.53697664\",\n \"0.53691584\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94592,"cells":{"query":{"kind":"string","value":"Do the actual work of this submodule."},"document":{"kind":"string","value":"def run(self):\n super(COTInjectConfig, self).run()\n\n vm = self.vm\n\n platform = vm.platform\n\n # Find the disk drive where the config should be injected\n # First, look for any previously-injected config disk to overwrite:\n if platform.BOOTSTRAP_DISK_TYPE == 'cdrom':\n (f, d, ci, drive_device) = vm.search_from_filename('config.iso')\n elif platform.BOOTSTRAP_DISK_TYPE == 'harddisk':\n (f, d, ci, drive_device) = vm.search_from_filename('config.vmdk')\n else:\n raise ValueUnsupportedError(\"bootstrap disk type\",\n platform.BOOTSTRAP_DISK_TYPE,\n \"'cdrom' or 'harddisk'\")\n if f is not None:\n file_id = vm.get_id_from_file(f)\n self.UI.confirm_or_die(\n \"Existing configuration disk '{0}' found.\\n\"\n \"Continue and overwrite it?\".format(file_id))\n logger.warning(\"Overwriting existing config disk '{0}'\"\n .format(file_id))\n else:\n file_id = None\n # Find the empty slot where we should inject the config\n drive_device = vm.find_empty_drive(platform.BOOTSTRAP_DISK_TYPE)\n\n if drive_device is None:\n raise LookupError(\"Could not find an empty {0} drive to \"\n \"inject the config into\"\n .format(platform.BOOTSTRAP_DISK_TYPE))\n (cont_type, drive_address) = vm.find_device_location(drive_device)\n\n # Copy config file(s) to per-platform name in working directory\n config_files = []\n if self.config_file:\n dest = os.path.join(vm.working_dir, platform.CONFIG_TEXT_FILE)\n shutil.copy(self.config_file, dest)\n config_files.append(dest)\n if self.secondary_config_file:\n dest = os.path.join(vm.working_dir,\n platform.SECONDARY_CONFIG_TEXT_FILE)\n shutil.copy(self.secondary_config_file, dest)\n config_files.append(dest)\n\n # Package the config files into a disk image\n if platform.BOOTSTRAP_DISK_TYPE == 'cdrom':\n bootstrap_file = os.path.join(vm.working_dir, 'config.iso')\n create_disk_image(bootstrap_file, contents=config_files)\n elif platform.BOOTSTRAP_DISK_TYPE == 'harddisk':\n bootstrap_file = os.path.join(vm.working_dir, 'config.img')\n create_disk_image(bootstrap_file, file_format='raw',\n contents=config_files)\n else:\n raise ValueUnsupportedError(\"bootstrap disk type\",\n platform.BOOTSTRAP_DISK_TYPE,\n \"'cdrom' or 'harddisk'\")\n\n # Inject the disk image into the OVA, using \"add-disk\" functionality\n add_disk_worker(\n UI=self.UI,\n vm=vm,\n DISK_IMAGE=bootstrap_file,\n type=platform.BOOTSTRAP_DISK_TYPE,\n file_id=file_id,\n controller=cont_type,\n address=drive_address,\n subtype=None,\n description='Configuration disk',\n diskname=None,\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main():\n obj = UnityFilesystem()\n obj.perform_module_operation()","def __gitSubmodulesSync(self):\n self.vcs.gitSubmoduleSync(self.project.getProjectPath())","def do_workload(self):\n module_manager = self._core.get_module_manager()\n module = module_manager.get_module_by_name(self._values[\"name\"])\n module_manager.update_module(module)","def __gitSubmodulesInit(self):\n self.vcs.gitSubmoduleInit(self.project.getProjectPath())","def do_work(self):","def __gitSubmoduleAdd(self):\n self.vcs.gitSubmoduleAdd(self.project.getProjectPath())","def __gitSubmodulesUpdate(self):\n self.vcs.gitSubmoduleUpdate(self.project.getProjectPath())","def run(self):\n\t\t\n\t\tpass","def _run(self):\n raise NotImplementedError","def _run(self):\n raise NotImplementedError","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def run(self):\n pass","def RUN(self):","def do_work(self):\n raise NotImplementedError","def run(self):\r\n pass","def __gitSubmodulesUpdateInit(self):\n self.vcs.gitSubmoduleUpdate(self.project.getProjectPath(),\n initialize=True)","def do_workload(self):\n module_manager = self._core.get_module_manager()\n module_manager.install_module(self.get_meta())","def run(self):\n \n pass","def __gitSubmodulesDeinit(self):\n self.vcs.gitSubmoduleDeinit(self.project.getProjectPath())","def run(self):\n self.update_repos()","def run(self): \r\n return","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):","def run(self):\n self.run()","def execute_module(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError","def run(self):\n raise NotImplementedError # implement in subclass","def modules():","def run(self, root):\r\n pass","def process(self):\n pass","def Run():\r\n pass","def run(self):\n try:\n self._run_internal()\n finally:\n self._cleanup()","def main(self):\r\n pass","def _run(self):\n raise NotImplementedError","def setup( self ):","def do_manipulations(self, *args, **kwargs):\n pass","def run(self):\n raise NotImplementedError(\"Subclasses mut override run()\")","def __run(self):\n sys.settrace(self.globaltrace)\n self.__run_backup()\n self.run = self.__run_backup","def run(self):\n self._setupLogger()\n self.setup()\n\n self.logger.info(self.moduleName + \" starting run loop.\")\n\n while True:\n self.loop()","def run(self):\n raise NotImplementedError()","def run(self):\n raise NotImplementedError()","def setup(self):","def setup(self):","def setup(self):","def setup(self):","def __init__(self):\n ScriptedLoadableModuleLogic.__init__(self)","def do_build(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tcfg = self.cfg\n\t\tself.log('PHASE: build, repository work', level=logging.DEBUG)\n\t\tmodule_id_list = self.module_ids()\n\t\tif self.build['deps_only']:\n\t\t\tmodule_id_list_build_only = filter(lambda x: cfg[x]['shutit.core.module.build'], module_id_list)\n\t\tfor module_id in module_id_list:\n\t\t\tmodule = self.shutit_map[module_id]\n\t\t\tself.log('Considering whether to build: ' + module.module_id, level=logging.INFO)\n\t\t\tif cfg[module.module_id]['shutit.core.module.build']:\n\t\t\t\tif self.build['delivery'] not in module.ok_delivery_methods:\n\t\t\t\t\tself.fail('Module: ' + module.module_id + ' can only be built with one of these --delivery methods: ' + str(module.ok_delivery_methods) + '\\nSee shutit build -h for more info, or try adding: --delivery <method> to your shutit invocation') # pragma: no cover\n\t\t\t\tif self.is_installed(module):\n\t\t\t\t\tself.build['report'] = (self.build['report'] + '\\nBuilt already: ' + module.module_id + ' with run order: ' + str(module.run_order))\n\t\t\t\telse:\n\t\t\t\t\t# We move to the module directory to perform the build, returning immediately afterwards.\n\t\t\t\t\tif self.build['deps_only'] and module_id == module_id_list_build_only[-1]:\n\t\t\t\t\t\t# If this is the last module, and we are only building deps, stop here.\n\t\t\t\t\t\tself.build['report'] = (self.build['report'] + '\\nSkipping: ' + module.module_id + ' with run order: ' + str(module.run_order) + '\\n\\tas this is the final module and we are building dependencies only')\n\t\t\t\t\telse:\n\t\t\t\t\t\trevert_dir = os.getcwd()\n\t\t\t\t\t\tself.get_current_shutit_pexpect_session_environment().module_root_dir = os.path.dirname(self.shutit_file_map[module_id])\n\t\t\t\t\t\tself.chdir(self.get_current_shutit_pexpect_session_environment().module_root_dir)\n\t\t\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\t\t\tself.build_module(module)\n\t\t\t\t\t\tself.logout(echo=False)\n\t\t\t\t\t\tself.chdir(revert_dir)\n\t\t\tif self.is_installed(module):\n\t\t\t\tself.log('Starting module',level=logging.DEBUG)\n\t\t\t\tif not module.start(self):\n\t\t\t\t\tself.fail(module.module_id + ' failed on start', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover","def run(self):\n # Call to the method of the parent to\n # clean common files and directories\n Clean.run(self)\n\n # Remove C and C++ files if the current working directory\n # is not a source distribution, since the source files\n # are needed by the package in release mode\n cwd = abspath(dirname(__file__))\n remove_c_files = not exists(join(cwd, \"PKG-INFO\"))\n\n if exists(\"build\"):\n shutil.rmtree(\"build\")\n\n for (dirpath, dirnames, filenames) in walk(MODNAME):\n for filename in filenames:\n extension = splitext(filename)[1]\n if filename.endswith((\".so\", \".pyd\", \".dll\", \".pyc\")):\n unlink(join(dirpath, filename))\n elif remove_c_files and extension in {\".c\", \".cpp\"}:\n pyx_file = str.replace(filename, extension, \".pyx\")\n # Remove the C and C++ files only when they are\n # generated from a Cython extension, because in\n # any other case, they really correspond to the\n # source code\n if exists(join(dirpath, pyx_file)):\n unlink(join(dirpath, filename))\n for ddirname in dirnames:\n if ddirname in {\"__pycache__\"}:\n shutil.rmtree(join(dirpath, ddirname))","def process(self):","def process(self):","def process(self):","def run(self):\n\n # Run bom compare if selected\n if self.root.compare_select.get():\n print('Starting BOM Compare')\n self.ccl.set_bom_compare(self.root.bom_compare_old, self.root.bom_compare_new)\n self.ccl.save_compare(self.root.bom_compare_save)\n progressbar.add_current(1)\n print('BOM Compare finished')\n # Run CCL Update\n # Note that ccl update is ran again even if already run once, could be room for improvement\n if self.root.update_select.get():\n print('Starting to update the CCL')\n self.ccl.ccl_docx = self.root.ccl_update_loc\n self.ccl.set_bom_compare(self.root.ccl_update_old, self.root.ccl_update_new)\n self.ccl.update_ccl(self.root.ccl_update_save_loc)\n print('CCL Has been updated and saved')\n progressbar.add_current(1)\n # Collect documents\n if self.root.docs_select.get():\n print('Collecting Documents')\n self.ccl.ccl_docx = self.root.docs_ccl\n self.ccl.path_checks = self.root.docs_paths\n self.ccl.path_ccl_data = self.root.docs_savedir\n self.ccl.username = self.root.docs_user\n self.ccl.password = self.root.docs_pass\n self.ccl.collect_documents(headless=self.root.headless.get())\n print('Documents have been successfully collected')\n # Progressbar progress will be updated in the filehandler module\n # Collect documents\n if self.root.ills_select.get():\n print('Starting to Collect Illustrations')\n self.ccl.ccl_docx = self.root.ill_ccl\n self.ccl.path_ccl_data = self.root.ill_scan\n self.ccl.path_illustration = self.root.ill_save\n self.ccl.collect_illustrations()\n self.ccl.insert_illustration_data(self.root.ill_cclsave)\n print('Illustrations have been collected and CCL has been updated')\n # Progressbar progress will be updated in the CCL module\n # Progress bar final update after all process has finished\n self.progressbar['value'] = progressbar.total\n self.progress_label.config(text='Done')\n print('FINISHED!')","def _run(self):\n # We usually override this in __init__\n # pylint: disable=method-hidden\n return","def main():\n obj = PowerMaxVolume()\n obj.perform_module_operation()","def setup(self):\n\t\tpass","def __gitSubmodulesSummary(self):\n self.vcs.gitSubmoduleSummary(self.project.getProjectPath())","def run(self) -> None:\n log.critical('Not implemented')","def postRun(self):\n pass","def __gitSubmodulesStatus(self):\n self.vcs.gitSubmoduleStatus(self.project.getProjectPath())","def RunModule(self):\n module = self.statModules.GetActive()\n \n self.ShutdownStatModule()\n \n if module is not None:\n wx.LogMessage(\"Running stat module...\")\n self.statPage = module.Load(self.nb, self)\n self.SetView(self.statModules.caption, self.statPage)","def perform(self):\n pass","def _run(self, *args, **kwargs):\n raise NotImplementedError","def run_component(self):\n raise NotImplementedError","def main():\n ModLoader.add_constructor(\"!ec2rlcore.module.Module\", ModLoader.ignoretag)\n\n mod_src_dir = os.path.join(os.getcwd(), \"src\")\n try:\n os.stat(mod_src_dir)\n except Exception:\n os.mkdir(mod_src_dir)\n\n try:\n for mod_file_name in os.listdir(os.path.join(root_ec2rl_dir, \"mod.d\")):\n if mod_file_name == \"ex_remediation.yaml\":\n continue\n with open(os.path.join(root_ec2rl_dir, \"mod.d\", mod_file_name), \"r\") as yamlfile:\n module = yaml.load(yamlfile, Loader=ModLoader)\n if module[\"language\"] == \"python\":\n mod_src_path = os.path.join(mod_src_dir, \"{}.py\".format(module[\"name\"]))\n with open(mod_src_path, \"w\") as pyfile:\n pyfile.write(module[\"content\"])\n print(\"Wrote: {}\".format(mod_src_path))\n print(\"Conversion complete.\")\n except Exception as ex:\n print(ex)\n print(\"Conversion failed. Please review the exception to resolve\")","def do_one_mod(self, names: List[str], infer: bool, exec_: bool, conf: dict):\n\n p = lambda: Progress(\n TextColumn(\"[progress.description]{task.description}\", justify=\"right\"),\n BarColumn(bar_width=None),\n \"[progress.percentage]{task.percentage:>3.1f}%\",\n \"[progress.completed]{task.completed} / {task.total}\",\n TimeElapsedColumn(),\n )\n # step one collect all the modules instances we want to analyse.\n\n modules = []\n for name in names:\n x, *r = name.split(\".\")\n n0 = __import__(name)\n for sub in r:\n n0 = getattr(n0, sub)\n modules.append(n0)\n\n # step 2 try to guess the version number from the top module.\n version = getattr(modules[0], \"__version__\", \"???\")\n\n root = names[0].split(\".\")[0]\n module_conf = conf.get(root, {})\n examples_folder = module_conf.get(\"examples_folder\", None)\n print(\"EF\", examples_folder)\n if examples_folder is not None:\n examples_folder = Path(examples_folder).expanduser()\n examples_data = self.collect_examples(examples_folder)\n for edoc, figs in examples_data:\n self.examples.update(\n {k: json.dumps(v.to_json()) for k, v in edoc.items()}\n )\n for name, data in figs:\n print(\"put one fig\", name)\n self.put_raw(name, data)\n print(\"Configuration:\", json.dumps(module_conf, indent=2))\n self.root = root\n self.version = version\n subs = module_conf.get(\"submodules\", [])\n extra_from_conf = [root + \".\" + s for s in subs]\n for name in extra_from_conf:\n x, *r = name.split(\".\")\n n0 = __import__(name)\n for sub in r:\n n0 = getattr(n0, sub)\n modules.append(n0)\n\n # print(modules)\n\n collector = DFSCollector(modules[0], modules[1:])\n collected: Dict[str, Any] = collector.items()\n\n # collect all items we want to document.\n for qa, item in collected.items():\n if (nqa := full_qual(item)) != qa:\n print(\"after import qa differs : {qa} -> {nqa}\")\n if collected[nqa] == item:\n print(\"present twice\")\n del collected[nqa]\n else:\n print(\"differs: {item} != {other}\")\n\n for target in module_conf.get(\"exclude\", []):\n print(\"exclude tgt:\", target)\n del collected[target]\n # p = nullcontext\n with p() as p2:\n\n # just nice display of progression.\n taskp = p2.add_task(description=\"parsing\", total=len(collected))\n\n for qa, target_item in collected.items():\n short_description = (qa[:19] + \"..\") if len(qa) > 21 else qa\n p2.update(taskp, description=short_description.ljust(17))\n p2.advance(taskp)\n item_docstring = target_item.__doc__\n\n # TODO: we may not want tosip items as they may have children\n # right now keep modules, but we may want to keep classes if\n # they have documented descendants.\n\n if item_docstring is None and not isinstance(target_item, ModuleType):\n continue\n elif item_docstring is None and isinstance(target_item, ModuleType):\n item_docstring = \"\"\"This module has no documentation\"\"\"\n\n # progress.console.print(qa)\n try:\n if tsparse is None:\n print(\n \"please see how to install Tree-sitter in the readme to parse complex RST documents\"\n )\n arbitrary = tsparse(dedent_but_first(item_docstring).encode())\n except Exception as e:\n print(f\"TS could not parse: {qa}\")\n raise ValueError(f\"from {qa}\") from e\n arbitrary = []\n # raise\n try:\n ndoc = NumpyDocString(dedent_but_first(item_docstring))\n except Exception:\n if not isinstance(target_item, ModuleType):\n p2.console.print(\n \"Unexpected error parsing\",\n target_item,\n target_item.__name__,\n )\n if isinstance(target_item, ModuleType):\n # from .take2 import main\n # main(item_docstring)\n ndoc = NumpyDocString(\n f\"Was not able to parse docstring for {qa}\"\n )\n else:\n continue\n if not isinstance(target_item, ModuleType):\n arbitrary = []\n execute_exclude_patterns = module_conf.get(\n \"execute_exclude_patterns\", None\n )\n ex = exec_\n if execute_exclude_patterns and exec_:\n for pat in execute_exclude_patterns:\n if qa.startswith(pat):\n ex = False\n break\n # else:\n # print(\"will run\", qa)\n\n try:\n doc_blob, figs = self.do_one_item(\n target_item, ndoc, infer, ex, qa, config=module_conf\n )\n doc_blob.arbitrary = arbitrary\n except Exception:\n raise\n if module_conf.get(\"exec_failure\", None) == \"fallback\":\n print(\"Re-analysing \", qa, \"without execution\")\n # debug:\n doc_blob, figs = self.do_one_item(\n target_item, ndoc, infer, False, qa, config=module_conf\n )\n doc_blob.aliases = collector.aliases[qa]\n\n # processing....\n doc_blob.signature = doc_blob.content.pop(\"Signature\")\n try:\n for section in [\"Extended Summary\", \"Summary\", \"Notes\", \"Warnings\"]:\n if section in doc_blob.content:\n if data := doc_blob.content[section]:\n PX = P2(data)\n doc_blob.content[section] = Section(PX)\n else:\n doc_blob.content[section] = Section()\n except Exception as e:\n raise type(e)(f\"during {qa}\")\n\n doc_blob.references = doc_blob.content.pop(\"References\")\n if isinstance(doc_blob.references, str):\n if doc_blob.references == \"\":\n doc_blob.references = None\n else:\n assert False\n doc_blob.references = list(doc_blob.references)\n assert (\n isinstance(doc_blob.references, list) or doc_blob.references is None\n )\n del doc_blob.content[\"Examples\"]\n del doc_blob.content[\"index\"]\n sections_ = [\n \"Parameters\",\n \"Returns\",\n \"Raises\",\n \"Yields\",\n \"Attributes\",\n \"Other Parameters\",\n \"Warns\",\n ##\"Warnings\",\n \"Methods\",\n # \"Summary\",\n \"Receives\",\n ]\n from .take2 import Param\n\n # new_doc_blob._content[\"Parameters\"] = [\n # Parameter(a, b, c)\n # for (a, b, c) in new_doc_blob._content.get(\"Parameters\", [])\n # ]\n\n for s in sections_:\n if s in doc_blob.content:\n assert isinstance(\n doc_blob.content[s], list\n ), f\"{s}, {doc_blob.content[s]} \"\n new_content = Section()\n for param, type_, desc in doc_blob.content[s]:\n assert isinstance(desc, list)\n items = []\n if desc:\n items = P2(desc)\n new_content.append(Param(param, type_, items))\n doc_blob.content[s] = new_content\n\n doc_blob.see_also = []\n if see_also := doc_blob.content.get(\"See Also\", None):\n for nts, d0 in see_also:\n try:\n d = d0\n for (name, type_or_description) in nts:\n if type_or_description and not d:\n desc = type_or_description\n if isinstance(desc, str):\n desc = [desc]\n assert isinstance(desc, list)\n desc = paragraphs(desc)\n type_ = None\n else:\n desc = d0\n type_ = type_or_description\n assert isinstance(desc, list)\n desc = paragraphs(desc)\n\n sai = SeeAlsoItem(Ref(name, None, None), desc, type_)\n doc_blob.see_also.append(sai)\n del desc\n del type_\n except Exception as e:\n raise ValueError(\n f\"Error {qa}: {see_also=} | {nts=} | {d0=}\"\n ) from e\n del doc_blob.content[\"See Also\"]\n\n for k, v in doc_blob.content.items():\n assert isinstance(v, Section), f\"{k} is not a section {v}\"\n # end processing\n\n self.put(qa, json.dumps(doc_blob.to_json(), indent=2))\n for name, data in figs:\n self.put_raw(name, data)\n\n found = {}\n not_found = []\n for k, v in collector.aliases.items():\n if [item for item in v if item != k]:\n if shorter := find_cannonical(k, v):\n found[k] = shorter\n else:\n not_found.append((k, v))\n\n if logo := module_conf.get(\"logo\", None):\n self.put_raw(\"logo.png\", Path(logo).read_bytes())\n self.metadata = {\n \"version\": version,\n \"logo\": \"logo.png\",\n \"aliases\": found,\n \"module\": root,\n }","def run(self) -> None:\n raise NotImplementedError()","def run(self):\n self._make_lib_file_symbolic_links()\n self._copy_each_include_files_to_include_dir()\n self._make_dep_lib_file_sym_links_and_copy_include_files()\n self.setup_py.add_patchs_to_build_without_pkg_config(\n self.rpm.lib_dir, self.rpm.include_dir\n )\n self.setup_py.apply_and_save()\n self._build_and_install()","def run():\n from cgl.plugins.blender.tasks.rig import parent_mdl_to_rig\n parent_mdl_to_rig()","def run(self):\n self.cmdloop()","def exec_module(self, module):\n pass","def run_game_logic(self):\n pass","def pull_submodules():\n subprocess.call([\"git\", \"submodule\", \"init\"])\n subprocess.call([\"git\", \"submodule\", \"update\", \"--recursive\", \"--remote\"])"],"string":"[\n \"def main():\\n obj = UnityFilesystem()\\n obj.perform_module_operation()\",\n \"def __gitSubmodulesSync(self):\\n self.vcs.gitSubmoduleSync(self.project.getProjectPath())\",\n \"def do_workload(self):\\n module_manager = self._core.get_module_manager()\\n module = module_manager.get_module_by_name(self._values[\\\"name\\\"])\\n module_manager.update_module(module)\",\n \"def __gitSubmodulesInit(self):\\n self.vcs.gitSubmoduleInit(self.project.getProjectPath())\",\n \"def do_work(self):\",\n \"def __gitSubmoduleAdd(self):\\n self.vcs.gitSubmoduleAdd(self.project.getProjectPath())\",\n \"def __gitSubmodulesUpdate(self):\\n self.vcs.gitSubmoduleUpdate(self.project.getProjectPath())\",\n \"def run(self):\\n\\t\\t\\n\\t\\tpass\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def run(self):\\n pass\",\n \"def RUN(self):\",\n \"def do_work(self):\\n raise NotImplementedError\",\n \"def run(self):\\r\\n pass\",\n \"def __gitSubmodulesUpdateInit(self):\\n self.vcs.gitSubmoduleUpdate(self.project.getProjectPath(),\\n initialize=True)\",\n \"def do_workload(self):\\n module_manager = self._core.get_module_manager()\\n module_manager.install_module(self.get_meta())\",\n \"def run(self):\\n \\n pass\",\n \"def __gitSubmodulesDeinit(self):\\n self.vcs.gitSubmoduleDeinit(self.project.getProjectPath())\",\n \"def run(self):\\n self.update_repos()\",\n \"def run(self): \\r\\n return\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\",\n \"def run(self):\\n self.run()\",\n \"def execute_module(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError # implement in subclass\",\n \"def modules():\",\n \"def run(self, root):\\r\\n pass\",\n \"def process(self):\\n pass\",\n \"def Run():\\r\\n pass\",\n \"def run(self):\\n try:\\n self._run_internal()\\n finally:\\n self._cleanup()\",\n \"def main(self):\\r\\n pass\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def setup( self ):\",\n \"def do_manipulations(self, *args, **kwargs):\\n pass\",\n \"def run(self):\\n raise NotImplementedError(\\\"Subclasses mut override run()\\\")\",\n \"def __run(self):\\n sys.settrace(self.globaltrace)\\n self.__run_backup()\\n self.run = self.__run_backup\",\n \"def run(self):\\n self._setupLogger()\\n self.setup()\\n\\n self.logger.info(self.moduleName + \\\" starting run loop.\\\")\\n\\n while True:\\n self.loop()\",\n \"def run(self):\\n raise NotImplementedError()\",\n \"def run(self):\\n raise NotImplementedError()\",\n \"def setup(self):\",\n \"def setup(self):\",\n \"def setup(self):\",\n \"def setup(self):\",\n \"def __init__(self):\\n ScriptedLoadableModuleLogic.__init__(self)\",\n \"def do_build(self):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tcfg = self.cfg\\n\\t\\tself.log('PHASE: build, repository work', level=logging.DEBUG)\\n\\t\\tmodule_id_list = self.module_ids()\\n\\t\\tif self.build['deps_only']:\\n\\t\\t\\tmodule_id_list_build_only = filter(lambda x: cfg[x]['shutit.core.module.build'], module_id_list)\\n\\t\\tfor module_id in module_id_list:\\n\\t\\t\\tmodule = self.shutit_map[module_id]\\n\\t\\t\\tself.log('Considering whether to build: ' + module.module_id, level=logging.INFO)\\n\\t\\t\\tif cfg[module.module_id]['shutit.core.module.build']:\\n\\t\\t\\t\\tif self.build['delivery'] not in module.ok_delivery_methods:\\n\\t\\t\\t\\t\\tself.fail('Module: ' + module.module_id + ' can only be built with one of these --delivery methods: ' + str(module.ok_delivery_methods) + '\\\\nSee shutit build -h for more info, or try adding: --delivery <method> to your shutit invocation') # pragma: no cover\\n\\t\\t\\t\\tif self.is_installed(module):\\n\\t\\t\\t\\t\\tself.build['report'] = (self.build['report'] + '\\\\nBuilt already: ' + module.module_id + ' with run order: ' + str(module.run_order))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# We move to the module directory to perform the build, returning immediately afterwards.\\n\\t\\t\\t\\t\\tif self.build['deps_only'] and module_id == module_id_list_build_only[-1]:\\n\\t\\t\\t\\t\\t\\t# If this is the last module, and we are only building deps, stop here.\\n\\t\\t\\t\\t\\t\\tself.build['report'] = (self.build['report'] + '\\\\nSkipping: ' + module.module_id + ' with run order: ' + str(module.run_order) + '\\\\n\\\\tas this is the final module and we are building dependencies only')\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\trevert_dir = os.getcwd()\\n\\t\\t\\t\\t\\t\\tself.get_current_shutit_pexpect_session_environment().module_root_dir = os.path.dirname(self.shutit_file_map[module_id])\\n\\t\\t\\t\\t\\t\\tself.chdir(self.get_current_shutit_pexpect_session_environment().module_root_dir)\\n\\t\\t\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\t\\t\\tself.build_module(module)\\n\\t\\t\\t\\t\\t\\tself.logout(echo=False)\\n\\t\\t\\t\\t\\t\\tself.chdir(revert_dir)\\n\\t\\t\\tif self.is_installed(module):\\n\\t\\t\\t\\tself.log('Starting module',level=logging.DEBUG)\\n\\t\\t\\t\\tif not module.start(self):\\n\\t\\t\\t\\t\\tself.fail(module.module_id + ' failed on start', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\",\n \"def run(self):\\n # Call to the method of the parent to\\n # clean common files and directories\\n Clean.run(self)\\n\\n # Remove C and C++ files if the current working directory\\n # is not a source distribution, since the source files\\n # are needed by the package in release mode\\n cwd = abspath(dirname(__file__))\\n remove_c_files = not exists(join(cwd, \\\"PKG-INFO\\\"))\\n\\n if exists(\\\"build\\\"):\\n shutil.rmtree(\\\"build\\\")\\n\\n for (dirpath, dirnames, filenames) in walk(MODNAME):\\n for filename in filenames:\\n extension = splitext(filename)[1]\\n if filename.endswith((\\\".so\\\", \\\".pyd\\\", \\\".dll\\\", \\\".pyc\\\")):\\n unlink(join(dirpath, filename))\\n elif remove_c_files and extension in {\\\".c\\\", \\\".cpp\\\"}:\\n pyx_file = str.replace(filename, extension, \\\".pyx\\\")\\n # Remove the C and C++ files only when they are\\n # generated from a Cython extension, because in\\n # any other case, they really correspond to the\\n # source code\\n if exists(join(dirpath, pyx_file)):\\n unlink(join(dirpath, filename))\\n for ddirname in dirnames:\\n if ddirname in {\\\"__pycache__\\\"}:\\n shutil.rmtree(join(dirpath, ddirname))\",\n \"def process(self):\",\n \"def process(self):\",\n \"def process(self):\",\n \"def run(self):\\n\\n # Run bom compare if selected\\n if self.root.compare_select.get():\\n print('Starting BOM Compare')\\n self.ccl.set_bom_compare(self.root.bom_compare_old, self.root.bom_compare_new)\\n self.ccl.save_compare(self.root.bom_compare_save)\\n progressbar.add_current(1)\\n print('BOM Compare finished')\\n # Run CCL Update\\n # Note that ccl update is ran again even if already run once, could be room for improvement\\n if self.root.update_select.get():\\n print('Starting to update the CCL')\\n self.ccl.ccl_docx = self.root.ccl_update_loc\\n self.ccl.set_bom_compare(self.root.ccl_update_old, self.root.ccl_update_new)\\n self.ccl.update_ccl(self.root.ccl_update_save_loc)\\n print('CCL Has been updated and saved')\\n progressbar.add_current(1)\\n # Collect documents\\n if self.root.docs_select.get():\\n print('Collecting Documents')\\n self.ccl.ccl_docx = self.root.docs_ccl\\n self.ccl.path_checks = self.root.docs_paths\\n self.ccl.path_ccl_data = self.root.docs_savedir\\n self.ccl.username = self.root.docs_user\\n self.ccl.password = self.root.docs_pass\\n self.ccl.collect_documents(headless=self.root.headless.get())\\n print('Documents have been successfully collected')\\n # Progressbar progress will be updated in the filehandler module\\n # Collect documents\\n if self.root.ills_select.get():\\n print('Starting to Collect Illustrations')\\n self.ccl.ccl_docx = self.root.ill_ccl\\n self.ccl.path_ccl_data = self.root.ill_scan\\n self.ccl.path_illustration = self.root.ill_save\\n self.ccl.collect_illustrations()\\n self.ccl.insert_illustration_data(self.root.ill_cclsave)\\n print('Illustrations have been collected and CCL has been updated')\\n # Progressbar progress will be updated in the CCL module\\n # Progress bar final update after all process has finished\\n self.progressbar['value'] = progressbar.total\\n self.progress_label.config(text='Done')\\n print('FINISHED!')\",\n \"def _run(self):\\n # We usually override this in __init__\\n # pylint: disable=method-hidden\\n return\",\n \"def main():\\n obj = PowerMaxVolume()\\n obj.perform_module_operation()\",\n \"def setup(self):\\n\\t\\tpass\",\n \"def __gitSubmodulesSummary(self):\\n self.vcs.gitSubmoduleSummary(self.project.getProjectPath())\",\n \"def run(self) -> None:\\n log.critical('Not implemented')\",\n \"def postRun(self):\\n pass\",\n \"def __gitSubmodulesStatus(self):\\n self.vcs.gitSubmoduleStatus(self.project.getProjectPath())\",\n \"def RunModule(self):\\n module = self.statModules.GetActive()\\n \\n self.ShutdownStatModule()\\n \\n if module is not None:\\n wx.LogMessage(\\\"Running stat module...\\\")\\n self.statPage = module.Load(self.nb, self)\\n self.SetView(self.statModules.caption, self.statPage)\",\n \"def perform(self):\\n pass\",\n \"def _run(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def run_component(self):\\n raise NotImplementedError\",\n \"def main():\\n ModLoader.add_constructor(\\\"!ec2rlcore.module.Module\\\", ModLoader.ignoretag)\\n\\n mod_src_dir = os.path.join(os.getcwd(), \\\"src\\\")\\n try:\\n os.stat(mod_src_dir)\\n except Exception:\\n os.mkdir(mod_src_dir)\\n\\n try:\\n for mod_file_name in os.listdir(os.path.join(root_ec2rl_dir, \\\"mod.d\\\")):\\n if mod_file_name == \\\"ex_remediation.yaml\\\":\\n continue\\n with open(os.path.join(root_ec2rl_dir, \\\"mod.d\\\", mod_file_name), \\\"r\\\") as yamlfile:\\n module = yaml.load(yamlfile, Loader=ModLoader)\\n if module[\\\"language\\\"] == \\\"python\\\":\\n mod_src_path = os.path.join(mod_src_dir, \\\"{}.py\\\".format(module[\\\"name\\\"]))\\n with open(mod_src_path, \\\"w\\\") as pyfile:\\n pyfile.write(module[\\\"content\\\"])\\n print(\\\"Wrote: {}\\\".format(mod_src_path))\\n print(\\\"Conversion complete.\\\")\\n except Exception as ex:\\n print(ex)\\n print(\\\"Conversion failed. Please review the exception to resolve\\\")\",\n \"def do_one_mod(self, names: List[str], infer: bool, exec_: bool, conf: dict):\\n\\n p = lambda: Progress(\\n TextColumn(\\\"[progress.description]{task.description}\\\", justify=\\\"right\\\"),\\n BarColumn(bar_width=None),\\n \\\"[progress.percentage]{task.percentage:>3.1f}%\\\",\\n \\\"[progress.completed]{task.completed} / {task.total}\\\",\\n TimeElapsedColumn(),\\n )\\n # step one collect all the modules instances we want to analyse.\\n\\n modules = []\\n for name in names:\\n x, *r = name.split(\\\".\\\")\\n n0 = __import__(name)\\n for sub in r:\\n n0 = getattr(n0, sub)\\n modules.append(n0)\\n\\n # step 2 try to guess the version number from the top module.\\n version = getattr(modules[0], \\\"__version__\\\", \\\"???\\\")\\n\\n root = names[0].split(\\\".\\\")[0]\\n module_conf = conf.get(root, {})\\n examples_folder = module_conf.get(\\\"examples_folder\\\", None)\\n print(\\\"EF\\\", examples_folder)\\n if examples_folder is not None:\\n examples_folder = Path(examples_folder).expanduser()\\n examples_data = self.collect_examples(examples_folder)\\n for edoc, figs in examples_data:\\n self.examples.update(\\n {k: json.dumps(v.to_json()) for k, v in edoc.items()}\\n )\\n for name, data in figs:\\n print(\\\"put one fig\\\", name)\\n self.put_raw(name, data)\\n print(\\\"Configuration:\\\", json.dumps(module_conf, indent=2))\\n self.root = root\\n self.version = version\\n subs = module_conf.get(\\\"submodules\\\", [])\\n extra_from_conf = [root + \\\".\\\" + s for s in subs]\\n for name in extra_from_conf:\\n x, *r = name.split(\\\".\\\")\\n n0 = __import__(name)\\n for sub in r:\\n n0 = getattr(n0, sub)\\n modules.append(n0)\\n\\n # print(modules)\\n\\n collector = DFSCollector(modules[0], modules[1:])\\n collected: Dict[str, Any] = collector.items()\\n\\n # collect all items we want to document.\\n for qa, item in collected.items():\\n if (nqa := full_qual(item)) != qa:\\n print(\\\"after import qa differs : {qa} -> {nqa}\\\")\\n if collected[nqa] == item:\\n print(\\\"present twice\\\")\\n del collected[nqa]\\n else:\\n print(\\\"differs: {item} != {other}\\\")\\n\\n for target in module_conf.get(\\\"exclude\\\", []):\\n print(\\\"exclude tgt:\\\", target)\\n del collected[target]\\n # p = nullcontext\\n with p() as p2:\\n\\n # just nice display of progression.\\n taskp = p2.add_task(description=\\\"parsing\\\", total=len(collected))\\n\\n for qa, target_item in collected.items():\\n short_description = (qa[:19] + \\\"..\\\") if len(qa) > 21 else qa\\n p2.update(taskp, description=short_description.ljust(17))\\n p2.advance(taskp)\\n item_docstring = target_item.__doc__\\n\\n # TODO: we may not want tosip items as they may have children\\n # right now keep modules, but we may want to keep classes if\\n # they have documented descendants.\\n\\n if item_docstring is None and not isinstance(target_item, ModuleType):\\n continue\\n elif item_docstring is None and isinstance(target_item, ModuleType):\\n item_docstring = \\\"\\\"\\\"This module has no documentation\\\"\\\"\\\"\\n\\n # progress.console.print(qa)\\n try:\\n if tsparse is None:\\n print(\\n \\\"please see how to install Tree-sitter in the readme to parse complex RST documents\\\"\\n )\\n arbitrary = tsparse(dedent_but_first(item_docstring).encode())\\n except Exception as e:\\n print(f\\\"TS could not parse: {qa}\\\")\\n raise ValueError(f\\\"from {qa}\\\") from e\\n arbitrary = []\\n # raise\\n try:\\n ndoc = NumpyDocString(dedent_but_first(item_docstring))\\n except Exception:\\n if not isinstance(target_item, ModuleType):\\n p2.console.print(\\n \\\"Unexpected error parsing\\\",\\n target_item,\\n target_item.__name__,\\n )\\n if isinstance(target_item, ModuleType):\\n # from .take2 import main\\n # main(item_docstring)\\n ndoc = NumpyDocString(\\n f\\\"Was not able to parse docstring for {qa}\\\"\\n )\\n else:\\n continue\\n if not isinstance(target_item, ModuleType):\\n arbitrary = []\\n execute_exclude_patterns = module_conf.get(\\n \\\"execute_exclude_patterns\\\", None\\n )\\n ex = exec_\\n if execute_exclude_patterns and exec_:\\n for pat in execute_exclude_patterns:\\n if qa.startswith(pat):\\n ex = False\\n break\\n # else:\\n # print(\\\"will run\\\", qa)\\n\\n try:\\n doc_blob, figs = self.do_one_item(\\n target_item, ndoc, infer, ex, qa, config=module_conf\\n )\\n doc_blob.arbitrary = arbitrary\\n except Exception:\\n raise\\n if module_conf.get(\\\"exec_failure\\\", None) == \\\"fallback\\\":\\n print(\\\"Re-analysing \\\", qa, \\\"without execution\\\")\\n # debug:\\n doc_blob, figs = self.do_one_item(\\n target_item, ndoc, infer, False, qa, config=module_conf\\n )\\n doc_blob.aliases = collector.aliases[qa]\\n\\n # processing....\\n doc_blob.signature = doc_blob.content.pop(\\\"Signature\\\")\\n try:\\n for section in [\\\"Extended Summary\\\", \\\"Summary\\\", \\\"Notes\\\", \\\"Warnings\\\"]:\\n if section in doc_blob.content:\\n if data := doc_blob.content[section]:\\n PX = P2(data)\\n doc_blob.content[section] = Section(PX)\\n else:\\n doc_blob.content[section] = Section()\\n except Exception as e:\\n raise type(e)(f\\\"during {qa}\\\")\\n\\n doc_blob.references = doc_blob.content.pop(\\\"References\\\")\\n if isinstance(doc_blob.references, str):\\n if doc_blob.references == \\\"\\\":\\n doc_blob.references = None\\n else:\\n assert False\\n doc_blob.references = list(doc_blob.references)\\n assert (\\n isinstance(doc_blob.references, list) or doc_blob.references is None\\n )\\n del doc_blob.content[\\\"Examples\\\"]\\n del doc_blob.content[\\\"index\\\"]\\n sections_ = [\\n \\\"Parameters\\\",\\n \\\"Returns\\\",\\n \\\"Raises\\\",\\n \\\"Yields\\\",\\n \\\"Attributes\\\",\\n \\\"Other Parameters\\\",\\n \\\"Warns\\\",\\n ##\\\"Warnings\\\",\\n \\\"Methods\\\",\\n # \\\"Summary\\\",\\n \\\"Receives\\\",\\n ]\\n from .take2 import Param\\n\\n # new_doc_blob._content[\\\"Parameters\\\"] = [\\n # Parameter(a, b, c)\\n # for (a, b, c) in new_doc_blob._content.get(\\\"Parameters\\\", [])\\n # ]\\n\\n for s in sections_:\\n if s in doc_blob.content:\\n assert isinstance(\\n doc_blob.content[s], list\\n ), f\\\"{s}, {doc_blob.content[s]} \\\"\\n new_content = Section()\\n for param, type_, desc in doc_blob.content[s]:\\n assert isinstance(desc, list)\\n items = []\\n if desc:\\n items = P2(desc)\\n new_content.append(Param(param, type_, items))\\n doc_blob.content[s] = new_content\\n\\n doc_blob.see_also = []\\n if see_also := doc_blob.content.get(\\\"See Also\\\", None):\\n for nts, d0 in see_also:\\n try:\\n d = d0\\n for (name, type_or_description) in nts:\\n if type_or_description and not d:\\n desc = type_or_description\\n if isinstance(desc, str):\\n desc = [desc]\\n assert isinstance(desc, list)\\n desc = paragraphs(desc)\\n type_ = None\\n else:\\n desc = d0\\n type_ = type_or_description\\n assert isinstance(desc, list)\\n desc = paragraphs(desc)\\n\\n sai = SeeAlsoItem(Ref(name, None, None), desc, type_)\\n doc_blob.see_also.append(sai)\\n del desc\\n del type_\\n except Exception as e:\\n raise ValueError(\\n f\\\"Error {qa}: {see_also=} | {nts=} | {d0=}\\\"\\n ) from e\\n del doc_blob.content[\\\"See Also\\\"]\\n\\n for k, v in doc_blob.content.items():\\n assert isinstance(v, Section), f\\\"{k} is not a section {v}\\\"\\n # end processing\\n\\n self.put(qa, json.dumps(doc_blob.to_json(), indent=2))\\n for name, data in figs:\\n self.put_raw(name, data)\\n\\n found = {}\\n not_found = []\\n for k, v in collector.aliases.items():\\n if [item for item in v if item != k]:\\n if shorter := find_cannonical(k, v):\\n found[k] = shorter\\n else:\\n not_found.append((k, v))\\n\\n if logo := module_conf.get(\\\"logo\\\", None):\\n self.put_raw(\\\"logo.png\\\", Path(logo).read_bytes())\\n self.metadata = {\\n \\\"version\\\": version,\\n \\\"logo\\\": \\\"logo.png\\\",\\n \\\"aliases\\\": found,\\n \\\"module\\\": root,\\n }\",\n \"def run(self) -> None:\\n raise NotImplementedError()\",\n \"def run(self):\\n self._make_lib_file_symbolic_links()\\n self._copy_each_include_files_to_include_dir()\\n self._make_dep_lib_file_sym_links_and_copy_include_files()\\n self.setup_py.add_patchs_to_build_without_pkg_config(\\n self.rpm.lib_dir, self.rpm.include_dir\\n )\\n self.setup_py.apply_and_save()\\n self._build_and_install()\",\n \"def run():\\n from cgl.plugins.blender.tasks.rig import parent_mdl_to_rig\\n parent_mdl_to_rig()\",\n \"def run(self):\\n self.cmdloop()\",\n \"def exec_module(self, module):\\n pass\",\n \"def run_game_logic(self):\\n pass\",\n \"def pull_submodules():\\n subprocess.call([\\\"git\\\", \\\"submodule\\\", \\\"init\\\"])\\n subprocess.call([\\\"git\\\", \\\"submodule\\\", \\\"update\\\", \\\"--recursive\\\", \\\"--remote\\\"])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66442853","0.62968755","0.6259206","0.624318","0.6181432","0.61801285","0.6152305","0.6148519","0.61186683","0.61186683","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.6116955","0.61045635","0.60868275","0.6016773","0.5894232","0.5880135","0.5877545","0.5868885","0.5857526","0.5855148","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58546335","0.58535993","0.5834693","0.5826229","0.5826229","0.5826229","0.5826229","0.5826229","0.5826229","0.5826229","0.5826229","0.5826229","0.58254987","0.57999045","0.575067","0.5750185","0.5740732","0.5718271","0.57014865","0.56973267","0.5694623","0.5682289","0.56673723","0.5665367","0.5646514","0.5642024","0.5642024","0.56408477","0.56408477","0.56408477","0.56408477","0.5632304","0.56014806","0.55977154","0.5591665","0.5591665","0.5591665","0.55913174","0.55720204","0.5568205","0.5564147","0.5557803","0.55571955","0.55530304","0.55515504","0.5547814","0.5538676","0.5537759","0.552871","0.5511285","0.55110115","0.5509438","0.5496187","0.5492567","0.548794","0.54527664","0.54464006","0.544595"],"string":"[\n \"0.66442853\",\n \"0.62968755\",\n \"0.6259206\",\n \"0.624318\",\n \"0.6181432\",\n \"0.61801285\",\n \"0.6152305\",\n \"0.6148519\",\n \"0.61186683\",\n \"0.61186683\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.6116955\",\n \"0.61045635\",\n \"0.60868275\",\n \"0.6016773\",\n \"0.5894232\",\n \"0.5880135\",\n \"0.5877545\",\n \"0.5868885\",\n \"0.5857526\",\n \"0.5855148\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58546335\",\n \"0.58535993\",\n \"0.5834693\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.5826229\",\n \"0.58254987\",\n \"0.57999045\",\n \"0.575067\",\n \"0.5750185\",\n \"0.5740732\",\n \"0.5718271\",\n \"0.57014865\",\n \"0.56973267\",\n \"0.5694623\",\n \"0.5682289\",\n \"0.56673723\",\n \"0.5665367\",\n \"0.5646514\",\n \"0.5642024\",\n \"0.5642024\",\n \"0.56408477\",\n \"0.56408477\",\n \"0.56408477\",\n \"0.56408477\",\n \"0.5632304\",\n \"0.56014806\",\n \"0.55977154\",\n \"0.5591665\",\n \"0.5591665\",\n \"0.5591665\",\n \"0.55913174\",\n \"0.55720204\",\n \"0.5568205\",\n \"0.5564147\",\n \"0.5557803\",\n \"0.55571955\",\n \"0.55530304\",\n \"0.55515504\",\n \"0.5547814\",\n \"0.5538676\",\n \"0.5537759\",\n \"0.552871\",\n \"0.5511285\",\n \"0.55110115\",\n \"0.5509438\",\n \"0.5496187\",\n \"0.5492567\",\n \"0.548794\",\n \"0.54527664\",\n \"0.54464006\",\n \"0.544595\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94593,"cells":{"query":{"kind":"string","value":"Add subparser for the CLI of this submodule."},"document":{"kind":"string","value":"def create_subparser(self, parent, storage):\n p = parent.add_parser(\n 'inject-config',\n help=\"Inject a configuration file into an OVF package\",\n usage=self.UI.fill_usage(\"inject-config\", [\n \"PACKAGE -c CONFIG_FILE [-o OUTPUT]\",\n \"PACKAGE -s SECONDARY_CONFIG_FILE [-o OUTPUT]\",\n \"PACKAGE -c CONFIG_FILE -s SECONDARY_CONFIG_FILE [-o OUTPUT]\",\n ]),\n description=\"\"\"Add one or more \"bootstrap\" configuration \"\"\"\n \"\"\"file(s) to the given OVF or OVA.\"\"\")\n\n p.add_argument('-o', '--output',\n help=\"\"\"Name/path of new VM package to create \"\"\"\n \"\"\"instead of updating the existing package\"\"\")\n\n p.add_argument('-c', '--config-file',\n help=\"\"\"Primary configuration text file to embed\"\"\")\n p.add_argument('-s', '--secondary-config-file',\n help=\"\"\"Secondary configuration text file to embed \"\"\"\n \"\"\"(currently only supported in IOS XRv for \"\"\"\n \"\"\"admin config)\"\"\")\n p.add_argument('PACKAGE',\n help=\"\"\"Package, OVF descriptor or OVA file to edit\"\"\")\n p.set_defaults(instance=self)\n\n storage['inject-config'] = p"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extend_cli(self, subparser):","def add(cls, subparsers):\n subparser = subparsers.add_parser(\n name=cls.__tool_name__(),\n description=cls.__get_description__())\n\n cls.__add_arguments__(subparser)\n subparser.set_defaults(func=cls.from_args)\n return subparser","def refine_cli(\n subparsers: SubParsersAction,\n parent_parsers: List[argparse.ArgumentParser],\n) -> None:","def add_subcommand(\n subp: _SubParsersAction, name: str, description: Optional[str] = None, **kwargs\n ) -> \"CommandParser\":\n desc_help = {\"description\": description, \"help\": description}\n return subp.add_parser(name, **desc_help, **kwargs)","def add_argparse_subparser(subparsers):\n\n new_sub_parser = subparsers.add_parser(\n PluginManager.argparse_subparser_name(), help=\"plugin commands\"\n )\n PluginManager.__argparse_subparser = new_sub_parser\n plugin_subparsers = new_sub_parser.add_subparsers(\n dest=PluginManager.__root_subparser_name\n )\n\n sub_sub_parser = plugin_subparsers.add_parser(\n \"list\", help=\"list the available plugins\"\n )\n sub_sub_parser.add_argument(\n \"--all\",\n dest=\"show_all\",\n action=\"store_true\",\n default=False,\n help=\"show all loaded plugins (default is False)\",\n )\n sub_sub_parser.add_argument(\n dest=\"list_filter\",\n default=None,\n help=\"filter\",\n nargs=\"?\",\n type=PluginManager.__list_filter_type,\n )\n sub_sub_parser = plugin_subparsers.add_parser(\n \"info\", help=\"information on a specific plugin\"\n )\n sub_sub_parser.add_argument(\n dest=\"info_filter\",\n default=None,\n type=PluginManager.__info_filter_type,\n help=\"an id\",\n )","def extend_cli(self, root_subparsers):\n\n user_dict = {}\n if self.add_base_groups:\n user_dict = dict(shared_groups=SHARED_GROUPS)\n\n self.specification = SpecParser.from_plugin(\n subparser=root_subparsers,\n plugin=self.plugin,\n base_groups=user_dict)","def _add_to_cli(self, parser, group=None):\n dest = self.dest\n if group is not None:\n dest = group.name + '_' + dest\n\n subparsers = parser.add_subparsers(dest=dest,\n title=self.title,\n description=self.description,\n help=self.help)\n # NOTE(jd) Set explicitly to True for Python 3\n # See http://bugs.python.org/issue9253 for context\n subparsers.required = True\n\n if self.handler is not None:\n self.handler(subparsers)","def addSubParser( parentParser, cmdName ) :\n parser = parentParser.add_parser( cmdName, help='Generate a new UBOS package scaffold.' )\n parser.add_argument( '--directory', required=True, help='Directory where to create the package scaffold')\n parser.add_argument( '--template', required=True, help='Name of the template to use' )\n parser.add_argument( '--json', required=False, help='Settings file' )","def add_subparser(sp, name, **kwargs):\n kwargs[\"add_help\"] = False\n kwargs['formatter_class'] = ap.ArgumentDefaultsHelpFormatter\n sparser = sp.add_parser(name, **kwargs)\n\n sparser.add_argument(\"-h\", \"--help\", action=custom_help(),\n help=\"print the short or long help\")\n\n return sparser","def add_cli(self, subparser):\n new_parser = subparser.add_parser('create', help='create new scratch file')\n new_parser.add_argument('name', nargs='?', default=None, help=\"Optional Name to be given to the file, \"\n \"default name is an increment of 'scratch##'\")\n new_parser.set_defaults(func=self.action)\n return subparser","def setup_subparser(name, description, commands):\n subparser = SUBPARSER.add_parser(\n name,\n help=description\n )\n subparser.add_argument(\n 'sub_command',\n metavar='sub_command',\n type=str,\n nargs='+',\n help='Which command to run. Options: %s' % ', '.join(commands),\n choices=commands\n )\n\n return subparser","def create_parser(self, prog_name, subcommand):\n parser = CommandParser(\n self, prog=\"%s %s\" % (os.path.basename(prog_name), subcommand),\n description=self.help or None,\n )\n parser.add_argument(\n '--pythonpath',\n help='A directory to add to the Python path',\n )\n self.add_arguments(parser)\n return parser","def add_subparser(subparsers):\n parser = subparsers.add_parser('run', help=\"run artifact\")\n parser.add_argument('run_config', default='cps.ini',\n help=\"run configuration file\")\n parser.add_argument('-p', '--persist', action=\"store_true\", default=False,\n help=\"to persist data, dont delete sandbox after use\")\n\n parser.set_defaults(func=main)","def create_parser(self, prog_name, subcommand):\r\n self.prog_name = \"{} {}\".format(prog_name, subcommand)\r\n return super(TrackedCommand, self).create_parser(prog_name, subcommand)","def customize_subparser(cls, subparser: argparse.ArgumentParser) -> None:\n pass","def subparser( parser, subparsers ):","def SubParser(self):\n if not self._sub_parser:\n # pylint: disable=protected-access\n self._sub_parser = self._parser.add_subparsers(\n action=parser_extensions.CloudSDKSubParsersAction,\n calliope_command=self)\n return self._sub_parser","def add_subcommands(cls, parser: argparse.ArgumentParser) -> None:\n if cls.SUBCOMMANDS:\n subparsers = parser.add_subparsers(title=\"subcommands\", metavar=\"\", dest='cmd')\n for subcmd_class in cls.SUBCOMMANDS:\n parsers = subcmd_class.get_args()\n subcmd_class.parser = parsers[-1]\n\n subparser = subparsers.add_parser(\n subcmd_class.NAMES[0],\n aliases=subcmd_class.NAMES[1:],\n parents=parsers,\n help=subcmd_class.HELP,\n epilog=subcmd_class.EPILOG)\n subparser.set_defaults(command_class=subcmd_class)\n subcmd_class.customize_subparser(subparser)","def create_parser(self, prog_name, subcommand):\r\n # Hack __main__ so --help in dev_appserver_main works OK.\r\n sys.modules['__main__'] = dev_appserver_main\r\n return super(Command, self).create_parser(prog_name, subcommand)","def get_parser(subparsers, parent=None):\n parser = subparsers.add_parser(\n \"flow\",\n description=\"Invoke ML on MCU flow\",\n parents=[parent] if parent else [],\n add_help=(parent is None),\n )\n parser.set_defaults(func=handle)\n add_common_options(parser)\n add_context_options(parser)\n add_flow_options(parser)\n subparsers = parser.add_subparsers(dest=\"subcommand2\") # this line changed\n load.get_parser(subparsers)\n tune.get_parser(subparsers)\n build.get_parser(subparsers)\n compile_.get_parser(subparsers)\n run.get_parser(subparsers)","def add_subparser(subparsers):\n parser = subparsers.add_parser(\"utils/update\",\n description=\"Update apt and the groot_ansible ecosystem\", # this shows in the help for this command\n help=\"update your ansible/apt environment\", # this shows in the parent parser\n formatter_class=argparse.ArgumentDefaultsHelpFormatter\n )\n common.add_ansible_arguments(parser)\n parser.set_defaults(func=parse_args)","def _add_to_cli(self, parser, group=None):\n container = self._get_argparse_container(parser, group)\n kwargs = self._get_argparse_kwargs(group)\n prefix = self._get_argparse_prefix('', group.name if group else None)\n deprecated_names = []\n for opt in self.deprecated_opts:\n deprecated_name = self._get_deprecated_cli_name(opt.name,\n opt.group)\n if deprecated_name is not None:\n deprecated_names.append(deprecated_name)\n self._add_to_argparse(parser, container, self.name, self.short,\n kwargs, prefix,\n self.positional, deprecated_names)","def fill_subparsers(subparsers):\n\n\t\tfor cls in BaseCommand.__subclasses__():\n\t\t\tcls.add_subparser(subparsers)","def init_parser(subparsers):\n parser = subparsers.add_parser(COMMAND, help=\"Add a new task to the task list\")\n parser.add_argument(\"title\", type=str, help=\"The title of the new task\")\n parser.add_argument(\"description\", type=str, help=\"The description of the new task\")\n doto.cli.cmd.task.init_task_flags(parser)","def create_parser():\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(title=\"Commands\", dest=\"subparser_name\")\n subparsers.add_parser(\"generate-settings\", help=\"Generate settings.json to install \"\n \"Gluu Cloud Native Edition non-interactively\")\n subparsers.add_parser(\"install\", help=\"Install Gluu Cloud Native Edition using Kustomize. Depreciated > 4.3.\")\n subparsers.add_parser(\"install-no-wait\", help=\"Install Gluu Cloud Native Edition using Kustomize. \"\n \"Depreciated > 4.3. \"\n \"There will be no wait time between installing services. \"\n \"Pods may look like they are restarting but they will \"\n \"be waiting for hierarchy \"\n \"pods to be running\")\n subparsers.add_parser(\"install-ldap-backup\", help=\"Install ldap backup cronjob only.\")\n subparsers.add_parser(\"restore\", help=\"Install Gluu Cloud Native Edition with a \"\n \"running database and previous configuration using Kustomize.\"\n \"Depreciated > 4.3\")\n subparsers.add_parser(\"uninstall\", help=\"Uninstall Gluu that was installed using Kustomize. \"\n \"Depreciated > 4.3\")\n subparsers.add_parser(\"upgrade\", help=\"Upgrade Gluu Cloud Native Edition using Kustomize. Depreciated > 4.3\")\n subparsers.add_parser(\"upgrade-values-yaml\", help=\"Upgrade Gluu Cloud Native Edition\")\n subparsers.add_parser(\"install-couchbase\", help=\"Install Couchbase only. Used with installation of Gluu with Helm\")\n subparsers.add_parser(\"install-couchbase-backup\", help=\"Install Couchbase backup only.\")\n subparsers.add_parser(\"uninstall-couchbase\", help=\"Uninstall Couchbase only.\")\n subparsers.add_parser(\"helm-install\", help=\"Install Gluu Cloud Native Edition using helm. \"\n \"This also installs the nginx-ingress chart\")\n subparsers.add_parser(\"helm-uninstall\", help=\"Uninstall Gluu Cloud Native Edition using helm. \"\n \"This also uninstalls the nginx-ingress chart\")\n\n subparsers.add_parser(\"helm-install-gluu\", help=\"Install Gluu Cloud Native Edition using helm. \"\n \"This assumes nginx-ingress is installed\")\n subparsers.add_parser(\"helm-uninstall-gluu\", help=\"Uninstall Gluu Cloud Native Edition using helm. \"\n \"This only uninstalls Gluu\")\n subparsers.add_parser(\"version\", help=\"Outputs version of pygluu installer.\")\n return parser","def add_args_to_subparser(the_parser, subcommand_name):\n\n the_parser.add_argument(CmdArgs.verbose_optional, help=CmdArgs.verbose_help,\n action='store_true',\n )\n\n if subcommand_name in DCA_VISUALIZATION_SUBCOMMANDS:\n the_parser.add_argument(CmdArgs.biomolecule, help=CmdArgs.biomolecule_help)\n the_parser.add_argument(CmdArgs.pdb_chain_id, help=CmdArgs.pdb_chain_id_help)\n the_parser.add_argument(CmdArgs.pdb_file, help=CmdArgs.pdb_file_help)\n the_parser.add_argument(CmdArgs.refseq_file, help=CmdArgs.refseq_file_help)\n the_parser.add_argument(CmdArgs.dca_file, help=CmdArgs.dca_file_help)\n the_parser.add_argument(CmdArgs.rna_secstruct_file_optional,\n help=CmdArgs.rna_secstruct_file_help,\n )\n the_parser.add_argument(CmdArgs.linear_dist_optional,\n help=CmdArgs.linear_dist_help, type = int,\n )\n the_parser.add_argument(CmdArgs.contact_dist_optional,\n help=CmdArgs.contact_dist_help, type = float,\n )\n the_parser.add_argument(CmdArgs.num_dca_contacts_optional,\n help = CmdArgs.num_dca_contacts_help, type = int,\n )\n the_parser.add_argument(CmdArgs.wc_neighbor_dist_optional, type= int,\n help = CmdArgs.wc_neighbor_dist_help,\n )\n the_parser.add_argument(CmdArgs.pdb_id_optional, help = CmdArgs.pdb_id_help)\n\n if subcommand_name in FILE_CONTENT_SUBCOMMANDS:\n if subcommand_name == 'pdb_content':\n the_parser.add_argument(CmdArgs.pdb_file, help = CmdArgs.pdb_file_help)\n if subcommand_name in MSA_TRIMMING_SUBCOMMANDS:\n the_parser.add_argument(CmdArgs.max_gap_optional,\n type = float, help = CmdArgs.max_gap_help,\n )\n if subcommand_name == 'trim_by_refseq':\n the_parser.add_argument(CmdArgs.biomolecule, help=CmdArgs.biomolecule_help)\n the_parser.add_argument(CmdArgs.msa_file, help=CmdArgs.msa_file_help)\n the_parser.add_argument(CmdArgs.refseq_file, help=CmdArgs.refseq_file_help)\n the_parser.add_argument(CmdArgs.remove_all_gaps_optional,\n help= CmdArgs.remove_all_gaps_help, action='store_true',\n )\n if subcommand_name == 'trim_by_gap_size':\n the_parser.add_argument(CmdArgs.msa_file, help=CmdArgs.msa_file_help)\n return None","def create_parser(self, prog_name, subcommand):\n parser = CommandParser(\n self, prog=\"%s %s\" % (os.path.basename(prog_name), subcommand),\n description=self.help or None,\n )\n parser.add_argument(\n '--version', action='version', version=self.get_version())\n\n self.add_arguments(parser)\n return parser","def configure_cli(subparsers) -> None: # type: ignore\n parser = subparsers.add_parser(\n name='items',\n description='Retrieve new/updated items from Aleph and send to CaiaSoft'\n )\n parser.set_defaults(cmd_name='items')","def add_subparsers(cls, parser, name=\"\", prefixes=[], delim=\"_\", title=\"commands\", description=\"available commands\", required=True):\n\t\tcommand = f\"command_{name}\"\n\t\tif command in cls.COMMANDS:\n\t\t\traise CommandParserNameDuplicated(f\"Command parser with name {name} already registered.\")\n\t\t\n\t\tcls.COMMANDS[command] = {}\n\t\t\n\t\tsub = parser.add_subparsers(title=title, dest=command, description=description)\n\t\tsub.required = True\n\t\tfor pf in prefixes:\n\t\t\tfor c, method in cls.get_commands(prefix=pf, delim=delim):\n\t\t\t\tcls.set_subparser_for(c, method, sub)\n\t\t\t\tcls.COMMANDS[command][c] = method","def register(\n self,\n root_parser: argparse.ArgumentParser,\n subparsers: argparse._SubParsersAction,\n ) -> None:\n self.root_parser = root_parser\n parser = subparsers.add_parser(\n self.name,\n aliases=self.aliases,\n help=self.help,\n description=self.help,\n add_help=self.add_help,\n )\n parser.set_defaults(command=self)\n self.configure(parser)","def add_arg_parser(subparsers):\n # add\n add_p = subparsers.add_parser('add', description='Create a bundle from a .csv, .tsv, or a directory of files.')\n add_p.add_argument('-t', '--tag', nargs=1, type=str, action='append',\n help=\"Set one or more tags: 'dsdt add -t authoritative:True -t version:0.7.1'\")\n add_p.add_argument('bundle', type=str, help='The destination bundle in the current context')\n add_p.add_argument('path_name', type=str, help='File or directory of files to add to the bundle', action='store')\n add_p.set_defaults(func=lambda args: _add(args))","def add_subparser(\n subparsers: SubParsersAction, parents: List[argparse.ArgumentParser]\n) -> None:\n run_parser = subparsers.add_parser(\n \"run\",\n parents=parents,\n conflict_handler=\"resolve\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n help=\"Starts a Rasa server with your trained model.\",\n )\n run_parser.set_defaults(func=run)\n\n run_subparsers = run_parser.add_subparsers()\n sdk_subparser = run_subparsers.add_parser(\n \"actions\",\n parents=parents,\n conflict_handler=\"resolve\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n help=\"Runs the action server.\",\n )\n sdk_subparser.set_defaults(func=run_actions)\n\n arguments.set_run_arguments(run_parser)\n arguments.set_run_action_arguments(sdk_subparser)","def create_parser(self, prog_name, subcommand):\r\n return OptionParser(prog=prog_name,\r\n usage=self.usage(subcommand),\r\n version='',\r\n add_help_option = False,\r\n option_list=self.option_list)","def add_subcommand(self, command):\n\n if self.subcommand_parser is None:\n self.subcommand_parser = self.parser.add_subparsers(\n dest='command', help='Please select one command mode below',\n title='Command modes'\n )\n self.subcommands = {}\n\n if not isinstance(command, ScriptCommand):\n raise ScriptError('Subcommand must be a ScriptCommand instance')\n\n parser = self.subcommand_parser.add_parser(\n command.name,\n help=command.short_description,\n description=command.description,\n epilog=command.epilog,\n formatter_class=argparse.RawTextHelpFormatter,\n )\n self.subcommands[command.name] = command\n command.script = self\n\n if callable(getattr(command, '__register_arguments__', None)):\n command.__register_arguments__(parser)\n\n return parser","def init_parser(self):\n usage = \"%s [options] [subcommand]\" % (self.command)\n description = \"An SBTools test plug-in.\"\n\n parser = SBToolOptionParser(self, self.sbtools, usage, description=description)\n return parser","def make_adder(self, *args, **kwargs):\n kwargs.setdefault(\"dest\", \"subcmd\")\n subp = self.add_subparsers(*args, **kwargs)\n return partial(self.add_subcommand, subp)","def register_cli(cls):\n for cmd in cls.SUB_GROUP_COMMANDS:\n getattr(cls, cls.SUB_GROUP_NAME).add_command(getattr(cls, cmd))","def add_parser(subparsers):\n parser = subparsers.add_parser('upload', help='upload build to Koji')\n\n parser.add_argument('--scm-url', required=True,\n help='SCM URL for this build, eg. git://...')\n parser.add_argument('--owner', required=True,\n help='koji user name that owns this build')\n parser.add_argument('--tag',\n help='tag this build, eg. ceph-3.2-xenial-candidate')\n parser.add_argument('--dryrun', action='store_true',\n help=\"Show what would happen, but don't do it\")\n parser.add_argument('--skip-log', action='store_true',\n help=\"Do not upload a .build log file\")\n parser.add_argument('directory', help=\"parent directory of a .dsc file\")\n parser.set_defaults(func=main)","def add_parser(subparsers) -> None:\n contest_parser = subparsers.add_parser(\n 'contest', help='build contest files')\n mut_ex_group = contest_parser.add_mutually_exclusive_group()\n mut_ex_group.add_argument(\n '-p', '--pdf', action='store_true', default=False, help='generate contest PDFs')\n mut_ex_group.add_argument('-i', '--io', action='store_true',\n default=False, help='generate contest input/output files')\n contest_parser.add_argument(\n 'problem_dir', help='path to problem(s)', nargs='+')\n contest_parser.add_argument(\n 'contest_dir', help='directory which the contest will be saved')\n contest_parser.set_defaults(function=lambda options: process_contest(\n options.problem_dir, options.contest_dir, options.pdf, options.io))","def build_cli(self):\n parser = argparse.ArgumentParser(\"xsgen\",\n conflict_handler='resolve', argument_default=NotSpecified)\n for plugin in self.plugins:\n plugin.update_argparser(parser)\n self.parser = parser\n return parser","def add_subcommand(self, cmd):\n self.subcommands.append(cmd)","def _init_cfg_subcmd(subparsers):\n cfg_related = subparsers.add_parser(\"cfg\",)\n cfg_subparsers = cfg_related.add_subparsers(dest=\"cfg_subcommand\")\n\n cfg_write_parser = cfg_subparsers.add_parser(\"write\")\n cfg_write_parser.add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=None,\n help=\"Specify if this config is for user or just the working directory.\",\n )\n cfg_write_parser.add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n cfg_subparsers.add_parser(\"show\")\n\n cfg_export_parser = cfg_subparsers.add_parser(\"export\")\n cfg_export_parser.add_argument(\"--dir\", default=os.getcwd())\n\n return cfg_related","def _make_parser(self, **kwargs):\n\n kwargs.setdefault('help', self.help)\n kwargs.setdefault('formatter_class',argparse.RawDescriptionHelpFormatter)\n kwargs.setdefault('description', self.description)\n kwargs.setdefault('name', self.name)\n names = (kwargs.get('name') or self.name).split('.')\n \n def _get_subparser(a):\n if a._subparsers:\n for action in a._subparsers._actions:\n if isinstance(action, argparse._SubParsersAction):\n return action\n raise RuntimeError('could not find adequate subparser')\n return a.add_subparsers(dest='command',\n title='commands',\n metavar='COMMAND')\n def _get_parser(node, idx, names):\n name = names[idx]\n if name in node.choices:\n return node.choices[name]\n args = {\n 'name' : name,\n 'help' : 'a group of sub-commands',\n }\n return node.add_parser(**args)\n \n parser = ACMD_PARSER\n node = _get_subparser(parser)\n\n for i,n in enumerate(names[:-1]):\n node = _get_subparser(parser)\n parser = _get_parser(node, i, names)\n \n node = _get_subparser(parser)\n kwargs['name'] = names[-1]\n parser = node.add_parser(**kwargs)\n return parser","def _init_cfg_subcmd(subparsers):\n cfg_related = subparsers.add_parser(\"cfg\")\n cfg_subparsers = cfg_related.add_subparsers(dest=\"cfg_subcommand\")\n\n cfg_write_parser = cfg_subparsers.add_parser(\"write\")\n cfg_write_parser.add_argument(\n \"--level\",\n choices=[\"user\", \"cwd\"],\n default=None,\n help=\"Specify if this config is for user or just the working directory.\",\n )\n cfg_write_parser.add_argument(\n \"--open\", action=\"store_const\", const=True, default=False\n )\n cfg_subparsers.add_parser(\"show\")\n\n cfg_export_parser = cfg_subparsers.add_parser(\"export\")\n cfg_export_parser.add_argument(\"--dir\", default=os.getcwd())\n\n return cfg_related","def add_new_subparser(subparsers, formatter_class=RawTextHelpFormatter):\n # TODO: add info on no args to description or help\n # Adds custom --help argument\n generic_parent_parser = cmd.argparse.get_generic_parent_parser()\n new_description = 'Create a new test module or page object'\n new_help = new_description\n new_parser = subparsers.add_parser(\n 'new', description=new_description, help=new_help,\n parents=[generic_parent_parser],\n formatter_class=formatter_class,\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\n )\n # New <type> subparsers\n new_type_desc = 'Run \\'{} <type> --help\\' for details'.format(new_parser.prog)\n new_subparsers = new_parser.add_subparsers(\n title='File Types', description=new_type_desc, dest='type', metavar='<type>'\n )\n # New test parser\n new_test_parent_parser = get_new_parent_parser(\n parents=[generic_parent_parser], class_name_metavar='<TestCaseClass>',\n class_name_help='Name to use for the initial test case class'\n )\n new_test_description = 'Create a new test module'\n new_test_help = new_test_description\n new_subparsers.add_parser(\n 'test', description=new_test_description, help=new_test_help,\n parents=[new_test_parent_parser],\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\n )\n # New page object parser\n new_page_parent_parser = get_new_parent_parser(\n parents=[generic_parent_parser], class_name_metavar='<PageObjectClass>',\n class_name_help='Name to use for the initial page object class'\n )\n new_page_description = 'Create a new page object module'\n new_page_help = new_page_description\n new_page_parser = new_subparsers.add_parser(\n 'page', description=new_page_description, help=new_page_help,\n parents=[new_page_parent_parser],\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\n )\n # TODO: add optional --prototype arg with a list of valid page object prototype classes\n return new_parser","def init_cli(self):\n self.cli_parser.add_cmd(\"completions\", \"Return a list of completions for autocomplete\",\n self.cli_completions)\n\n self.cli_parser.add_cmd(\"disable-plugin\", \"Disable the named plugin\",\n self.cli_disable_plugin,\n \"plugin_name\", \"Name of plugin to disable\")\n\n self.cli_parser.add_cmd(\"enable-plugin\", \"Enables the named plugin\",\n self.cli_enable_plugin,\n \"plugin_name\", \"Name of the plugin to enable\")\n\n self.cli_parser.add_cmd(\"help\", \"Displays this message\",\n self.cli_parser.get_help)\n\n self.cli_parser.add_cmd(\"kill-plugin\", \"Kills the named plugin\",\n self.cli_kill_plugin,\n \"plugin_name\", \"Name of the plugin to kill\")\n\n self.cli_parser.add_cmd(\"load-config\", \"Reloads a configuration from file\",\n self.cli_load_config,\n \"config_path\", \"Path of the configuration file\")\n\n self.cli_parser.add_cmd(\"load-plugin\", \"Loads (or reloads) a plugin from disk\",\n self.cli_load_plugin,\n \"plugin_name\", \"Name of the plugin to load\")\n\n self.cli_parser.add_cmd(\"list-plugins\", \"List all loaded plugins\",\n self.cli_list_plugins)\n\n self.cli_parser.add_cmd(\"start-plugin\", \"Start the named plugin\",\n self.cli_start_plugin,\n \"plugin_name\", \"Name of the plugin to start\")\n\n self.cli_parser.add_cmd(\"stop-plugin\", \"Stop the named plugin\",\n self.cli_stop_plugin,\n \"plugin_name\", \"Name of the plugin to stop\")\n\n self.cli_parser.add_cmd(\"stop-all-plugins\", \"Stop all plugins\",\n self.cli_stop_all_plugins)\n\n self.cli_parser.add_cmd(\"restart-plugin\", \"Restarts the named plugin\",\n self.cli_restart_plugin,\n \"plugin_name\", \"Name of the plugin to restart\")\n\n self.cli_parser.add_cmd(\"unload-plugin\", \"Unloads the named plugin\",\n self.cli_unload_plugin,\n \"plugin_name\", \"Name of the plugin to unload\")","def set_subparser_for(cls, command, method, subparser):\n\n\t\tdef add_pos_argument(sub, label, arg):\n\t\t\tif arg[\"type\"] == bool:\n\t\t\t\traise CommandTypeError(\"bool type not supported as positional argument\")\n\t\t\tif \"value\" in arg:\n\t\t\t\tif arg[\"type\"] in [str, int, float]:\n\t\t\t\t\tsub.add_argument(label, nargs='?', default=arg[\"value\"], type=arg[\"type\"], help=arg[\"help_line\"])\n\t\t\telif \"values\" in arg:\n\t\t\t\tif arg[\"type\"] in [str, int, float]:\n\t\t\t\t\tsub.add_argument(label, nargs='?', default=arg[\"values\"][0], choices=arg[\"values\"], type=arg[\"type\"], help=arg[\"help_line\"])\n\t\t\t\telif arg[\"type\"] == list:\n\t\t\t\t\tsub.add_argument(label, nargs='+', default=arg[\"values\"][0], choices=arg[\"values\"], help=arg[\"help_line\"])\n\t\t\telse:\n\t\t\t\tsub.add_argument(label, type=arg[\"type\"], help=arg[\"help_line\"])\n\n\t\tdef add_opt_argument(sub, label, arg, add_alias=True):\n\t\t\tif arg[\"type\"] == bool:\n\t\t\t\tif add_alias:\n\t\t\t\t\tsub.add_argument(arg[\"alias\"], arg[\"name\"], action=\"store_true\", default=False, help=arg[\"help_line\"])\n\t\t\t\telse:\n\t\t\t\t\tsub.add_argument(arg[\"name\"], action=\"store_true\", default=False, help=arg[\"help_line\"])\n\n\t\t\telif arg[\"type\"] in [str, int, float] and \"value\" in arg:\n\t\t\t\tif add_alias:\n\t\t\t\t\tsub.add_argument(arg[\"alias\"], arg[\"name\"], type=arg[\"type\"], default=arg[\"value\"], help=arg[\"help_line\"])\n\t\t\t\telse:\n\t\t\t\t\tsub.add_argument(arg[\"name\"], type=arg[\"type\"], default=arg[\"value\"], help=arg[\"help_line\"])\n\t\t\telif arg[\"type\"] == list and \"values\" not in arg:\n\t\t\t\tsub.add_argument(label, nargs=\"*\", help=arg[\"help_line\"])\n\t\t\telif \"values\" in arg:\n\t\t\t\tif arg[\"type\"] == list:\n\t\t\t\t\tsub.add_argument(label, choices=arg[\"values\"], default=arg[\"values\"][0], nargs=\"*\", help=arg[\"help_line\"])\n\t\t\t\telse:\n\t\t\t\t\tsub.add_argument(label, type=arg[\"type\"], choices=arg[\"values\"], default=arg[\"values\"][0], nargs=\"?\", help=arg[\"help_line\"])\n\t\t\telse:\n\t\t\t\tif add_alias:\n\t\t\t\t\tsub.add_argument(arg[\"alias\"], arg[\"name\"], type=arg[\"type\"], help=arg[\"help_line\"])\n\t\t\t\telse:\n\t\t\t\t\tsub.add_argument(arg[\"name\"], type=arg[\"type\"], help=arg[\"help_line\"])\n\t\t\n\t\tfunc = getattr(cls, method)\n\n\t\targs_info = cls.__parse_docstring(func.__doc__)\n\t\tif args_info == {}:\n\t\t\treturn\n\n\t\tc = subparser.add_parser(command, help=args_info[\"help_line\"])\n\n\t\tif \"arguments\" in args_info:\n\t\t\tfor label, arg in args_info[\"arguments\"].items():\n\t\t\t\tif arg[\"pos\"]:\n\t\t\t\t\tadd_pos_argument(c, label, arg)\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tadd_opt_argument(c, label, arg, add_alias=True)\n\t\t\t\t\texcept ArgumentError as e:\n\t\t\t\t\t\tadd_opt_argument(c, label, arg, add_alias=False)","def calcs_parser(subparser, name, help_msg):\n parser = subparser.add_parser(name,\\\n help=help_msg)\n parser = add_arg(parser, 'parallel')\n parser = add_arg(parser, 'xvars')\n parser = add_arg(parser, 'time')\n parser = add_arg(parser, 'modif_time')\n parser = add_arg(parser, 'modif_coord')\n parser = add_arg(parser, 'modif_var')\n parser = add_arg(parser, 'eswitch')\n parser = add_arg(parser, 'fswitch')\n parser.add_argument(\"args\", metavar='SELAFIN file', nargs=\"+\")\n\n return subparser","def setup_subcommands(argparser):\n\n subparsers = argparser.add_subparsers()\n\n parser_info = subparsers.add_parser('info', help = 'Provide the information about the user')\n parser_info.add_argument('user', help = 'The user to inspect')\n\n parser_ownerships = subparsers.add_parser('ownerships', help = 'Show items which this user owns')\n parser_ownerships.add_argument('user', help = 'The name of the user to show information about')\n parser_ownerships.add_argument('-r', '--recursive', action = 'store_true', help = 'Show items which this user own through being in lists')\n \n parser_info.set_defaults(handler = show_info)\n parser_ownerships.set_defaults(handler = show_ownerships)","def create_parser(self, prog_name, subcommand):\n return OptionParser(prog=prog_name,\n usage=self.usage(subcommand),\n version=self.get_version(),\n option_list=self.option_list)","def test_create_subparser_noop(self):\n self.command.create_subparser()","def create_parser():\n parser = argparse.ArgumentParser(\n description='CLI for SMS',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n\n # Add subcommands\n subparsers = parser.add_subparsers(title='subcommands', dest='cmd')\n\n # Downlink Unitdata\n downlink_unitdata_parser = subparsers.add_parser(\n 'DU', help=\"Send downlink unitdata to SMSOrc8rGW service\",\n )\n downlink_unitdata_parser.add_argument('imsi', help='e.g. 001010000090122 (no prefix required)')\n downlink_unitdata_parser.add_argument('data', help='Data as a hex string e.g. 1fc13a00')\n downlink_unitdata_parser.set_defaults(func=send_downlink_unitdata)\n\n return parser","def add_cmd(self, name: str, help_str: str, cmd_fn: typing.Callable, arg: str = None, arg_help: str = None):\n self.cmd_names.append(name)\n cmd = self.cli_subparsers.add_parser(name, help=help_str)\n cmd.set_defaults(func=cmd_fn)\n if arg is not None:\n cmd.add_argument(arg, help=arg_help)","def load_into(subparser, as_cmd=None):\n p = subparser\n p.description = description\n\n if not as_cmd:\n as_cmd = default_name\n out = cli.CommandSuite(as_cmd, p)\n out.load_subcommand(topics)\n return out","def add_arguments(self, sub_parser):\n sp = sub_parser","def cli():\n pass # do nothing here, it just defines the name for other subcommands","def build_subcommands_parser(parser, module):\n mdefs = module.__dict__\n keys = list(mdefs.keys())\n keys.sort()\n subparsers = parser.add_subparsers(help='sub-command help')\n for command in keys:\n if command.startswith('pub_'):\n func = module.__dict__[command]\n parser = subparsers.add_parser(command[4:], help=func.__doc__)\n parser.set_defaults(func=func)\n argspec = inspect.signature(func)\n positionals = []\n short_opts = set([])\n for arg in argspec.parameters.values():\n if arg.default == inspect.Parameter.empty:\n positionals += [arg]\n else:\n param_name = arg.name.replace('_', '-')\n short_opt = param_name[0]\n if not (param_name.startswith('no') or\n (short_opt in short_opts)):\n opts = ['-%s' % short_opt, '--%s' % param_name]\n else:\n opts = ['--%s' % param_name]\n short_opts |= set([short_opt])\n if isinstance(arg.default, list):\n parser.add_argument(*opts, action='append')\n elif isinstance(arg.default, dict):\n parser.add_argument(*opts, type=json.loads)\n elif arg.default is False:\n parser.add_argument(*opts, action='store_true')\n elif arg.default is not None:\n parser.add_argument(*opts, default=arg.default)\n else:\n parser.add_argument(*opts)\n if positionals:\n for arg in positionals[:-1]:\n parser.add_argument(arg.name)\n parser.add_argument(positionals[-1].name, nargs='*')","def add_complete_hic_subparser(subparsers):\n parser = subparsers.add_parser(\"hic-complete\", help=\"HiFive HiC Project Complete Analysis Function: Create all necessary files (Fend, Data, and Project) and learn correction parameters for a HiFive HiC project.\", epilog=\"For command line options of each normalization approach, type: %(prog)s <ALGORITHM> -h\")\n subparser2 = parser.add_subparsers(dest='algorithm')\n prob_parser = subparser2.add_parser(\"probability\", help=\"A probability model based approach for learning correction values associated with each fend. Learning is accomplished using gradient descent.\")\n exp_parser = subparser2.add_parser(\"express\", help=\"An appoximation based approach for learning correction values associated with each fend. Learning is accomplished using a variation of matrix balancing.\")\n bin_parser = subparser2.add_parser(\"binning\", help=\"A multivariate binning probability model-based approach for learning correction values associated with fend characteristics. Learning is accomplished using the Broyden-Fletcher-Goldfarb-Shanno algorithm.\")\n binprob_parser = subparser2.add_parser(\"binning-probability\", help=\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a probability model.\")\n binexp_parser = subparser2.add_parser(\"binning-express\", help=\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a matrix-balancing approximation.\")\n add_complete_hic_group(prob_parser)\n add_hic_probability_group(prob_parser)\n add_complete_hic_group(exp_parser)\n add_hic_express_group(exp_parser)\n add_complete_hic_group(bin_parser)\n add_hic_binning_group(bin_parser)\n add_complete_hic_group(binprob_parser)\n add_hic_probability_group(binprob_parser)\n add_hic_binning_group(binprob_parser)\n add_complete_hic_group(binexp_parser)\n add_hic_express_group(binexp_parser)\n add_hic_binning_group(binexp_parser)\n return","def add_hicnormalize_subparser(subparsers):\n parser = subparsers.add_parser(\"hic-normalize\", help=\"HiFive HiC Project Normalization Function: Learn correction parameters for a HiFive HiC project.\", epilog=\"For command line options of each normalization approach, type: %(prog)s <ALGORITHM> -h\")\n subparser2 = parser.add_subparsers(dest='algorithm')\n prob_parser = subparser2.add_parser(\"probability\", help=\"A probability model based approach for learning correction values associated with each fend. Learning is accomplished using gradient descent.\")\n exp_parser = subparser2.add_parser(\"express\", help=\"An appoximation based approach for learning correction values associated with each fend. Learning is accomplished using a variation of matrix balancing.\")\n bin_parser = subparser2.add_parser(\"binning\", help=\"A multivariate binning probability model-based approach for learning correction values associated with fend characteristics. Learning is accomplished using the Broyden-Fletcher-Goldfarb-Shanno algorithm.\")\n binprob_parser = subparser2.add_parser(\"binning-probability\", help=\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a probability model.\")\n binexp_parser = subparser2.add_parser(\"binning-express\", help=\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a matrix-balancing approximation.\")\n add_hic_normalize_group(prob_parser)\n add_hic_probability_group(prob_parser)\n add_hic_normalize_group(exp_parser)\n add_hic_express_group(exp_parser)\n exp_parser.add_argument(\"-f\", \"--min-interactions\", dest=\"minint\", required=False, type=int, default=20,\n action='store', help=\"The minimum number of interactions for fend filtering, if refiltering is required due to distance cutoff or selected reads. [default: %(default)s]\")\n add_hic_normalize_group(bin_parser)\n add_hic_binning_group(bin_parser)\n add_hic_normalize_group(binprob_parser)\n add_hic_probability_group(binprob_parser)\n add_hic_binning_group(binprob_parser)\n add_hic_normalize_group(binexp_parser)\n add_hic_express_group(binexp_parser)\n binexp_parser.add_argument(\"-f\", \"--min-interactions\", dest=\"minint\", required=False, type=int, default=20,\n action='store', help=\"The minimum number of interactions for fend filtering, if refiltering is required due to distance cutoff or selected reads. [default: %(default)s]\")\n add_hic_binning_group(binexp_parser)\n return","def register_argument_parser(add_parser, action):\n sub_command = str(action)\n return add_parser(sub_command,\n help=f'{sub_command} token',\n description=f'{sub_command.capitalize()} a Waiter token. '\n 'In addition to the optional arguments '\n 'explicitly listed below, '\n 'you can optionally provide any Waiter '\n 'token parameter as a flag. For example, '\n 'to specify 10 seconds for the '\n 'grace-period-secs parameter, '\n 'you can pass --grace-period-secs 10. '\n 'You can also provide nested fields separated by a period. For example, '\n 'to specify an environment variable FOO as \\\"bar\\\", you can pass --env.FOO \\\"bar\\\".')","def add_parser(subp, raw):\n tmpp = subp.add_parser('mkmodel', help='create a model file',\n formatter_class=raw,\n description=textwrap.dedent(DESC))\n tmpp.add_argument('model', type=str, metavar='MODELNAME',\n help='name of the model')\n tmpp.add_argument('type', type=str, choices=['ssa', 'sde'],\n help='model type')\n tmpp.add_argument('--nspecs', type=positive_type,\n help='number of species')\n tmpp.add_argument('--nreacs', type=positive_type,\n help='number of reactions')\n tmpp.add_argument('--dim', type=positive_type,\n help='dimension of phase space')\n tmpp.add_argument('-z', type=str, choices=['row', 'col'],\n help='state change matrix format')","def set_parser(*, collected, parser=None):\n if parser is None:\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers()\n commands = unique(collected)\n for name, details in commands.items():\n original = details.original\n args = details.extra\n a_subparser = subparsers.add_parser(name)\n a_subparser.set_defaults(\n __gather_name__=name,\n __gather_command__=original,\n )\n for arg_details in args:\n a_subparser.add_argument(*arg_details.args, **dict(arg_details.kwargs))\n return parser","def make_parser():\n parser = argparse.ArgumentParser(prog=__file__.replace(\".py\", \"\"),\n description='simple $PATH tool')\n parser.add_argument('-n', '--nocolor', dest=\"color\",\n action=\"store_false\", default=True,\n help='Turn off ANSI color codes.')\n parser.add_argument('-w', '--nowarn', action=\"store_true\",\n help='Turn off path warnings.')\n subs = parser.add_subparsers(title='subcommands',\n description='The subcommands')\n\n sub = subs.add_parser('replace', description=\"Search & Replace $PATH\")\n sub.set_defaults(cmd='path_replace')\n sub.add_argument('terms', nargs='+',\n help='Format: search:replace, search:replace, ...')\n\n sub = subs.add_parser('show', description=\"Show $PATH compoents\")\n sub.set_defaults(cmd='path_show')\n sub.add_argument('-n', '--nocolor', dest=\"color\",\n action=\"store_false\", default=True,\n help='Turn off ANSI color codes.')\n sub.add_argument('-w', '--nowarn', action=\"store_true\",\n help='Turn off path warnings.')\n\n sub = subs.add_parser('which', description=\"Platform agnostic `which -a`\")\n sub.set_defaults(cmd='path_which')\n sub.add_argument('look', help='Look for this executable')\n sub.add_argument('-n', '--nocolor', dest=\"color\",\n action=\"store_false\", default=True,\n help='Turn off ANSI color codes.')\n sub.add_argument('-v', '--version', action=\"store_true\",\n help='Show version of exact matches.')\n\n return parser","def argparse_subparser_name():\n return \"plugins\"","def cli_add_command(parent_parser):\n parser = parent_parser.add_parser(\"setup\", description=cli_add_command.__doc__)\n\n def run_it(_):\n from .util import print_important_env\n print_important_env()\n\n log.info(\"AKRR Setup\")\n\n _config_setup()\n setup()\n\n parser.set_defaults(func=run_it)","def register_subcommand(nest_ops_subparsers):\n parser = nest_ops_subparsers.add_parser('compile', \\\n help=COMPILE_CMD_HELP, \\\n description=COMPILE_CMD_DESCRIPTION, \\\n formatter_class=argparse.RawTextHelpFormatter )\n\n parser.add_argument('--code_type', \\\n choices=VALID_CODE_TYPES, \\\n help=CODE_TYPE_ARG_HELP, \\\n default='all' \\\n )\n\n parser.add_argument('--project', \\\n help=\"\"\"Which project to build. Only affects the web_assets:dist\n code_type, where it determines which project's index.html\n will be the main entry point index.html in the static files.\"\"\", \\\n choices=nest_envs.VALID_PROJECT_NAMES, \\\n default=nest_envs.DEFAULT_PROJECT_NAME, \\\n )\n \n parser.add_argument('--runlevel', \\\n help='Determines the run level for logging, error checking, etc.',\n choices=nest_envs.VALID_RUNLEVEL_NAMES,\n default=nest_envs.DEFAULT_RUNLEVEL_NAME, \\\n )\n\n #super ugly callback mechanism from argparse\n parser.set_defaults(func=_run_compile_cmd)\n return","def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs) -> ArgumentParser:\n return add_argparse_args(cls, parent_parser, **kwargs)","def get_command_line_parser():\n command_line_parser = argparse.ArgumentParser(\n description=\"Execute data workflows defined in flo.yaml files\",\n )\n subcommand_creator = command_line_parser.add_subparsers(\n title='SUBCOMMANDS',\n )\n for command_module in COMMAND_MODULES:\n command = command_module.Command(subcommand_creator)\n\n # this sets a default value for the command \"option\" so\n # that, when this Command is selected by argparse from the\n # command line, we know which comman instance it\n # corresponds with. See run_subcommand function below.\n command.option_parser.set_defaults(command=command)\n return command_line_parser","def import_additional_parser():\n # Parse arguments\n try:\n global add_parser\n import add_parser\n except ImportError as e:\n print('No additional parser found.')\n pass","def arg_parser(self):\n print\n\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers()\n\n status = subparsers.add_parser('status', help=self.viewer.status.__doc__)\n status.set_defaults(func=self.viewer.status)\n\n view_catalog = subparsers.add_parser('catalog', help=self.viewer.view_full_catalog.__doc__)\n view_catalog.set_defaults(func=self.viewer.view_full_catalog)\n\n view_tags = subparsers.add_parser('tags', help=self.viewer.view_all_tags.__doc__)\n view_tags.add_argument('-a', '--alpha', action='store_true', help=\"List tags alphabetically\")\n view_tags.set_defaults(func=self.viewer.view_all_tags)\n\n search = subparsers.add_parser('search', help=self.viewer.search_tags.__doc__)\n search.add_argument('-t', '--tags', nargs='+', required=True, help=\"Tags to search for\")\n search.add_argument('-o', action='store_true', help=\"Search using 'or' logic ('and' logic by default)\")\n search.set_defaults(func=self.viewer.search_tags) \n\n add_file = subparsers.add_parser('addfile', help=self.editor.add_files.__doc__)\n add_file.add_argument('filename', help=\"Name of file to add catalog\")\n add_file.set_defaults(func=self.editor.add_files)\n\n add_tags = subparsers.add_parser('addtags', help=self.editor.add_tag.__doc__)\n add_tags.add_argument('filename', help=\"Name of file to add catalog\")\n add_tags.add_argument('-t', '--tags', nargs='+', help=\"Tags to add to catalog\")\n add_tags.set_defaults(func=self.editor.add_tag)\n\n edit_entry = subparsers.add_parser('edit', help=self.editor.edit_entry.__doc__)\n edit_entry.add_argument('filename', help=\"Name of file to add catalog\")\n edit_entry.set_defaults(func=self.editor.edit_entry)\n\n clean_catalog = subparsers.add_parser('clean', help=self.editor.clean_catalog.__doc__)\n clean_catalog.add_argument('-t', '--tags', nargs='+', help=\"Tags to be deleted from catalog entirely\")\n clean_catalog.set_defaults(func=self.editor.clean_catalog)\n\n delete_entry = subparsers.add_parser('delete', help=self.editor.delete_entry.__doc__)\n delete_entry.add_argument('filename', help=\"File from which to delete specified tags\")\n delete_entry.add_argument('-t', '--tags', nargs='+', help=\"Tags to be deleted\")\n delete_entry.set_defaults(func=self.editor.delete_entry)\n\n merge_tags = subparsers.add_parser('merge', help=self.editor.merge_tags.__doc__)\n merge_tags.add_argument('--source', required=True, help=\"File from which tags are being taken\")\n merge_tags.add_argument('--dest', required=True, help=\"Destination file to which tags from --source are added\")\n merge_tags.set_defaults(func=self.editor.merge_tags)\n\n \n args = parser.parse_args()\n\n args.func(args)","def register_command(subparser):\n update_parser = subparser.add_parser('update', help='Run the log files through an updater. Used to update '\n 'between versions of autology')\n update_parser.set_defaults(func=_main)\n\n # Arguments\n update_parser.add_argument('-f', '--files', help='Update the files that are currently defined in the log '\n 'directories', action='store_true')\n update_parser.add_argument('-t', '--templates', help='Install a new output template', action='store_true')\n update_parser.add_argument('-T', '--template-definition', help='Define a template definition to install',\n default=template_utilities.DEFAULT_TEMPLATES_URL)","def add_commands(subparsers, path):\n log.debug('importing %s', path)\n try:\n del sys.modules[path]\n except KeyError:\n pass\n try:\n package = importlib.import_module(path)\n except Exception as e:\n log.warning('failed to import commands package %s',\n path, exc_info=True)\n return\n log.debug('commands package: %s', path)\n for (finder, name, ispkg) in pkgutil.iter_modules(package.__path__):\n if ispkg:\n continue\n try:\n command = importlib.import_module('.' + name, path)\n except Exception as e:\n log.warning('failed to import %s command: %s',\n path, name, exc_info=True)\n continue\n if not getattr(command, 'run', None):\n log.warning('skipping command module without run function: %s',\n name)\n continue\n log.debug('command: %s'%(name))\n name = command.__name__.split('.')[-1]\n parser_help = getattr(command, 'parser_help', None)\n if parser_help is None:\n log.warning('command %s missing help text'%(command.__name__))\n parser = subparsers.add_parser(name, help=parser_help)\n command.add_arguments(parser)\n parser.set_defaults(run=command.run)","def create_parser():\n helpdict = create_parser.helpdict\n # Customized usage, for more verbosity concerning these subparsers options.\n usage = \"\"\"%(prog)s [-h] [--version] {run,info} ... \"\"\"\n usage += tw.dedent(\"\"\"\\n\n From more help on each of the subcommands, type:\n %(prog)s run -h\n %(prog)s info -h\\n\\n\"\"\")\n\n # parser = ap.ArgumentParser(\n #parser = MpArgumentParser(\n #formatter_class=ap.ArgumentDefaultsHelpFormatter,\n #description='Monte Python, a Monte Carlo code in Python',\n #usage=usage)\n parser = initialise_parser(\n description='Monte Python, a Monte Carlo code in Python', usage=usage)\n\n # -- add the subparsers\n subparser = parser.add_subparsers(dest='subparser_name')\n\n ###############\n # run the MCMC\n runparser = add_subparser(subparser, 'run', help=\"run the MCMC chains\")\n\n # -- number of steps (OPTIONAL)\n runparser.add_argument('-N', help=helpdict['N'], type=positive_int,\n dest='N')\n # -- output folder (OBLIGATORY)\n runparser.add_argument('-o', '--output', help=helpdict['o'], type=str,\n dest='folder')\n # -- parameter file (OBLIGATORY)\n runparser.add_argument('-p', '--param', help=helpdict['p'],\n type=existing_file, dest='param')\n # -- covariance matrix (OPTIONAL)\n runparser.add_argument('-c', '--covmat', help=helpdict['c'],\n type=existing_file, dest='cov')\n # -- jumping method (OPTIONAL)\n runparser.add_argument('-j', '--jumping', help=helpdict['j'],\n dest='jumping', default='fast',\n choices=['global', 'sequential', 'fast'])\n # -- sampling method (OPTIONAL)\n runparser.add_argument('-m', '--method', help=helpdict['m'],\n dest='method', default='MH',\n choices=['MH', 'NS', 'PC', 'CH', 'IS', 'Der', 'Fisher'])\n # -- update Metropolis Hastings (OPTIONAL)\n runparser.add_argument('--update', help=helpdict['update'], type=int,\n dest='update', default=50)\n # -- update Metropolis Hastings with an adaptive jumping factor (OPTIONAL)\n runparser.add_argument('--superupdate', help=helpdict['superupdate'], type=int,\n dest='superupdate', default=0)\n # -- superupdate acceptance rate argument (OPTIONAL)\n runparser.add_argument('--superupdate-ar', help=helpdict['superupdate-ar'], type=float,\n dest='superupdate_ar', default=0.26)\n # -- superupdate acceptance rate tolerance argument (OPTIONAL)\n runparser.add_argument('--superupdate-ar-tol', help=helpdict['superupdate-ar-tol'], type=float,\n dest='superupdate_ar_tol', default=0.01)\n # -- adaptive jumping factor Metropolis Hastings (OPTIONAL)\n runparser.add_argument('--adaptive', help=helpdict['adaptive'], type=int,\n dest='adaptive', default=0)\n # -- adaptive ts argument (OPTIONAL)\n runparser.add_argument('--adaptive-ts', help=helpdict['adaptive-ts'], type=int,\n dest='adaptive_ts', default=1000)\n\n # -- jumping factor (OPTIONAL)\n runparser.add_argument('-f', help=helpdict['f'], type=float,\n dest='jumping_factor', default=2.4)\n # -- temperature (OPTIONAL)\n runparser.add_argument('-T', help=helpdict['T'], type=float,\n dest='temperature', default=1.0)\n # -- minimize (OPTIONAL)\n runparser.add_argument('--minimize', help=helpdict['minimize'],\n action='store_true')\n # -- minimize argument, minimization tolerance (OPTIONAL)\n runparser.add_argument('--minimize-tol', help=helpdict['minimize-tol'], type=float,\n dest='minimize_tol', default=0.00001)\n # -- fisher (OPTIONAL)\n runparser.add_argument('--fisher', help=helpdict['fisher'],\n action='store_true')\n # -- fisher argument (OPTIONAL)\n runparser.add_argument('--fisher-asymmetric', help=helpdict['fisher-asymmetric'],\n dest='fisher_asymmetric',action='store_true')\n # -- fisher step iteration (OPTIONAL)\n runparser.add_argument('--fisher-step-it', help=helpdict['fisher-step-it'],\n dest='fisher_step_it', default=10)\n # -- fisher step iteration argument, -deltaloglkl target (OPTIONAL)\n runparser.add_argument('--fisher-delta', help=helpdict['fisher-delta'], type=float,\n dest='fisher_delta', default=0.1)\n # -- fisher step iteration argument, -deltaloglkl tolerance (OPTIONAL)\n runparser.add_argument('--fisher-tol', help=helpdict['fisher-tol'], type=float,\n dest='fisher_tol', default=0.05)\n # -- fisher symmetric likelihood assumption threshold (OPTIONAL)\n runparser.add_argument('--fisher-sym-lkl', help=helpdict['fisher-sym-lkl'], type=float,\n dest='fisher_sym_lkl', default=0.1)\n # -- configuration file (OPTIONAL)\n runparser.add_argument('--conf', help=helpdict['conf'],\n type=str, dest='config_file',\n default='default.conf')\n # -- arbitrary numbering of an output chain (OPTIONAL)\n runparser.add_argument('--chain-number', help=helpdict['chain-number'])\n # -- stop run after first successful update using --update (EXPERIMENTAL)\n runparser.add_argument('--stop-after-update', help=helpdict['stop-after-update'],\n dest='stop_after_update', action='store_true')\n # display option\n runparser.add_argument('--display-each-chi2', help=helpdict['display-each-chi2'],\n dest='display_each_chi2', action='store_true')\n # -- parallel chains without MPI (OPTIONAL)\n runparser.add_argument('--parallel-chains', help=helpdict['parallel-chains'],\n action='store_true')\n\n ###############\n # MCMC restart from chain or best fit file\n runparser.add_argument('-r', '--restart', help=helpdict['r'],\n type=existing_file, dest='restart')\n runparser.add_argument('-b', '--bestfit', dest='bf', help=helpdict['b'],\n type=existing_file)\n\n ###############\n # Silence the output (no print on the console)\n runparser.add_argument('--silent', help=helpdict['silent'],\n action='store_true')\n ###############\n # Adding new derived parameters to a run\n runparser.add_argument(\n '--Der-target-folder', dest=\"Der_target_folder\",\n help=helpdict['Der-target-folder'], type=str, default='')\n runparser.add_argument(\n '--Der-param-list', dest='derived_parameters',\n help=helpdict['Der-param-list'], type=str, default='', nargs='+')\n\n ###############\n # Importance Sampling Arguments\n runparser.add_argument(\n '--IS-starting-folder', dest='IS_starting_folder',\n help=helpdict['IS-starting-folder'], type=str, default='', nargs='+')\n\n ###############\n # We need the following so the run does not crash if one of the external\n # samplers is not correctly installed despite not being used\n from contextlib import contextmanager\n import sys, os\n\n @contextmanager\n def suppress_stdout():\n with open(os.devnull, \"w\") as devnull:\n old_stdout = sys.stdout\n sys.stdout = devnull\n try:\n yield\n finally:\n sys.stdout = old_stdout\n\n ###############\n # MultiNest arguments (all OPTIONAL and ignored if not \"-m=NS\")\n # The default values of -1 mean to take the PyMultiNest default values\n try:\n with suppress_stdout():\n from MultiNest import NS_prefix, NS_user_arguments\n NSparser = runparser.add_argument_group(\n title=\"MultiNest\",\n description=\"Run the MCMC chains using MultiNest\"\n )\n for arg in NS_user_arguments:\n NSparser.add_argument('--'+NS_prefix+arg,\n default=-1,\n **NS_user_arguments[arg])\n except ImportError:\n # Not defined if not installed\n pass\n except:\n warnings.warn('PyMultiNest detected but MultiNest likely not installed correctly. '\n 'You can safely ignore this if not running with option -m NS')\n\n ###############\n # PolyChord arguments (all OPTIONAL and ignored if not \"-m=PC\")\n # The default values of -1 mean to take the PyPolyChord default values\n try:\n with suppress_stdout():\n from PolyChord import PC_prefix, PC_user_arguments\n PCparser = runparser.add_argument_group(\n title=\"PolyChord\",\n description=\"Run the MCMC chains using PolyChord\"\n )\n for arg in PC_user_arguments:\n PCparser.add_argument('--'+PC_prefix+arg,\n default=-1,\n **PC_user_arguments[arg])\n except ImportError:\n # Not defined if not installed\n pass\n except:\n warnings.warn('PyPolyChord detected but PolyChord likely not installed correctly. '\n 'You can safely ignore this if not running with option -m PC')\n\n ###############\n # CosmoHammer arguments (all OPTIONAL and ignored if not \"-m=CH\")\n # The default values of -1 mean to take the CosmoHammer default values\n try:\n with suppress_stdout():\n from cosmo_hammer import CH_prefix, CH_user_arguments\n CHparser = runparser.add_argument_group(\n title=\"CosmoHammer\",\n description=\"Run the MCMC chains using the CosmoHammer framework\")\n for arg in CH_user_arguments:\n CHparser.add_argument('--'+CH_prefix+arg,\n default=-1,\n **CH_user_arguments[arg])\n except ImportError:\n # Not defined if not installed\n pass\n except:\n warnings.warn('CosmoHammer detected but emcee likely not installed correctly. '\n 'You can safely ignore this if not running with option -m CH')\n\n ###############\n # Information\n infoparser = add_subparser(subparser, 'info',\n help=\"analyze the MCMC chains\")\n\n # -- folder to analyze\n infoparser.add_argument('files', help=helpdict['files'],\n nargs='+')\n # Silence the output (no print on the console)\n infoparser.add_argument('--silent', help=helpdict['silent'],\n action='store_true')\n # -- to only write the covmat and bestfit, without computing the posterior\n infoparser.add_argument('--minimal', help=helpdict['minimal'],\n action='store_true')\n # -- number of bins (defaulting to 20)\n infoparser.add_argument('--bins', help=helpdict['bins'],\n type=int, default=20)\n # -- temperature (OPTIONAL)\n infoparser.add_argument('-T', help=helpdict['T'], type=float,\n dest='temperature', default=1.0)\n # -- deprecated: remove the mean-likelihood line\n infoparser.add_argument('--no-mean', help=helpdict['no-mean'],\n dest='mean_likelihood_old', action='store_false')\n # -- plot the mean-likelihood line\n infoparser.add_argument('--plot-mean', help=helpdict['plot-mean'],\n dest='mean_likelihood', action='store_true')\n # -- to remove the mean and 68% limits on top of each 1D plot\n infoparser.add_argument('--short-title-1d', help=helpdict['short-title-1d'],\n dest='short_title_1d', action='store_true')\n # -- possible plot file describing custom commands\n infoparser.add_argument('--extra', help=helpdict['extra'],\n dest='optional_plot_file', default='')\n # -- if you just want the covariance matrix, use this option\n infoparser.add_argument('--noplot', help=helpdict['noplot'],\n dest='plot', action='store_false')\n # -- if you just want to output 1d posterior distributions (faster)\n infoparser.add_argument('--noplot-2d', help=helpdict['noplot-2d'],\n dest='plot_2d', action='store_false')\n # -- if you just want to output triangle with 2d contours\n infoparser.add_argument('--noplot-2d-diag', help=helpdict['noplot-2d-diag'],\n dest='plot_diag', action='store_false')\n # -- when plotting 2d posterior distribution, use contours and not contours\n # filled (might be useful when comparing several folders)\n infoparser.add_argument('--contours-only', help=helpdict['contours-only'],\n dest='contours_only', action='store_true')\n # -- if you want to output every single subplots\n infoparser.add_argument('--all', help=helpdict['all'], dest='subplot',\n action='store_true')\n # -- to change the extension used to output files (pdf is the default one,\n # but takes long, valid options are png and eps)\n infoparser.add_argument('--ext', help=helpdict['ext'],\n type=str, dest='extension', default='pdf')\n # -- to set manually the number of plots per hoorizontal raw in 1d plot\n infoparser.add_argument('--num-columns-1d', help=helpdict['num-columns-1d'],\n type=int, dest='num_columns_1d')\n # -- also analyze the non-markovian part of the chains\n infoparser.add_argument('--keep-non-markovian', help=helpdict['keep-non-markovian'],\n dest='markovian', action='store_false')\n # -- force only analyzing the markovian part of the chains\n infoparser.add_argument('--keep-only-markovian', help=helpdict['keep-only-markovian'],\n dest='only_markovian', action='store_true')\n # -- fraction of chains to be analyzed after burn-in removal (defaulting to 1.0)\n infoparser.add_argument('--keep-fraction', help=helpdict['keep-fraction'],\n type=float, dest='keep_fraction', default=1.0)\n # -- calculate the covariant matrix when analyzing the chains\n infoparser.add_argument('--want-covmat', help=helpdict['want-covmat'],\n dest='want_covmat', action='store_true')\n # -------------------------------------\n # Further customization\n # -- fontsize of plots (defaulting to 16)\n infoparser.add_argument('--fontsize', help=helpdict['fontsize'],\n type=int, default=16)\n # -- ticksize of plots (defaulting to 14)\n infoparser.add_argument('--ticksize', help=helpdict['ticksize'],\n type=int, default=14)\n # -- linewidth of 1d plots (defaulting to 4, 2 being a bare minimum for\n # legible graphs\n infoparser.add_argument('--line-width', help=helpdict['line-width'],\n type=int, default=4)\n # -- number of decimal places that appear on the tick legend. If you want\n # to increase the number of ticks, you should reduce this number\n infoparser.add_argument('--decimal', help=helpdict['decimal'], type=int,\n default=3)\n # -- number of ticks that appear on the graph.\n infoparser.add_argument('--ticknumber', help=helpdict['ticknumber'],\n type=int, default=3)\n # -- legend type, to choose between top (previous style) to sides (new\n # style). It modifies the place where the name of the variable appear.\n infoparser.add_argument('--legend-style', help=helpdict['legend-style'],\n type=str, choices=['sides', 'top'],\n default='sides')\n # -- width of gaussian smoothing for plotting posteriors,\n # in units of bin size, increase for smoother data.\n infoparser.add_argument('--gaussian-smoothing', help=helpdict['gaussian-smoothing'],\n type=float, default=0.5)\n # interpolation factor for plotting posteriors, 1 means no interpolation,\n # increase for smoother curves (it means that extra bins are created\n # and interpolated between computed bins)\n infoparser.add_argument('--interpolation-smoothing', help=helpdict['interpolation-smoothing'],\n type=int, default=4)\n # -- plot Fisher ellipses\n infoparser.add_argument('--plot-fisher', help=helpdict['plot-fisher'],\n dest='plot_fisher',action='store_true')\n infoparser.add_argument('--center-fisher', help=helpdict['center-fisher'],\n dest='center_fisher',action='store_true')\n\n infoparser.add_argument('--posterior-smoothing', help=helpdict['posterior-smoothing'],\n type=int, default=5)\n\n return parser","def setup_args() -> argparse.ArgumentParser:\n main_parser = argparse.ArgumentParser(prog=\"gh\")\n subparsers = main_parser.add_subparsers(dest=\"subparser\")\n command_parser = subparsers.add_parser(\"commands\", help=\"Runs a command\")\n command_parser.add_argument(\n \"choice\",\n help=\"The chosen command to run\",\n choices=gh.commands.OPTIONS.keys(),\n )\n analytics_parser = subparsers.add_parser(\"analytics\", help=\"Runs an analysis\")\n analytics_parser.add_argument(\n \"choice\",\n help=\"The chosen analysis to run\",\n choices=gh.analytics.OPTIONS.keys(),\n )\n return main_parser","def vultr_cli(parser):\n return parser","def parser(subparsers, repo):\n desc = 'show status of the repo'\n status_parser = subparsers.add_parser(\n 'status', help=desc, description=desc.capitalize(), aliases=['st'])\n status_parser.add_argument(\n 'paths', nargs='*', help='the specific path(s) to status',\n action=helpers.PathProcessor, repo=repo)\n status_parser.set_defaults(func=main)","def add_hic_interval_subparser(subparsers):\n parser = subparsers.add_parser(\"hic-interval\",\n help=\"HiFive Binning Function: Create a tabular interaction file containing data from a HiFive HiC project. Data are a genomic-interval format (chr1 start1 stop1 chr2 start2 stop2).\")\n parser.add_argument(\"-c\", \"--chromosome\", dest=\"chrom\", default=None, required=True, type=str,\n help=\"The chromosome from which to pull interaction data from.\")\n parser.add_argument(\"--chromosome2\", dest=\"chrom2\", default=None, required=False, type=str,\n help=\"The second chromosome from which to pull interaction data from if pulling trans data.\")\n parser.add_argument(\"-s\", \"--start\", dest=\"start\", default=None, required=False, type=int,\n help=\"The start coordinate of the pulled region to return. (None indicates the first valid bin on the chromosome) [default %(default)s]\")\n parser.add_argument(\"-e\", \"--stop\", dest=\"stop\", default=None, required=False, type=int,\n help=\"The stop coordinate of the pulled region to return. (None indicates the last valid bin on the chromosome) [default %(default)s]\")\n parser.add_argument(\"--start2\", dest=\"start2\", default=None, required=False, type=int,\n help=\"The start coordinate of the second chromosome pulled region to return. (None indicates the first valid bin on the chromosome) [default %(default)s]\")\n parser.add_argument(\"--stop2\", dest=\"stop2\", default=None, required=False, type=int,\n help=\"The stop coordinate of the second chromosome pulled region to return. (None indicates the last valid bin on the chromosome) [default %(default)s]\")\n parser.add_argument(\"-b\", \"--binsize\", dest=\"binsize\", default=10000, type=int,\n help=\"The size of bins, in base pairs, to group data into. [default: %(default)s]\")\n parser.add_argument(\"-m\", \"--max-distance\", dest=\"maxdist\", default=None, type=int,\n help=\"The maximum interaction distance to return (None indicates no maximum). [default: %(default)s]\")\n parser.add_argument(\"-d\", \"--data-type\", dest=\"datatype\", default=\"fend\",\n help=\"Which corrections (if any) to apply to counts. [default: %(default)s]\",\n choices=[\"raw\", \"fend\", \"distance\", \"enrichment\", \"expected\"])\n parser.add_argument(\"-M\", \"--matrix\", dest=\"matrix\", default=False, action=\"store_true\",\n help=\"Store output as a tab-separated matrix of values.\")\n parser.add_argument(\"-y\", \"--dynamically-bin\", dest=\"dynamic\", default=False, action=\"store_true\",\n help=\"Dynamically bin heatmap.\")\n parser.add_argument(\"-x\", \"--expansion-binsize\", dest=\"expbinsize\", default=10000, type=int,\n help=\"The size of bins, in base pairs, to group data into for expanding under-populated bins. [default: %(default)s]\")\n parser.add_argument(\"-f\", \"--minobservations\", dest=\"minobs\", default=20, type=int,\n help=\"The minimum number of observed reads in a bin for it to be considered valid. [default: %(default)s]\")\n parser.add_argument(\"-a\", \"--search-distance\", dest=\"search\", default=0, type=int,\n help=\"The furthest distance from the bin minpoint to expand bounds. If set to zero, there is no limit on expansion distance. [default: %(default)s]\")\n parser.add_argument(\"-v\", \"--remove-failed\", dest=\"remove\", default=False, action=\"store_true\",\n help=\"If a non-zero 'search-distance' is given, it is possible for a bin not to meet the 'minobservations' criteria before stopping looking. If this occurs and 'remove-failed' is set, the observed and expected values for that bin are zero.\")\n parser.add_argument(\"-i\", \"--image-file\", dest=\"image\", default=None, type=str,\n help=\"Save the data as an image to this file.\")\n parser.add_argument(\"-p\", \"--pdf\", dest=\"pdf\", default=False, action=\"store_true\",\n help=\"Format the image in PDF format. [default: %(default)s]\")\n parser.add_argument(\"-r\", \"--rotate\", dest=\"rotate\", default=False, action=\"store_true\",\n help=\"Rotate the plot 45 degrees (cis binned only). [default: %(default)s]\")\n parser.add_argument(\"-t\", \"--ticks\", dest=\"ticks\", default=False, action=\"store_true\",\n help=\"Add tick marks and labels to the plot (pdf format and binned only). [default: %(default)s]\")\n parser.add_argument(\"-l\", \"--legend\", dest=\"legend\", default=False, action=\"store_true\",\n help=\"Add color scale to the plot (pdf format only). [default: %(default)s]\")\n parser.add_argument(\"-k\", \"--keyword\", dest=\"keywords\", default=[], type=str, action='append',\n help=\"Additional keyword arguments to pass to plotting function.\")\n add_silent_argument(parser)\n parser.add_argument(dest=\"project\", type=str,\n help=\"The name of a HiFive HiC project file to pull data from.\")\n parser.add_argument(dest=\"output\", type=str,\n help=\"The name of the file to write HiC interval to.\")\n return","def get_parser():\n parser = ArgumentParser(description=__doc__,\n formatter_class=ArgumentDefaultsHelpFormatter,\n prog='pv2')\n subparsers = parser.add_subparsers(dest='cmd')\n # subparsers.add_parser('selfcheck',\n # add_help=False,\n # help=\"Self-check of the sst toolkit.\")\n # parser.add_argument('--version',\n # action='version',\n # version=('sst %s' % str(sst.__version__)))\n subparsers.add_parser('eval',\n add_help=False,\n parents=[evaluate.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Evaluate a single image\"))\n subparsers.add_parser('train',\n add_help=False,\n parents=[train.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Train a new model.\"))\n subparsers.add_parser('plot',\n add_help=False,\n parents=[plot.get_parser()],\n formatter_class=ArgumentDefaultsHelpFormatter,\n help=(\"Plot summary information.\"))\n return parser","def add_list_parser(subparsers):\n list_parser = subparsers.add_parser(\"list\")\n list_parser.set_defaults(func=list_cli.main)\n list_parser.add_argument('--scenarios', '-s',\n dest=\"scenarios\",\n action='store_true',\n help='List Scenarios')","def create_parser(general_defaults={}, constants={}, subcommand=MAIN):\n\n defaults = general_defaults['BigMLer']\n\n version = pkg_resources.require(\"BigMLer\")[0].version\n version_text = \"\"\"\\\nBigMLer %s - A Higher Level API to BigML's API\nCopyright 2012-2015 BigML\n\nLicensed under the Apache License, Version 2.0 (the \\\"License\\\"); you may\nnot use this file except in compliance with the License. You may obtain\na copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \\\"AS IS\\\" BASIS, WITHOUT\nWARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\nLicense for the specific language governing permissions and limitations\nunder the License.\"\"\" % version\n constants['version_text'] = version_text\n main_parser = argparse.ArgumentParser(\n description=\"A higher level API to BigML's API.\",\n epilog=\"Happy predictive modeling!\",\n version=version_text,\n formatter_class=argparse.RawTextHelpFormatter)\n subparsers = main_parser.add_subparsers()\n\n # list of options\n common_options = get_common_options(defaults=defaults, constants=constants)\n delete_options = get_delete_options(defaults=defaults)\n source_options = get_source_options(defaults=defaults)\n dataset_options = get_dataset_options(defaults=defaults)\n test_options = get_test_options(defaults=defaults)\n multi_label_options = get_multi_label_options(defaults=defaults)\n\n # subcommand options\n subcommand_options = {}\n # specific options\n subcommand_options[\"main\"] = get_main_options(defaults=defaults,\n constants=constants)\n # general options\n subcommand_options[\"main\"].update(common_options)\n subcommand_options[\"main\"].update(source_options)\n subcommand_options[\"main\"].update(dataset_options)\n subcommand_options[\"main\"].update(multi_label_options)\n subcommand_options[\"main\"].update(test_options)\n subcommand_options[\"main\"].update({\n '--source-tag': delete_options['--source-tag'],\n '--dataset-tag': delete_options['--dataset-tag'],\n '--model-tag': delete_options['--model-tag'],\n '--ensemble-tag': delete_options['--ensemble-tag'],\n '--prediction-tag': delete_options['--prediction-tag'],\n '--batch-prediction-tag': delete_options['--batch-prediction-tag']})\n\n main_options = subcommand_options[\"main\"]\n\n defaults = general_defaults[\"BigMLer analyze\"]\n subcommand_options[\"analyze\"] = get_analyze_options(defaults=defaults)\n subcommand_options[\"analyze\"].update(common_options)\n # we add the options that should be transmitted to bigmler main subcommands\n # in analyze\n subcommand_options[\"analyze\"].update({\n '--objective': main_options['--objective'],\n '--max-parallel-models': main_options['--max-parallel-models'],\n '--max-parallel-evaluations': main_options[\n '--max-parallel-evaluations'],\n '--model-fields': main_options['--model-fields'],\n '--balance': main_options['--balance'],\n '--no-balance': main_options['--no-balance'],\n '--number-of-models': main_options['--number-of-models'],\n '--sample-rate': main_options['--sample-rate'],\n '--replacement': main_options['--replacement'],\n '--missing-splits': main_options['--missing-splits'],\n '--pruning': main_options['--pruning'],\n '--weight-field': main_options['--weight-field'],\n '--replacement': main_options['--replacement'],\n '--objective-weights': main_options['--objective-weights'],\n '--replacement': main_options['--replacement'],\n '--model-attributes': main_options['--model-attributes'],\n '--ensemble-attributes': main_options['--ensemble-attributes'],\n '--tlp': main_options['--tlp'],\n '--randomize': main_options['--randomize']})\n\n defaults = general_defaults[\"BigMLer cluster\"]\n subcommand_options[\"cluster\"] = get_cluster_options(defaults=defaults)\n # general options\n subcommand_options[\"cluster\"].update(common_options)\n subcommand_options[\"cluster\"].update(source_options)\n subcommand_options[\"cluster\"].update(dataset_options)\n subcommand_options[\"cluster\"].update(test_options)\n subcommand_options[\"cluster\"].update({\n '--cpp': main_options['--cpp'],\n '--fields-map': main_options['--fields-map'],\n '--source-tag': delete_options['--source-tag'],\n '--dataset-tag': delete_options['--dataset-tag'],\n '--cluster-tag': delete_options['--cluster-tag'],\n '--centroid-tag': delete_options['--centroid-tag'],\n '--batch-centroid-tag': delete_options['--batch-centroid-tag'],\n '--prediction-info': main_options['--prediction-info'],\n '--prediction-header': main_options['--prediction-header'],\n '--prediction-fields': main_options['--prediction-fields'],\n '--reports': main_options['--reports'],\n '--remote': main_options['--remote'],\n '--no-batch': main_options['--no-batch']})\n\n defaults = general_defaults[\"BigMLer anomaly\"]\n subcommand_options[\"anomaly\"] = get_anomaly_options(defaults=defaults)\n # general options\n subcommand_options[\"anomaly\"].update(common_options)\n subcommand_options[\"anomaly\"].update(source_options)\n subcommand_options[\"anomaly\"].update(dataset_options)\n subcommand_options[\"anomaly\"].update(test_options)\n subcommand_options[\"anomaly\"].update({\n '--cpp': main_options['--cpp'],\n '--fields-map': main_options['--fields-map'],\n '--source-tag': delete_options['--source-tag'],\n '--dataset-tag': delete_options['--dataset-tag'],\n '--anomaly-tag': delete_options['--anomaly-tag'],\n '--anomaly-score-tag': delete_options['--anomaly-score-tag'],\n '--batch-anomaly-score-tag': delete_options['--batch-anomaly-score-tag'],\n '--prediction-info': main_options['--prediction-info'],\n '--prediction-header': main_options['--prediction-header'],\n '--prediction-fields': main_options['--prediction-fields'],\n '--reports': main_options['--reports'],\n '--remote': main_options['--remote'],\n '--no-batch': main_options['--no-batch']})\n\n\n subcommand_options[\"delete\"] = delete_options\n subcommand_options[\"delete\"].update(common_options)\n\n for subcommand in SUBCOMMANDS:\n subparser = subparsers.add_parser(subcommand)\n parser_add_options(subparser, subcommand_options[subcommand])\n\n # options to be transmitted from analyze to main\n chained_options = [\n \"--debug\", \"--dev\", \"--username\", \"--api-key\", \"--resources-log\",\n \"--store\", \"--clear-logs\", \"--max-parallel-models\",\n \"--max-parallel-evaluations\", \"--objective\", \"--tag\",\n \"--no-tag\", \"--no-debug\", \"--no-dev\", \"--model-fields\", \"--balance\",\n \"--verbosity\", \"--resume\", \"--stack_level\", \"--no-balance\",\n \"--args-separator\", \"--name\"]\n\n return main_parser, chained_options","def add_rm_parser(subparsers):\n rm_parser = subparsers.add_parser(\"rm\")\n rm_parser.set_defaults(func=rm_cli.main)\n rm_parser.add_argument('--scenario', '-s',\n dest=\"scenario\",\n help='Predefined scenario to use for exection')\n rm_parser.add_argument('--platform', dest=\"platform\",\n help=\"The platform to use \\\n(podman, docker, terraform, shell, python)\")\n rm_parser.add_argument('--vars', dest=\"vars\",\n default=\"\",\n help=\"extra variables\")\n rm_parser.add_argument('--debug', dest=\"debug\",\n action=\"store_true\",\n help=\"Enable debug level logging\")","def generate_main_parser() -> ArgumentParser:\n # Create parser\n parser = ArgumentParser(\n description=\"Command line interface tool for iic2343.\",\n )\n\n # Add version flag\n parser.add_argument(\n \"-v\",\n \"--version\",\n action=\"version\",\n version=f\"iic2343 version {iic2343.__version__}\",\n )\n\n # Create subparsers\n subparsers = parser.add_subparsers(help=\"Action to be executed.\")\n\n # Serial ports subparser\n generate_serial_ports_subparser(subparsers)\n\n return parser","def subdivide_parser(subparser):\n parser = subparser.add_parser('subdivide',\\\n help='will subdivide a mesh by one iteration '\\\n '(splitting all triangles in four others)'\\\n ' deprecated use converter.py refine instead')\n parser = add_arg(parser, 'replace')\n parser.add_argument(\"args\", metavar='SELAFIN file', nargs=\"+\")\n\n return subparser","def build_parser(self, add_help=True):\n self.parser = argparse.ArgumentParser(\n description=self.description, add_help=add_help\n )\n self.parser.prog = f\"python -m {self.package}.{self.module_name}\"\n self.parser.add_argument(\n \"config_file\", help=\"Path/name of YAML configuration file for NEMO nowcast.\"\n )","def add_command(subparsers):\n\n parser = subparsers.add_parser('create', help=create.__doc__)\n\n parser.add_argument('-r', '--recreate', action='store_true', help='If set, I\\'ll first erase the current database')\n parser.add_argument('-v', '--verbose', action='count', help='Increase verbosity?')\n parser.add_argument('-d', '--image-dir', default='/idiap/project/hface/databases/polimetric_thermal_database/Registered/', help=\"Change the relative path to the directory containing the images of the Polarimetric database.\")\n\n parser.set_defaults(func=create) #action","def _spawn_cli() -> GooeyParser:\n parser = _add_gui_arg(_cli())\n\n return vars(parser.parse_args())","def _cli() -> GooeyParser:\n parser = GooeyParser(description='A template CLI app with optional GUI.')\n\n return parser","def get_parser(subparsers):\n parser = subparsers.add_parser(\"compile\", description=\"Compile model using ML on MCU flow.\")\n parser.set_defaults(flow_func=handle)\n add_compile_options(parser)\n return parser","def internal_setup_options_parser(self, argparser):\n self.setup_options_parser(argparser)\n subparser = argparser.add_subparsers(dest='verb')\n for (name, verb) in self.verbs.items():\n vparser = subparser.add_parser(name, help=verb.help)\n verb.setup_options_parser(vparser)","def __init__(self, *args, **kwargs):\n # The subcommand array, with the help command included.\n self.subcommands = list(kwargs.pop('subcommands', []))\n self.subcommands.append(self._HelpSubcommand)\n\n # A more helpful default usage.\n if 'usage' not in kwargs:\n kwargs['usage'] = \"\"\"\n %prog [global options] COMMAND [ARGS...]\n %prog help COMMAND\"\"\"\n\n # Super constructor.\n optparse.OptionParser.__init__(self, *args, **kwargs)\n\n # Adjust the help-visible name of each subcommand.\n for subcommand in self.subcommands:\n subcommand.parser.prog = '%s %s' % \\\n (self.get_prog_name(), subcommand.name)\n\n # Our root parser needs to stop on the first unrecognized argument.\n self.disable_interspersed_args()","def add_arguments(self, sub_parser):\n sp = sub_parser\n sp.add_argument('--fork', nargs='?')","def get_parser(subparsers):\n parser = subparsers.add_parser(\"models\", description=\"Manage ML on MCU models.\")\n parser.set_defaults(func=handle)\n add_common_options(parser)\n add_context_options(parser)\n add_models_options(parser)\n return parser","def add_command_parsers(parser, logparser):\n subparsers = parser.add_subparsers(metavar='Command')\n help_text = 'ONE OF THE FOLLOWING:\\n'\n available_commands = find_commands(\n os.path.join(os.path.abspath(os.path.dirname(__file__)), os.path.pardir, 'functions'))\n max_length = max([len(a) for a in available_commands])\n for command in available_commands:\n child_parser = subparsers.add_parser(command, parents=[logparser])\n call = importlib.import_module('functions.%s'% command)\n if hasattr(call, 'set_argparser'):\n call.set_argparser(child_parser)\n else:\n child_parser.description = 'Description is missing'\n help_text += command + \": \" + \" \"*(max_length-len(command)) + ('\\n'+' '*(max_length+2)\n ).join(textwrap.wrap(child_parser.description,60)) + '\\n'\n child_parser.set_defaults(func=call.main)\n subparsers.help = help_text + '\\nType \"Command --help\" for more information about given command'","def make_parser():\n\n parser = argparse.ArgumentParser(description='Inference engine.')\n subparsers = parser.add_subparsers(dest=\"subcommand\")\n subparsers.required = True\n solver_subparser = subparsers.add_parser('run')\n solver_subparser.add_argument(\n '-v', '--verbose', help='enable verbose mode.', action='store_true'\n )\n solver_subparser.add_argument(\n '-d', '--debug', help='enable debug mode.', action='store_true'\n )\n solver_subparser.add_argument(\n 'filename', type=str,\n help='filename containing the instructions to process.'\n )\n return parser","def get_argument_parser(self):\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(dest='command')\n fetch_parser = subparsers.add_parser('fetch', help='fetches and displays a release from discogs')\n fetch_parser.add_argument('discogs_id', help='the ID of the release')\n rip_parser = subparsers.add_parser('rip', help='rips the current CD to WAV')\n rip_parser.add_argument('--destination', help='optional destination for the CD rip')\n search_parser = subparsers.add_parser(\n 'search',\n prog='search',\n help='performs a very simple search on discogs')\n search_parser.add_argument('term', help='the term to search for')\n encode_parser = subparsers.add_parser(\n 'encode', help='Encodes a CD or a set of WAV files to mp3.')\n encode_parser.add_argument(\n 'encoding_from', choices=['cd', 'wav'], help='The source to encode from.')\n encode_parser.add_argument(\n 'encoding_to', choices=['mp3', 'flac'], help='The destination to encode to.')\n encode_parser.add_argument(\n '--source', help='The destination of the source wav file. This can be a file or directory.')\n encode_parser.add_argument(\n '--destination', help='The destination of the resulting mp3 or flac. This can be a file or directory.')\n encode_parser.add_argument(\n '--keep-source', action='store_true', help='If encoding from wav, use this to keep the original wav being removed.')\n encode_parser.add_argument(\n '--collapse-index-tracks', action='store_true', help='If set this will collapse any subtracks to a single track.')\n encode_parser.add_argument(\n '--discogs-id', help='The discogs ID for the release. When this is used metadata from the discogs release will be applied to the encoded files.')\n decode_parser = subparsers.add_parser('decode', help='Decodes a set of FLAC or MP3 files to WAV.')\n decode_parser.add_argument(\n 'decode_from', choices=['flac', 'mp3'], help='The source to decode from.')\n decode_parser.add_argument(\n '--source', help='The destination of the source file. This can be a file or directory.')\n decode_parser.add_argument(\n '--destination', help='The destination of the resulting wav. This can be a file or directory.')\n tag_parser = subparsers.add_parser('tag', help='Tags an audio file')\n tag_parser.add_argument(\n 'action', choices=['add', 'remove'], help='The tagging action to be performed. A tag can be added or removed.')\n tag_parser.add_argument(\n 'format', choices=['mp3', 'flac'], help='The file format of the audio file being tagged.')\n tag_parser.add_argument(\n '--collapse-index-tracks', action='store_true', help='If set this will collapse any subtracks to a single track.')\n tag_parser.add_argument(\n '--source',\n help='The source audio files to tag. This can be a file or a directory. If the source is omitted, the files in the current working directory will be used.')\n tag_parser.add_argument('--discogs-id', help='The discogs ID for the release. When this is used metadata from the discogs release will be applied to the tagged files.')\n tag_parser.add_argument('--artist', help='The artist to use for the tag.')\n tag_parser.add_argument('--album-artist', help='The album artist to use for the tag.')\n tag_parser.add_argument('--album', help='The album to use for the tag.')\n tag_parser.add_argument('--title', help='The title to use for the tag.')\n tag_parser.add_argument('--year', help='The year to use for the tag.')\n tag_parser.add_argument('--genre', help='The year to use for the tag.')\n tag_parser.add_argument('--track-number', help='The track number to use for the tag.')\n tag_parser.add_argument('--track-total', help='The track total to use for the tag.')\n tag_parser.add_argument('--disc-number', help='The disc number to use for the tag.')\n tag_parser.add_argument('--disc-total', help='The disc total to use for the tag.')\n tag_parser.add_argument('--comment', help='The comment for the tag.')\n artwork_parser = subparsers.add_parser('artwork', help='adds or removes artwork from a file')\n artwork_parser.add_argument(\n 'action', choices=['add', 'remove'], help='The artwork action to be performed. The artwork can be added or removed.')\n artwork_parser.add_argument(\n 'type', choices=['mp3', 'flac'], help='The type of file to apply the artwork to.')\n artwork_parser.add_argument(\n '--source', help='The destination file or directory to apply the artwork to. If there is no source then any artwork in the current directory will be used.')\n artwork_parser.add_argument(\n '--destination', help='The destination file or directory to apply the artwork to. If there is no destination then the current directory will be used.')\n mix_parser = subparsers.add_parser('mix', help='adds a mix')\n mix_parser.add_argument('source', help='the source of the mix')\n mix_parser.add_argument('--artist', help='The artist to use for the tag.')\n mix_parser.add_argument('--album', help='The album to use for the mix.')\n mix_parser.add_argument('--title', help='The title to use for the mix.')\n mix_parser.add_argument('--year', help='The year to use for the mix.')\n mix_parser.add_argument('--comment', help='The comment for the mix.')\n return parser","def add_generate_token_subcommand(\n subparsers: Any,\n) -> None:\n generate_token_sp = subparsers.add_parser(\n \"generate-token\",\n formatter_class=Formatter,\n description=dedent( # noqa: WPS462 -- docs\n \"\"\"\\\n Generate token.\n\n Token is required to consume the protected endpoints.\n\n Example:\n ```shell\n # Generate a rsa key pair\n openssl genpkey -algorithm RSA -out private_key.pem \\\\\n -pkeyopt rsa_keygen_bits:2048\n openssl rsa -pubout -in private_key.pem -out public_key.pem\n # Generate token\n bartender generate-token --format header > token.txt\n # Use token\n curl -X 'GET' \\\\\n 'http://127.0.0.1:8000/api/whoami' \\\\\n -H 'accept: application/json' \\\\\n -H @token.txt | jq .\n ```\n \"\"\",\n ),\n help=\"Generate token.\",\n )\n generate_token_sp.add_argument(\n \"--private-key\",\n default=Path(\"private_key.pem\"),\n type=Path,\n help=\"Path to RSA private key file\",\n )\n generate_token_sp.add_argument(\n \"--username\",\n default=\"someone\",\n help=\"Username to use in token\",\n )\n generate_token_sp.add_argument(\n \"--roles\",\n nargs=\"+\",\n default=[\"expert\", \"guru\"],\n help=\"Roles to use in token\",\n )\n onehour_in_minutes = 60\n generate_token_sp.add_argument(\n \"--lifetime\",\n default=onehour_in_minutes,\n type=int,\n help=\"Lifetime of token in minutes\",\n )\n generate_token_sp.add_argument(\n \"--issuer\",\n default=\"bartendercli\",\n help=\"Issuer of token\",\n )\n generate_token_sp.add_argument(\n \"--oformat\",\n default=\"plain\",\n choices=[\"header\", \"plain\"],\n help=\"Format of output\",\n )\n generate_token_sp.set_defaults(func=generate_token_subcommand)","def setup_parser():\r\n parser = argparse.ArgumentParser(description='Freeseer Recording Utility',\r\n formatter_class=argparse.RawTextHelpFormatter)\r\n parser.add_argument(\"-v\", \"--version\", action='version',\r\n version=textwrap.dedent('''\\\r\n Freeseer {version} ({platform})\r\n Python {pymajor}.{pyminor}.{pymicro}\r\n PyGst {pygst_version}\r\n PyQt {pyqt_version}\r\n Qt {qt_version}\r\n Yapsy {yapsy_version}\r\n '''.format(version=__version__,\r\n platform=sys.platform,\r\n pymajor=sys.version_info.major,\r\n pyminor=sys.version_info.minor,\r\n pymicro=sys.version_info.micro,\r\n pygst_version=pygst._pygst_version,\r\n pyqt_version=QtCore.PYQT_VERSION_STR,\r\n qt_version=QtCore.QT_VERSION_STR,\r\n yapsy_version=yapsy.__version__)))\r\n\r\n # Configure Subparsers\r\n subparsers = parser.add_subparsers(dest='app', help='Command List')\r\n setup_parser_record(subparsers)\r\n setup_parser_config(subparsers)\r\n setup_parser_talk(subparsers)\r\n setup_parser_report(subparsers)\r\n setup_parser_upload(subparsers)\r\n return parser","def setup_argparse():\n prsr = argparse.ArgumentParser( description=__description__,\n version=__version__ )\n subprsrs = prsr.add_subparsers(dest='subparser_name')\n \n sub_export = subprsrs.add_parser(prsr_EXPORT,description=prsr_EXPORT_desc,\n help=prsr_EXPORT_help)\n sub_update = subprsrs.add_parser(prsr_UPDATE, description=prsr_UPDATE_desc,\n help=prsr_UPDATE_help)\n sub_script= subprsrs.add_parser(prsr_SCRIPT,description=prsr_SCRIPT_desc,\n help=prsr_SCRIPT_help) \n \n # toplevel options \n # --debug turn on debug logging.\n # --warnings let warnings be logged too, errors are the only thing logged by default.\n # --logfile set the path to the log file.\n prsr.add_argument('--debug', action='store_true',help=\"Turn on debug logging.\",dest='log_debug')\n prsr.add_argument('--warnings',action='store_true',help=\"Let warnings be logged too, errors are the only thing logged by default.\",dest='log_warnings')\n prsr.add_argument('--logfile', default=\"errors.log\",help=\"Set the path to the log file.\",dest='log_path')\n prsr.add_argument('--save', metavar=\"path\",help=\"Set the path to where the current query can be saved.\",dest='script_save_path')\n prsr.add_argument('--time', action='store_true',help=\"LiVSs will report how long it took to run your command.\",dest='run_timer')\n \n #export subcommand\n # input the file/directory that should be the target of an export\n # output the file/directory that should be the location of said export\n #\n # Subcommands of Export\n # util options for dumping utility files from the input target\n # translator options for dumping a translator file from the input target\n #\n esubprsrs = sub_export.add_subparsers(dest='subsubparser_name')\n util_sub_parse = esubprsrs.add_parser(subprsr_UTIL, description=subprsr_UTIL_desc, \n help=subprsr_UTIL_help)\n translator_sub_parse = esubprsrs.add_parser(subprsr_TRANSLATOR,description=subprsr_TRANSLATOR_desc,\n help=subprsr_TRANSLATOR_help)\n sub_export.add_argument('-l','--level', choices=['sys','proj','lang'], help=\"set the level to build the export to, read the HOWTO if you don't know.\",dest='export_level')\n sub_export.add_argument('--mem', action='store_true', help=\"Keep everything in memory and only saves the level specified, generates new files.\",dest='export_mem')\n sub_export.add_argument('--keep', action='store_true', help=\"Use what was currently generated in the input path, don't generate new files.\",dest='export_existing')\n sub_export.add_argument('input', help='the file/directory that should be the target of an export')\n sub_export.add_argument('output', help='the file/directory that should be the location of said export')\n \n # export util subcommand\n # -m, --menus export the menus out of the target\n # -d, --dialogs export the dialogs out of a target\n # -s, --strings export the string tables out of a target\n # -a, --all export all utils from a target (output must be a directory)\n util_sub_parse.add_argument('-m','--menus', action='store_true', help='export the menus out of the target',dest='export_util_menus')\n util_sub_parse.add_argument('-d','--dialogs', action='store_true', help='export the dialogs out of a target',dest='export_util_dialogs')\n util_sub_parse.add_argument('-s','--strings', action='store_true', help='export the string tables out of a target',dest='export_util_strings')\n util_sub_parse.add_argument('-a','--all', action='store_true', help='export all utils from a target (output must be a directory)',dest='export_util_all')\n \n # export translator subcommand\n # -s, --sort Sort the list of strings alphabetically\n # -c, --langcodes Provide a list of language codes you want in the translator file\n # -p, --prune Takes a path to a file that has a list of strings that shouldn't be in the translation file\n translator_sub_parse.add_argument('-s','--sort', action='store_true', help=\"Sort the list of strings alphabetically\", dest='export_translator_order')\n translator_sub_parse.add_argument('-c','--langcodes', metavar='code', nargs='+', help=\"Provide a list of language codes you want in the translator file\",dest='export_translator_langcodes')\n translator_sub_parse.add_argument('-p','--prune', metavar='path', nargs=1, help=\"Takes a path to a file that has a list of strings that shouldn't be in the translation file\",dest='export_translator_prunepath')\n translator_sub_parse.add_argument('-m','--mark', action='store_true', help=\"Highlights possible conflicts in the new translation file\", dest='export_translator_markconflicts')\n\n #update subcommand\n # -t, --translator Location of input translator file\n # -d, --dialogs Location of input dialogs file\n # -m, --menus Location of input menus file\n # -s, --strings Location of input string table file\n # -c, --langcode Limit what lang code in the input file gets updated, this can be a list.\n # -o, --outtype What should we be updating? (sys, proj, rcs)\n # -n, --new Generates new files for whatever outtype is set to (cannot be rcs).\n # output The directory/file to update\n locations = sub_update.add_mutually_exclusive_group(required=True)\n locations.add_argument('-t','--translator',metavar='path',help='Location of input translator file',dest='update_translator')\n locations.add_argument('-d','--dialogs',metavar='path',help='Location of input dialogs file',dest='update_dialogs')\n locations.add_argument('-m','--menus',metavar='path',help='Location of input menus file',dest='update_menus')\n locations.add_argument('-s','--strings',metavar='path',help='Location of input string table file',dest='update_strings')\n sub_update.add_argument('-c','--langcode', metavar='code', nargs='+', help=\"Limit what lang code in the input file gets updated, this can be a list.\", dest='update_langcodes')\n sub_update.add_argument('-o','--outtype', metavar='type', choices=['sys','proj','rcs'], help='What should we be updating?', dest='update_to')\n sub_update.add_argument('-n','--new',action='store_true', help='Generates new files for whatever outtype is set to (cannot be rcs).', dest='update_new')\n sub_update.add_argument('output',help='The directory/file to update')\n \n #script subcommand\n # --dump instead of reading in a file, all default configurations are dumped to the given file\n # path takes a path to a file to run it. For more information on scripting, see the howto file.\n sub_script.add_argument(\"--dump\", action=\"store_true\", help=\"instead of reading in a file, all default configurations are dumped to the given file\")\n sub_script.add_argument(\"input\", help=\"takes a path to a headshot script to run it.\")\n \n return prsr","def __init__(self, subparsers, region_arg: bool = True, expects_extra_args: bool = False, **argparse_kwargs):\n parser_name = argparse_kwargs.pop(\"name\")\n parser = subparsers.add_parser(parser_name, **argparse_kwargs)\n parser.add_argument(\"--debug\", action=\"store_true\", help=\"Turn on debug logging.\", default=False)\n if region_arg:\n parser.add_argument(\"-r\", \"--region\", help=\"AWS Region this operation corresponds to.\")\n self.register_command_args(parser)\n parser.set_defaults(func=self.execute, expects_extra_args=expects_extra_args)","def make_cli_parser(self):\n super(BplnArgParser, self).make_cli_parser()\n links_opt = self.cli_parser.add_option('--selected-links',\n help=(\"A CSV-formatted file containing pairs of \"\n \"terms to test. Tests will be done to decide \"\n \"if the annotation term from the first column \"\n \"\\\"is linked to\\\" the annotation term from the \"\n \"second column. [NOTE: Selecting this option \"\n \"restricts the program to only test the matches \"\n \"designated in the file.] [NOTE: This option \"\n \"conflicts with '--selected-terms' and \"\n \"'--selected-terms-with-all'.]\"\n )\n )\n anns_opt = self.cli_parser.add_option('--selected-terms',\n help=(\"A file containing annotation terms to test \"\n \"linkage to each other. The file should contain one \"\n \"term per line. Selecting this option restricts the \"\n \"program to only testing the given terms against \"\n \"each other. [NOTE: This option conflicts with \"\n \"'--selected-links' and \"\n \"'--selected-terms-with-all'.]\"\n )\n )\n anns_all_opt = self.cli_parser.add_option(\n '--selected-terms-with-all',\n help=(\"A file containing annotation terms to test \"\n \"linkage to all other terms (one-against-all and \"\n \"all-against-one). The file should contain one \"\n \"term per line. Selecting this option restricts \"\n \"the program to only testing the given terms \"\n \"against all other terms. [NOTE: \"\n \"This option conflicts with '--selected-links' and \"\n \"'--selected-terms'.]\"\n )\n )\n self.cli_parser.register_conflict(\n (links_opt, anns_opt, anns_all_opt))"],"string":"[\n \"def extend_cli(self, subparser):\",\n \"def add(cls, subparsers):\\n subparser = subparsers.add_parser(\\n name=cls.__tool_name__(),\\n description=cls.__get_description__())\\n\\n cls.__add_arguments__(subparser)\\n subparser.set_defaults(func=cls.from_args)\\n return subparser\",\n \"def refine_cli(\\n subparsers: SubParsersAction,\\n parent_parsers: List[argparse.ArgumentParser],\\n) -> None:\",\n \"def add_subcommand(\\n subp: _SubParsersAction, name: str, description: Optional[str] = None, **kwargs\\n ) -> \\\"CommandParser\\\":\\n desc_help = {\\\"description\\\": description, \\\"help\\\": description}\\n return subp.add_parser(name, **desc_help, **kwargs)\",\n \"def add_argparse_subparser(subparsers):\\n\\n new_sub_parser = subparsers.add_parser(\\n PluginManager.argparse_subparser_name(), help=\\\"plugin commands\\\"\\n )\\n PluginManager.__argparse_subparser = new_sub_parser\\n plugin_subparsers = new_sub_parser.add_subparsers(\\n dest=PluginManager.__root_subparser_name\\n )\\n\\n sub_sub_parser = plugin_subparsers.add_parser(\\n \\\"list\\\", help=\\\"list the available plugins\\\"\\n )\\n sub_sub_parser.add_argument(\\n \\\"--all\\\",\\n dest=\\\"show_all\\\",\\n action=\\\"store_true\\\",\\n default=False,\\n help=\\\"show all loaded plugins (default is False)\\\",\\n )\\n sub_sub_parser.add_argument(\\n dest=\\\"list_filter\\\",\\n default=None,\\n help=\\\"filter\\\",\\n nargs=\\\"?\\\",\\n type=PluginManager.__list_filter_type,\\n )\\n sub_sub_parser = plugin_subparsers.add_parser(\\n \\\"info\\\", help=\\\"information on a specific plugin\\\"\\n )\\n sub_sub_parser.add_argument(\\n dest=\\\"info_filter\\\",\\n default=None,\\n type=PluginManager.__info_filter_type,\\n help=\\\"an id\\\",\\n )\",\n \"def extend_cli(self, root_subparsers):\\n\\n user_dict = {}\\n if self.add_base_groups:\\n user_dict = dict(shared_groups=SHARED_GROUPS)\\n\\n self.specification = SpecParser.from_plugin(\\n subparser=root_subparsers,\\n plugin=self.plugin,\\n base_groups=user_dict)\",\n \"def _add_to_cli(self, parser, group=None):\\n dest = self.dest\\n if group is not None:\\n dest = group.name + '_' + dest\\n\\n subparsers = parser.add_subparsers(dest=dest,\\n title=self.title,\\n description=self.description,\\n help=self.help)\\n # NOTE(jd) Set explicitly to True for Python 3\\n # See http://bugs.python.org/issue9253 for context\\n subparsers.required = True\\n\\n if self.handler is not None:\\n self.handler(subparsers)\",\n \"def addSubParser( parentParser, cmdName ) :\\n parser = parentParser.add_parser( cmdName, help='Generate a new UBOS package scaffold.' )\\n parser.add_argument( '--directory', required=True, help='Directory where to create the package scaffold')\\n parser.add_argument( '--template', required=True, help='Name of the template to use' )\\n parser.add_argument( '--json', required=False, help='Settings file' )\",\n \"def add_subparser(sp, name, **kwargs):\\n kwargs[\\\"add_help\\\"] = False\\n kwargs['formatter_class'] = ap.ArgumentDefaultsHelpFormatter\\n sparser = sp.add_parser(name, **kwargs)\\n\\n sparser.add_argument(\\\"-h\\\", \\\"--help\\\", action=custom_help(),\\n help=\\\"print the short or long help\\\")\\n\\n return sparser\",\n \"def add_cli(self, subparser):\\n new_parser = subparser.add_parser('create', help='create new scratch file')\\n new_parser.add_argument('name', nargs='?', default=None, help=\\\"Optional Name to be given to the file, \\\"\\n \\\"default name is an increment of 'scratch##'\\\")\\n new_parser.set_defaults(func=self.action)\\n return subparser\",\n \"def setup_subparser(name, description, commands):\\n subparser = SUBPARSER.add_parser(\\n name,\\n help=description\\n )\\n subparser.add_argument(\\n 'sub_command',\\n metavar='sub_command',\\n type=str,\\n nargs='+',\\n help='Which command to run. Options: %s' % ', '.join(commands),\\n choices=commands\\n )\\n\\n return subparser\",\n \"def create_parser(self, prog_name, subcommand):\\n parser = CommandParser(\\n self, prog=\\\"%s %s\\\" % (os.path.basename(prog_name), subcommand),\\n description=self.help or None,\\n )\\n parser.add_argument(\\n '--pythonpath',\\n help='A directory to add to the Python path',\\n )\\n self.add_arguments(parser)\\n return parser\",\n \"def add_subparser(subparsers):\\n parser = subparsers.add_parser('run', help=\\\"run artifact\\\")\\n parser.add_argument('run_config', default='cps.ini',\\n help=\\\"run configuration file\\\")\\n parser.add_argument('-p', '--persist', action=\\\"store_true\\\", default=False,\\n help=\\\"to persist data, dont delete sandbox after use\\\")\\n\\n parser.set_defaults(func=main)\",\n \"def create_parser(self, prog_name, subcommand):\\r\\n self.prog_name = \\\"{} {}\\\".format(prog_name, subcommand)\\r\\n return super(TrackedCommand, self).create_parser(prog_name, subcommand)\",\n \"def customize_subparser(cls, subparser: argparse.ArgumentParser) -> None:\\n pass\",\n \"def subparser( parser, subparsers ):\",\n \"def SubParser(self):\\n if not self._sub_parser:\\n # pylint: disable=protected-access\\n self._sub_parser = self._parser.add_subparsers(\\n action=parser_extensions.CloudSDKSubParsersAction,\\n calliope_command=self)\\n return self._sub_parser\",\n \"def add_subcommands(cls, parser: argparse.ArgumentParser) -> None:\\n if cls.SUBCOMMANDS:\\n subparsers = parser.add_subparsers(title=\\\"subcommands\\\", metavar=\\\"\\\", dest='cmd')\\n for subcmd_class in cls.SUBCOMMANDS:\\n parsers = subcmd_class.get_args()\\n subcmd_class.parser = parsers[-1]\\n\\n subparser = subparsers.add_parser(\\n subcmd_class.NAMES[0],\\n aliases=subcmd_class.NAMES[1:],\\n parents=parsers,\\n help=subcmd_class.HELP,\\n epilog=subcmd_class.EPILOG)\\n subparser.set_defaults(command_class=subcmd_class)\\n subcmd_class.customize_subparser(subparser)\",\n \"def create_parser(self, prog_name, subcommand):\\r\\n # Hack __main__ so --help in dev_appserver_main works OK.\\r\\n sys.modules['__main__'] = dev_appserver_main\\r\\n return super(Command, self).create_parser(prog_name, subcommand)\",\n \"def get_parser(subparsers, parent=None):\\n parser = subparsers.add_parser(\\n \\\"flow\\\",\\n description=\\\"Invoke ML on MCU flow\\\",\\n parents=[parent] if parent else [],\\n add_help=(parent is None),\\n )\\n parser.set_defaults(func=handle)\\n add_common_options(parser)\\n add_context_options(parser)\\n add_flow_options(parser)\\n subparsers = parser.add_subparsers(dest=\\\"subcommand2\\\") # this line changed\\n load.get_parser(subparsers)\\n tune.get_parser(subparsers)\\n build.get_parser(subparsers)\\n compile_.get_parser(subparsers)\\n run.get_parser(subparsers)\",\n \"def add_subparser(subparsers):\\n parser = subparsers.add_parser(\\\"utils/update\\\",\\n description=\\\"Update apt and the groot_ansible ecosystem\\\", # this shows in the help for this command\\n help=\\\"update your ansible/apt environment\\\", # this shows in the parent parser\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter\\n )\\n common.add_ansible_arguments(parser)\\n parser.set_defaults(func=parse_args)\",\n \"def _add_to_cli(self, parser, group=None):\\n container = self._get_argparse_container(parser, group)\\n kwargs = self._get_argparse_kwargs(group)\\n prefix = self._get_argparse_prefix('', group.name if group else None)\\n deprecated_names = []\\n for opt in self.deprecated_opts:\\n deprecated_name = self._get_deprecated_cli_name(opt.name,\\n opt.group)\\n if deprecated_name is not None:\\n deprecated_names.append(deprecated_name)\\n self._add_to_argparse(parser, container, self.name, self.short,\\n kwargs, prefix,\\n self.positional, deprecated_names)\",\n \"def fill_subparsers(subparsers):\\n\\n\\t\\tfor cls in BaseCommand.__subclasses__():\\n\\t\\t\\tcls.add_subparser(subparsers)\",\n \"def init_parser(subparsers):\\n parser = subparsers.add_parser(COMMAND, help=\\\"Add a new task to the task list\\\")\\n parser.add_argument(\\\"title\\\", type=str, help=\\\"The title of the new task\\\")\\n parser.add_argument(\\\"description\\\", type=str, help=\\\"The description of the new task\\\")\\n doto.cli.cmd.task.init_task_flags(parser)\",\n \"def create_parser():\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers(title=\\\"Commands\\\", dest=\\\"subparser_name\\\")\\n subparsers.add_parser(\\\"generate-settings\\\", help=\\\"Generate settings.json to install \\\"\\n \\\"Gluu Cloud Native Edition non-interactively\\\")\\n subparsers.add_parser(\\\"install\\\", help=\\\"Install Gluu Cloud Native Edition using Kustomize. Depreciated > 4.3.\\\")\\n subparsers.add_parser(\\\"install-no-wait\\\", help=\\\"Install Gluu Cloud Native Edition using Kustomize. \\\"\\n \\\"Depreciated > 4.3. \\\"\\n \\\"There will be no wait time between installing services. \\\"\\n \\\"Pods may look like they are restarting but they will \\\"\\n \\\"be waiting for hierarchy \\\"\\n \\\"pods to be running\\\")\\n subparsers.add_parser(\\\"install-ldap-backup\\\", help=\\\"Install ldap backup cronjob only.\\\")\\n subparsers.add_parser(\\\"restore\\\", help=\\\"Install Gluu Cloud Native Edition with a \\\"\\n \\\"running database and previous configuration using Kustomize.\\\"\\n \\\"Depreciated > 4.3\\\")\\n subparsers.add_parser(\\\"uninstall\\\", help=\\\"Uninstall Gluu that was installed using Kustomize. \\\"\\n \\\"Depreciated > 4.3\\\")\\n subparsers.add_parser(\\\"upgrade\\\", help=\\\"Upgrade Gluu Cloud Native Edition using Kustomize. Depreciated > 4.3\\\")\\n subparsers.add_parser(\\\"upgrade-values-yaml\\\", help=\\\"Upgrade Gluu Cloud Native Edition\\\")\\n subparsers.add_parser(\\\"install-couchbase\\\", help=\\\"Install Couchbase only. Used with installation of Gluu with Helm\\\")\\n subparsers.add_parser(\\\"install-couchbase-backup\\\", help=\\\"Install Couchbase backup only.\\\")\\n subparsers.add_parser(\\\"uninstall-couchbase\\\", help=\\\"Uninstall Couchbase only.\\\")\\n subparsers.add_parser(\\\"helm-install\\\", help=\\\"Install Gluu Cloud Native Edition using helm. \\\"\\n \\\"This also installs the nginx-ingress chart\\\")\\n subparsers.add_parser(\\\"helm-uninstall\\\", help=\\\"Uninstall Gluu Cloud Native Edition using helm. \\\"\\n \\\"This also uninstalls the nginx-ingress chart\\\")\\n\\n subparsers.add_parser(\\\"helm-install-gluu\\\", help=\\\"Install Gluu Cloud Native Edition using helm. \\\"\\n \\\"This assumes nginx-ingress is installed\\\")\\n subparsers.add_parser(\\\"helm-uninstall-gluu\\\", help=\\\"Uninstall Gluu Cloud Native Edition using helm. \\\"\\n \\\"This only uninstalls Gluu\\\")\\n subparsers.add_parser(\\\"version\\\", help=\\\"Outputs version of pygluu installer.\\\")\\n return parser\",\n \"def add_args_to_subparser(the_parser, subcommand_name):\\n\\n the_parser.add_argument(CmdArgs.verbose_optional, help=CmdArgs.verbose_help,\\n action='store_true',\\n )\\n\\n if subcommand_name in DCA_VISUALIZATION_SUBCOMMANDS:\\n the_parser.add_argument(CmdArgs.biomolecule, help=CmdArgs.biomolecule_help)\\n the_parser.add_argument(CmdArgs.pdb_chain_id, help=CmdArgs.pdb_chain_id_help)\\n the_parser.add_argument(CmdArgs.pdb_file, help=CmdArgs.pdb_file_help)\\n the_parser.add_argument(CmdArgs.refseq_file, help=CmdArgs.refseq_file_help)\\n the_parser.add_argument(CmdArgs.dca_file, help=CmdArgs.dca_file_help)\\n the_parser.add_argument(CmdArgs.rna_secstruct_file_optional,\\n help=CmdArgs.rna_secstruct_file_help,\\n )\\n the_parser.add_argument(CmdArgs.linear_dist_optional,\\n help=CmdArgs.linear_dist_help, type = int,\\n )\\n the_parser.add_argument(CmdArgs.contact_dist_optional,\\n help=CmdArgs.contact_dist_help, type = float,\\n )\\n the_parser.add_argument(CmdArgs.num_dca_contacts_optional,\\n help = CmdArgs.num_dca_contacts_help, type = int,\\n )\\n the_parser.add_argument(CmdArgs.wc_neighbor_dist_optional, type= int,\\n help = CmdArgs.wc_neighbor_dist_help,\\n )\\n the_parser.add_argument(CmdArgs.pdb_id_optional, help = CmdArgs.pdb_id_help)\\n\\n if subcommand_name in FILE_CONTENT_SUBCOMMANDS:\\n if subcommand_name == 'pdb_content':\\n the_parser.add_argument(CmdArgs.pdb_file, help = CmdArgs.pdb_file_help)\\n if subcommand_name in MSA_TRIMMING_SUBCOMMANDS:\\n the_parser.add_argument(CmdArgs.max_gap_optional,\\n type = float, help = CmdArgs.max_gap_help,\\n )\\n if subcommand_name == 'trim_by_refseq':\\n the_parser.add_argument(CmdArgs.biomolecule, help=CmdArgs.biomolecule_help)\\n the_parser.add_argument(CmdArgs.msa_file, help=CmdArgs.msa_file_help)\\n the_parser.add_argument(CmdArgs.refseq_file, help=CmdArgs.refseq_file_help)\\n the_parser.add_argument(CmdArgs.remove_all_gaps_optional,\\n help= CmdArgs.remove_all_gaps_help, action='store_true',\\n )\\n if subcommand_name == 'trim_by_gap_size':\\n the_parser.add_argument(CmdArgs.msa_file, help=CmdArgs.msa_file_help)\\n return None\",\n \"def create_parser(self, prog_name, subcommand):\\n parser = CommandParser(\\n self, prog=\\\"%s %s\\\" % (os.path.basename(prog_name), subcommand),\\n description=self.help or None,\\n )\\n parser.add_argument(\\n '--version', action='version', version=self.get_version())\\n\\n self.add_arguments(parser)\\n return parser\",\n \"def configure_cli(subparsers) -> None: # type: ignore\\n parser = subparsers.add_parser(\\n name='items',\\n description='Retrieve new/updated items from Aleph and send to CaiaSoft'\\n )\\n parser.set_defaults(cmd_name='items')\",\n \"def add_subparsers(cls, parser, name=\\\"\\\", prefixes=[], delim=\\\"_\\\", title=\\\"commands\\\", description=\\\"available commands\\\", required=True):\\n\\t\\tcommand = f\\\"command_{name}\\\"\\n\\t\\tif command in cls.COMMANDS:\\n\\t\\t\\traise CommandParserNameDuplicated(f\\\"Command parser with name {name} already registered.\\\")\\n\\t\\t\\n\\t\\tcls.COMMANDS[command] = {}\\n\\t\\t\\n\\t\\tsub = parser.add_subparsers(title=title, dest=command, description=description)\\n\\t\\tsub.required = True\\n\\t\\tfor pf in prefixes:\\n\\t\\t\\tfor c, method in cls.get_commands(prefix=pf, delim=delim):\\n\\t\\t\\t\\tcls.set_subparser_for(c, method, sub)\\n\\t\\t\\t\\tcls.COMMANDS[command][c] = method\",\n \"def register(\\n self,\\n root_parser: argparse.ArgumentParser,\\n subparsers: argparse._SubParsersAction,\\n ) -> None:\\n self.root_parser = root_parser\\n parser = subparsers.add_parser(\\n self.name,\\n aliases=self.aliases,\\n help=self.help,\\n description=self.help,\\n add_help=self.add_help,\\n )\\n parser.set_defaults(command=self)\\n self.configure(parser)\",\n \"def add_arg_parser(subparsers):\\n # add\\n add_p = subparsers.add_parser('add', description='Create a bundle from a .csv, .tsv, or a directory of files.')\\n add_p.add_argument('-t', '--tag', nargs=1, type=str, action='append',\\n help=\\\"Set one or more tags: 'dsdt add -t authoritative:True -t version:0.7.1'\\\")\\n add_p.add_argument('bundle', type=str, help='The destination bundle in the current context')\\n add_p.add_argument('path_name', type=str, help='File or directory of files to add to the bundle', action='store')\\n add_p.set_defaults(func=lambda args: _add(args))\",\n \"def add_subparser(\\n subparsers: SubParsersAction, parents: List[argparse.ArgumentParser]\\n) -> None:\\n run_parser = subparsers.add_parser(\\n \\\"run\\\",\\n parents=parents,\\n conflict_handler=\\\"resolve\\\",\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n help=\\\"Starts a Rasa server with your trained model.\\\",\\n )\\n run_parser.set_defaults(func=run)\\n\\n run_subparsers = run_parser.add_subparsers()\\n sdk_subparser = run_subparsers.add_parser(\\n \\\"actions\\\",\\n parents=parents,\\n conflict_handler=\\\"resolve\\\",\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n help=\\\"Runs the action server.\\\",\\n )\\n sdk_subparser.set_defaults(func=run_actions)\\n\\n arguments.set_run_arguments(run_parser)\\n arguments.set_run_action_arguments(sdk_subparser)\",\n \"def create_parser(self, prog_name, subcommand):\\r\\n return OptionParser(prog=prog_name,\\r\\n usage=self.usage(subcommand),\\r\\n version='',\\r\\n add_help_option = False,\\r\\n option_list=self.option_list)\",\n \"def add_subcommand(self, command):\\n\\n if self.subcommand_parser is None:\\n self.subcommand_parser = self.parser.add_subparsers(\\n dest='command', help='Please select one command mode below',\\n title='Command modes'\\n )\\n self.subcommands = {}\\n\\n if not isinstance(command, ScriptCommand):\\n raise ScriptError('Subcommand must be a ScriptCommand instance')\\n\\n parser = self.subcommand_parser.add_parser(\\n command.name,\\n help=command.short_description,\\n description=command.description,\\n epilog=command.epilog,\\n formatter_class=argparse.RawTextHelpFormatter,\\n )\\n self.subcommands[command.name] = command\\n command.script = self\\n\\n if callable(getattr(command, '__register_arguments__', None)):\\n command.__register_arguments__(parser)\\n\\n return parser\",\n \"def init_parser(self):\\n usage = \\\"%s [options] [subcommand]\\\" % (self.command)\\n description = \\\"An SBTools test plug-in.\\\"\\n\\n parser = SBToolOptionParser(self, self.sbtools, usage, description=description)\\n return parser\",\n \"def make_adder(self, *args, **kwargs):\\n kwargs.setdefault(\\\"dest\\\", \\\"subcmd\\\")\\n subp = self.add_subparsers(*args, **kwargs)\\n return partial(self.add_subcommand, subp)\",\n \"def register_cli(cls):\\n for cmd in cls.SUB_GROUP_COMMANDS:\\n getattr(cls, cls.SUB_GROUP_NAME).add_command(getattr(cls, cmd))\",\n \"def add_parser(subparsers):\\n parser = subparsers.add_parser('upload', help='upload build to Koji')\\n\\n parser.add_argument('--scm-url', required=True,\\n help='SCM URL for this build, eg. git://...')\\n parser.add_argument('--owner', required=True,\\n help='koji user name that owns this build')\\n parser.add_argument('--tag',\\n help='tag this build, eg. ceph-3.2-xenial-candidate')\\n parser.add_argument('--dryrun', action='store_true',\\n help=\\\"Show what would happen, but don't do it\\\")\\n parser.add_argument('--skip-log', action='store_true',\\n help=\\\"Do not upload a .build log file\\\")\\n parser.add_argument('directory', help=\\\"parent directory of a .dsc file\\\")\\n parser.set_defaults(func=main)\",\n \"def add_parser(subparsers) -> None:\\n contest_parser = subparsers.add_parser(\\n 'contest', help='build contest files')\\n mut_ex_group = contest_parser.add_mutually_exclusive_group()\\n mut_ex_group.add_argument(\\n '-p', '--pdf', action='store_true', default=False, help='generate contest PDFs')\\n mut_ex_group.add_argument('-i', '--io', action='store_true',\\n default=False, help='generate contest input/output files')\\n contest_parser.add_argument(\\n 'problem_dir', help='path to problem(s)', nargs='+')\\n contest_parser.add_argument(\\n 'contest_dir', help='directory which the contest will be saved')\\n contest_parser.set_defaults(function=lambda options: process_contest(\\n options.problem_dir, options.contest_dir, options.pdf, options.io))\",\n \"def build_cli(self):\\n parser = argparse.ArgumentParser(\\\"xsgen\\\",\\n conflict_handler='resolve', argument_default=NotSpecified)\\n for plugin in self.plugins:\\n plugin.update_argparser(parser)\\n self.parser = parser\\n return parser\",\n \"def add_subcommand(self, cmd):\\n self.subcommands.append(cmd)\",\n \"def _init_cfg_subcmd(subparsers):\\n cfg_related = subparsers.add_parser(\\\"cfg\\\",)\\n cfg_subparsers = cfg_related.add_subparsers(dest=\\\"cfg_subcommand\\\")\\n\\n cfg_write_parser = cfg_subparsers.add_parser(\\\"write\\\")\\n cfg_write_parser.add_argument(\\n \\\"--level\\\",\\n choices=[\\\"user\\\", \\\"cwd\\\"],\\n default=None,\\n help=\\\"Specify if this config is for user or just the working directory.\\\",\\n )\\n cfg_write_parser.add_argument(\\n \\\"--open\\\", action=\\\"store_const\\\", const=True, default=False\\n )\\n cfg_subparsers.add_parser(\\\"show\\\")\\n\\n cfg_export_parser = cfg_subparsers.add_parser(\\\"export\\\")\\n cfg_export_parser.add_argument(\\\"--dir\\\", default=os.getcwd())\\n\\n return cfg_related\",\n \"def _make_parser(self, **kwargs):\\n\\n kwargs.setdefault('help', self.help)\\n kwargs.setdefault('formatter_class',argparse.RawDescriptionHelpFormatter)\\n kwargs.setdefault('description', self.description)\\n kwargs.setdefault('name', self.name)\\n names = (kwargs.get('name') or self.name).split('.')\\n \\n def _get_subparser(a):\\n if a._subparsers:\\n for action in a._subparsers._actions:\\n if isinstance(action, argparse._SubParsersAction):\\n return action\\n raise RuntimeError('could not find adequate subparser')\\n return a.add_subparsers(dest='command',\\n title='commands',\\n metavar='COMMAND')\\n def _get_parser(node, idx, names):\\n name = names[idx]\\n if name in node.choices:\\n return node.choices[name]\\n args = {\\n 'name' : name,\\n 'help' : 'a group of sub-commands',\\n }\\n return node.add_parser(**args)\\n \\n parser = ACMD_PARSER\\n node = _get_subparser(parser)\\n\\n for i,n in enumerate(names[:-1]):\\n node = _get_subparser(parser)\\n parser = _get_parser(node, i, names)\\n \\n node = _get_subparser(parser)\\n kwargs['name'] = names[-1]\\n parser = node.add_parser(**kwargs)\\n return parser\",\n \"def _init_cfg_subcmd(subparsers):\\n cfg_related = subparsers.add_parser(\\\"cfg\\\")\\n cfg_subparsers = cfg_related.add_subparsers(dest=\\\"cfg_subcommand\\\")\\n\\n cfg_write_parser = cfg_subparsers.add_parser(\\\"write\\\")\\n cfg_write_parser.add_argument(\\n \\\"--level\\\",\\n choices=[\\\"user\\\", \\\"cwd\\\"],\\n default=None,\\n help=\\\"Specify if this config is for user or just the working directory.\\\",\\n )\\n cfg_write_parser.add_argument(\\n \\\"--open\\\", action=\\\"store_const\\\", const=True, default=False\\n )\\n cfg_subparsers.add_parser(\\\"show\\\")\\n\\n cfg_export_parser = cfg_subparsers.add_parser(\\\"export\\\")\\n cfg_export_parser.add_argument(\\\"--dir\\\", default=os.getcwd())\\n\\n return cfg_related\",\n \"def add_new_subparser(subparsers, formatter_class=RawTextHelpFormatter):\\n # TODO: add info on no args to description or help\\n # Adds custom --help argument\\n generic_parent_parser = cmd.argparse.get_generic_parent_parser()\\n new_description = 'Create a new test module or page object'\\n new_help = new_description\\n new_parser = subparsers.add_parser(\\n 'new', description=new_description, help=new_help,\\n parents=[generic_parent_parser],\\n formatter_class=formatter_class,\\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\\n )\\n # New <type> subparsers\\n new_type_desc = 'Run \\\\'{} <type> --help\\\\' for details'.format(new_parser.prog)\\n new_subparsers = new_parser.add_subparsers(\\n title='File Types', description=new_type_desc, dest='type', metavar='<type>'\\n )\\n # New test parser\\n new_test_parent_parser = get_new_parent_parser(\\n parents=[generic_parent_parser], class_name_metavar='<TestCaseClass>',\\n class_name_help='Name to use for the initial test case class'\\n )\\n new_test_description = 'Create a new test module'\\n new_test_help = new_test_description\\n new_subparsers.add_parser(\\n 'test', description=new_test_description, help=new_test_help,\\n parents=[new_test_parent_parser],\\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\\n )\\n # New page object parser\\n new_page_parent_parser = get_new_parent_parser(\\n parents=[generic_parent_parser], class_name_metavar='<PageObjectClass>',\\n class_name_help='Name to use for the initial page object class'\\n )\\n new_page_description = 'Create a new page object module'\\n new_page_help = new_page_description\\n new_page_parser = new_subparsers.add_parser(\\n 'page', description=new_page_description, help=new_page_help,\\n parents=[new_page_parent_parser],\\n add_help=False, epilog=cmd.argparse.ARGPARSE_EPILOG\\n )\\n # TODO: add optional --prototype arg with a list of valid page object prototype classes\\n return new_parser\",\n \"def init_cli(self):\\n self.cli_parser.add_cmd(\\\"completions\\\", \\\"Return a list of completions for autocomplete\\\",\\n self.cli_completions)\\n\\n self.cli_parser.add_cmd(\\\"disable-plugin\\\", \\\"Disable the named plugin\\\",\\n self.cli_disable_plugin,\\n \\\"plugin_name\\\", \\\"Name of plugin to disable\\\")\\n\\n self.cli_parser.add_cmd(\\\"enable-plugin\\\", \\\"Enables the named plugin\\\",\\n self.cli_enable_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to enable\\\")\\n\\n self.cli_parser.add_cmd(\\\"help\\\", \\\"Displays this message\\\",\\n self.cli_parser.get_help)\\n\\n self.cli_parser.add_cmd(\\\"kill-plugin\\\", \\\"Kills the named plugin\\\",\\n self.cli_kill_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to kill\\\")\\n\\n self.cli_parser.add_cmd(\\\"load-config\\\", \\\"Reloads a configuration from file\\\",\\n self.cli_load_config,\\n \\\"config_path\\\", \\\"Path of the configuration file\\\")\\n\\n self.cli_parser.add_cmd(\\\"load-plugin\\\", \\\"Loads (or reloads) a plugin from disk\\\",\\n self.cli_load_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to load\\\")\\n\\n self.cli_parser.add_cmd(\\\"list-plugins\\\", \\\"List all loaded plugins\\\",\\n self.cli_list_plugins)\\n\\n self.cli_parser.add_cmd(\\\"start-plugin\\\", \\\"Start the named plugin\\\",\\n self.cli_start_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to start\\\")\\n\\n self.cli_parser.add_cmd(\\\"stop-plugin\\\", \\\"Stop the named plugin\\\",\\n self.cli_stop_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to stop\\\")\\n\\n self.cli_parser.add_cmd(\\\"stop-all-plugins\\\", \\\"Stop all plugins\\\",\\n self.cli_stop_all_plugins)\\n\\n self.cli_parser.add_cmd(\\\"restart-plugin\\\", \\\"Restarts the named plugin\\\",\\n self.cli_restart_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to restart\\\")\\n\\n self.cli_parser.add_cmd(\\\"unload-plugin\\\", \\\"Unloads the named plugin\\\",\\n self.cli_unload_plugin,\\n \\\"plugin_name\\\", \\\"Name of the plugin to unload\\\")\",\n \"def set_subparser_for(cls, command, method, subparser):\\n\\n\\t\\tdef add_pos_argument(sub, label, arg):\\n\\t\\t\\tif arg[\\\"type\\\"] == bool:\\n\\t\\t\\t\\traise CommandTypeError(\\\"bool type not supported as positional argument\\\")\\n\\t\\t\\tif \\\"value\\\" in arg:\\n\\t\\t\\t\\tif arg[\\\"type\\\"] in [str, int, float]:\\n\\t\\t\\t\\t\\tsub.add_argument(label, nargs='?', default=arg[\\\"value\\\"], type=arg[\\\"type\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\telif \\\"values\\\" in arg:\\n\\t\\t\\t\\tif arg[\\\"type\\\"] in [str, int, float]:\\n\\t\\t\\t\\t\\tsub.add_argument(label, nargs='?', default=arg[\\\"values\\\"][0], choices=arg[\\\"values\\\"], type=arg[\\\"type\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\t\\telif arg[\\\"type\\\"] == list:\\n\\t\\t\\t\\t\\tsub.add_argument(label, nargs='+', default=arg[\\\"values\\\"][0], choices=arg[\\\"values\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsub.add_argument(label, type=arg[\\\"type\\\"], help=arg[\\\"help_line\\\"])\\n\\n\\t\\tdef add_opt_argument(sub, label, arg, add_alias=True):\\n\\t\\t\\tif arg[\\\"type\\\"] == bool:\\n\\t\\t\\t\\tif add_alias:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"alias\\\"], arg[\\\"name\\\"], action=\\\"store_true\\\", default=False, help=arg[\\\"help_line\\\"])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"name\\\"], action=\\\"store_true\\\", default=False, help=arg[\\\"help_line\\\"])\\n\\n\\t\\t\\telif arg[\\\"type\\\"] in [str, int, float] and \\\"value\\\" in arg:\\n\\t\\t\\t\\tif add_alias:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"alias\\\"], arg[\\\"name\\\"], type=arg[\\\"type\\\"], default=arg[\\\"value\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"name\\\"], type=arg[\\\"type\\\"], default=arg[\\\"value\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\telif arg[\\\"type\\\"] == list and \\\"values\\\" not in arg:\\n\\t\\t\\t\\tsub.add_argument(label, nargs=\\\"*\\\", help=arg[\\\"help_line\\\"])\\n\\t\\t\\telif \\\"values\\\" in arg:\\n\\t\\t\\t\\tif arg[\\\"type\\\"] == list:\\n\\t\\t\\t\\t\\tsub.add_argument(label, choices=arg[\\\"values\\\"], default=arg[\\\"values\\\"][0], nargs=\\\"*\\\", help=arg[\\\"help_line\\\"])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsub.add_argument(label, type=arg[\\\"type\\\"], choices=arg[\\\"values\\\"], default=arg[\\\"values\\\"][0], nargs=\\\"?\\\", help=arg[\\\"help_line\\\"])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif add_alias:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"alias\\\"], arg[\\\"name\\\"], type=arg[\\\"type\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsub.add_argument(arg[\\\"name\\\"], type=arg[\\\"type\\\"], help=arg[\\\"help_line\\\"])\\n\\t\\t\\n\\t\\tfunc = getattr(cls, method)\\n\\n\\t\\targs_info = cls.__parse_docstring(func.__doc__)\\n\\t\\tif args_info == {}:\\n\\t\\t\\treturn\\n\\n\\t\\tc = subparser.add_parser(command, help=args_info[\\\"help_line\\\"])\\n\\n\\t\\tif \\\"arguments\\\" in args_info:\\n\\t\\t\\tfor label, arg in args_info[\\\"arguments\\\"].items():\\n\\t\\t\\t\\tif arg[\\\"pos\\\"]:\\n\\t\\t\\t\\t\\tadd_pos_argument(c, label, arg)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tadd_opt_argument(c, label, arg, add_alias=True)\\n\\t\\t\\t\\t\\texcept ArgumentError as e:\\n\\t\\t\\t\\t\\t\\tadd_opt_argument(c, label, arg, add_alias=False)\",\n \"def calcs_parser(subparser, name, help_msg):\\n parser = subparser.add_parser(name,\\\\\\n help=help_msg)\\n parser = add_arg(parser, 'parallel')\\n parser = add_arg(parser, 'xvars')\\n parser = add_arg(parser, 'time')\\n parser = add_arg(parser, 'modif_time')\\n parser = add_arg(parser, 'modif_coord')\\n parser = add_arg(parser, 'modif_var')\\n parser = add_arg(parser, 'eswitch')\\n parser = add_arg(parser, 'fswitch')\\n parser.add_argument(\\\"args\\\", metavar='SELAFIN file', nargs=\\\"+\\\")\\n\\n return subparser\",\n \"def setup_subcommands(argparser):\\n\\n subparsers = argparser.add_subparsers()\\n\\n parser_info = subparsers.add_parser('info', help = 'Provide the information about the user')\\n parser_info.add_argument('user', help = 'The user to inspect')\\n\\n parser_ownerships = subparsers.add_parser('ownerships', help = 'Show items which this user owns')\\n parser_ownerships.add_argument('user', help = 'The name of the user to show information about')\\n parser_ownerships.add_argument('-r', '--recursive', action = 'store_true', help = 'Show items which this user own through being in lists')\\n \\n parser_info.set_defaults(handler = show_info)\\n parser_ownerships.set_defaults(handler = show_ownerships)\",\n \"def create_parser(self, prog_name, subcommand):\\n return OptionParser(prog=prog_name,\\n usage=self.usage(subcommand),\\n version=self.get_version(),\\n option_list=self.option_list)\",\n \"def test_create_subparser_noop(self):\\n self.command.create_subparser()\",\n \"def create_parser():\\n parser = argparse.ArgumentParser(\\n description='CLI for SMS',\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n )\\n\\n # Add subcommands\\n subparsers = parser.add_subparsers(title='subcommands', dest='cmd')\\n\\n # Downlink Unitdata\\n downlink_unitdata_parser = subparsers.add_parser(\\n 'DU', help=\\\"Send downlink unitdata to SMSOrc8rGW service\\\",\\n )\\n downlink_unitdata_parser.add_argument('imsi', help='e.g. 001010000090122 (no prefix required)')\\n downlink_unitdata_parser.add_argument('data', help='Data as a hex string e.g. 1fc13a00')\\n downlink_unitdata_parser.set_defaults(func=send_downlink_unitdata)\\n\\n return parser\",\n \"def add_cmd(self, name: str, help_str: str, cmd_fn: typing.Callable, arg: str = None, arg_help: str = None):\\n self.cmd_names.append(name)\\n cmd = self.cli_subparsers.add_parser(name, help=help_str)\\n cmd.set_defaults(func=cmd_fn)\\n if arg is not None:\\n cmd.add_argument(arg, help=arg_help)\",\n \"def load_into(subparser, as_cmd=None):\\n p = subparser\\n p.description = description\\n\\n if not as_cmd:\\n as_cmd = default_name\\n out = cli.CommandSuite(as_cmd, p)\\n out.load_subcommand(topics)\\n return out\",\n \"def add_arguments(self, sub_parser):\\n sp = sub_parser\",\n \"def cli():\\n pass # do nothing here, it just defines the name for other subcommands\",\n \"def build_subcommands_parser(parser, module):\\n mdefs = module.__dict__\\n keys = list(mdefs.keys())\\n keys.sort()\\n subparsers = parser.add_subparsers(help='sub-command help')\\n for command in keys:\\n if command.startswith('pub_'):\\n func = module.__dict__[command]\\n parser = subparsers.add_parser(command[4:], help=func.__doc__)\\n parser.set_defaults(func=func)\\n argspec = inspect.signature(func)\\n positionals = []\\n short_opts = set([])\\n for arg in argspec.parameters.values():\\n if arg.default == inspect.Parameter.empty:\\n positionals += [arg]\\n else:\\n param_name = arg.name.replace('_', '-')\\n short_opt = param_name[0]\\n if not (param_name.startswith('no') or\\n (short_opt in short_opts)):\\n opts = ['-%s' % short_opt, '--%s' % param_name]\\n else:\\n opts = ['--%s' % param_name]\\n short_opts |= set([short_opt])\\n if isinstance(arg.default, list):\\n parser.add_argument(*opts, action='append')\\n elif isinstance(arg.default, dict):\\n parser.add_argument(*opts, type=json.loads)\\n elif arg.default is False:\\n parser.add_argument(*opts, action='store_true')\\n elif arg.default is not None:\\n parser.add_argument(*opts, default=arg.default)\\n else:\\n parser.add_argument(*opts)\\n if positionals:\\n for arg in positionals[:-1]:\\n parser.add_argument(arg.name)\\n parser.add_argument(positionals[-1].name, nargs='*')\",\n \"def add_complete_hic_subparser(subparsers):\\n parser = subparsers.add_parser(\\\"hic-complete\\\", help=\\\"HiFive HiC Project Complete Analysis Function: Create all necessary files (Fend, Data, and Project) and learn correction parameters for a HiFive HiC project.\\\", epilog=\\\"For command line options of each normalization approach, type: %(prog)s <ALGORITHM> -h\\\")\\n subparser2 = parser.add_subparsers(dest='algorithm')\\n prob_parser = subparser2.add_parser(\\\"probability\\\", help=\\\"A probability model based approach for learning correction values associated with each fend. Learning is accomplished using gradient descent.\\\")\\n exp_parser = subparser2.add_parser(\\\"express\\\", help=\\\"An appoximation based approach for learning correction values associated with each fend. Learning is accomplished using a variation of matrix balancing.\\\")\\n bin_parser = subparser2.add_parser(\\\"binning\\\", help=\\\"A multivariate binning probability model-based approach for learning correction values associated with fend characteristics. Learning is accomplished using the Broyden-Fletcher-Goldfarb-Shanno algorithm.\\\")\\n binprob_parser = subparser2.add_parser(\\\"binning-probability\\\", help=\\\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a probability model.\\\")\\n binexp_parser = subparser2.add_parser(\\\"binning-express\\\", help=\\\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a matrix-balancing approximation.\\\")\\n add_complete_hic_group(prob_parser)\\n add_hic_probability_group(prob_parser)\\n add_complete_hic_group(exp_parser)\\n add_hic_express_group(exp_parser)\\n add_complete_hic_group(bin_parser)\\n add_hic_binning_group(bin_parser)\\n add_complete_hic_group(binprob_parser)\\n add_hic_probability_group(binprob_parser)\\n add_hic_binning_group(binprob_parser)\\n add_complete_hic_group(binexp_parser)\\n add_hic_express_group(binexp_parser)\\n add_hic_binning_group(binexp_parser)\\n return\",\n \"def add_hicnormalize_subparser(subparsers):\\n parser = subparsers.add_parser(\\\"hic-normalize\\\", help=\\\"HiFive HiC Project Normalization Function: Learn correction parameters for a HiFive HiC project.\\\", epilog=\\\"For command line options of each normalization approach, type: %(prog)s <ALGORITHM> -h\\\")\\n subparser2 = parser.add_subparsers(dest='algorithm')\\n prob_parser = subparser2.add_parser(\\\"probability\\\", help=\\\"A probability model based approach for learning correction values associated with each fend. Learning is accomplished using gradient descent.\\\")\\n exp_parser = subparser2.add_parser(\\\"express\\\", help=\\\"An appoximation based approach for learning correction values associated with each fend. Learning is accomplished using a variation of matrix balancing.\\\")\\n bin_parser = subparser2.add_parser(\\\"binning\\\", help=\\\"A multivariate binning probability model-based approach for learning correction values associated with fend characteristics. Learning is accomplished using the Broyden-Fletcher-Goldfarb-Shanno algorithm.\\\")\\n binprob_parser = subparser2.add_parser(\\\"binning-probability\\\", help=\\\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a probability model.\\\")\\n binexp_parser = subparser2.add_parser(\\\"binning-express\\\", help=\\\"A chained-correction approach first learning fend characteristic corrections and applying them prior to learning fend-associated correction values via a matrix-balancing approximation.\\\")\\n add_hic_normalize_group(prob_parser)\\n add_hic_probability_group(prob_parser)\\n add_hic_normalize_group(exp_parser)\\n add_hic_express_group(exp_parser)\\n exp_parser.add_argument(\\\"-f\\\", \\\"--min-interactions\\\", dest=\\\"minint\\\", required=False, type=int, default=20,\\n action='store', help=\\\"The minimum number of interactions for fend filtering, if refiltering is required due to distance cutoff or selected reads. [default: %(default)s]\\\")\\n add_hic_normalize_group(bin_parser)\\n add_hic_binning_group(bin_parser)\\n add_hic_normalize_group(binprob_parser)\\n add_hic_probability_group(binprob_parser)\\n add_hic_binning_group(binprob_parser)\\n add_hic_normalize_group(binexp_parser)\\n add_hic_express_group(binexp_parser)\\n binexp_parser.add_argument(\\\"-f\\\", \\\"--min-interactions\\\", dest=\\\"minint\\\", required=False, type=int, default=20,\\n action='store', help=\\\"The minimum number of interactions for fend filtering, if refiltering is required due to distance cutoff or selected reads. [default: %(default)s]\\\")\\n add_hic_binning_group(binexp_parser)\\n return\",\n \"def register_argument_parser(add_parser, action):\\n sub_command = str(action)\\n return add_parser(sub_command,\\n help=f'{sub_command} token',\\n description=f'{sub_command.capitalize()} a Waiter token. '\\n 'In addition to the optional arguments '\\n 'explicitly listed below, '\\n 'you can optionally provide any Waiter '\\n 'token parameter as a flag. For example, '\\n 'to specify 10 seconds for the '\\n 'grace-period-secs parameter, '\\n 'you can pass --grace-period-secs 10. '\\n 'You can also provide nested fields separated by a period. For example, '\\n 'to specify an environment variable FOO as \\\\\\\"bar\\\\\\\", you can pass --env.FOO \\\\\\\"bar\\\\\\\".')\",\n \"def add_parser(subp, raw):\\n tmpp = subp.add_parser('mkmodel', help='create a model file',\\n formatter_class=raw,\\n description=textwrap.dedent(DESC))\\n tmpp.add_argument('model', type=str, metavar='MODELNAME',\\n help='name of the model')\\n tmpp.add_argument('type', type=str, choices=['ssa', 'sde'],\\n help='model type')\\n tmpp.add_argument('--nspecs', type=positive_type,\\n help='number of species')\\n tmpp.add_argument('--nreacs', type=positive_type,\\n help='number of reactions')\\n tmpp.add_argument('--dim', type=positive_type,\\n help='dimension of phase space')\\n tmpp.add_argument('-z', type=str, choices=['row', 'col'],\\n help='state change matrix format')\",\n \"def set_parser(*, collected, parser=None):\\n if parser is None:\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers()\\n commands = unique(collected)\\n for name, details in commands.items():\\n original = details.original\\n args = details.extra\\n a_subparser = subparsers.add_parser(name)\\n a_subparser.set_defaults(\\n __gather_name__=name,\\n __gather_command__=original,\\n )\\n for arg_details in args:\\n a_subparser.add_argument(*arg_details.args, **dict(arg_details.kwargs))\\n return parser\",\n \"def make_parser():\\n parser = argparse.ArgumentParser(prog=__file__.replace(\\\".py\\\", \\\"\\\"),\\n description='simple $PATH tool')\\n parser.add_argument('-n', '--nocolor', dest=\\\"color\\\",\\n action=\\\"store_false\\\", default=True,\\n help='Turn off ANSI color codes.')\\n parser.add_argument('-w', '--nowarn', action=\\\"store_true\\\",\\n help='Turn off path warnings.')\\n subs = parser.add_subparsers(title='subcommands',\\n description='The subcommands')\\n\\n sub = subs.add_parser('replace', description=\\\"Search & Replace $PATH\\\")\\n sub.set_defaults(cmd='path_replace')\\n sub.add_argument('terms', nargs='+',\\n help='Format: search:replace, search:replace, ...')\\n\\n sub = subs.add_parser('show', description=\\\"Show $PATH compoents\\\")\\n sub.set_defaults(cmd='path_show')\\n sub.add_argument('-n', '--nocolor', dest=\\\"color\\\",\\n action=\\\"store_false\\\", default=True,\\n help='Turn off ANSI color codes.')\\n sub.add_argument('-w', '--nowarn', action=\\\"store_true\\\",\\n help='Turn off path warnings.')\\n\\n sub = subs.add_parser('which', description=\\\"Platform agnostic `which -a`\\\")\\n sub.set_defaults(cmd='path_which')\\n sub.add_argument('look', help='Look for this executable')\\n sub.add_argument('-n', '--nocolor', dest=\\\"color\\\",\\n action=\\\"store_false\\\", default=True,\\n help='Turn off ANSI color codes.')\\n sub.add_argument('-v', '--version', action=\\\"store_true\\\",\\n help='Show version of exact matches.')\\n\\n return parser\",\n \"def argparse_subparser_name():\\n return \\\"plugins\\\"\",\n \"def cli_add_command(parent_parser):\\n parser = parent_parser.add_parser(\\\"setup\\\", description=cli_add_command.__doc__)\\n\\n def run_it(_):\\n from .util import print_important_env\\n print_important_env()\\n\\n log.info(\\\"AKRR Setup\\\")\\n\\n _config_setup()\\n setup()\\n\\n parser.set_defaults(func=run_it)\",\n \"def register_subcommand(nest_ops_subparsers):\\n parser = nest_ops_subparsers.add_parser('compile', \\\\\\n help=COMPILE_CMD_HELP, \\\\\\n description=COMPILE_CMD_DESCRIPTION, \\\\\\n formatter_class=argparse.RawTextHelpFormatter )\\n\\n parser.add_argument('--code_type', \\\\\\n choices=VALID_CODE_TYPES, \\\\\\n help=CODE_TYPE_ARG_HELP, \\\\\\n default='all' \\\\\\n )\\n\\n parser.add_argument('--project', \\\\\\n help=\\\"\\\"\\\"Which project to build. Only affects the web_assets:dist\\n code_type, where it determines which project's index.html\\n will be the main entry point index.html in the static files.\\\"\\\"\\\", \\\\\\n choices=nest_envs.VALID_PROJECT_NAMES, \\\\\\n default=nest_envs.DEFAULT_PROJECT_NAME, \\\\\\n )\\n \\n parser.add_argument('--runlevel', \\\\\\n help='Determines the run level for logging, error checking, etc.',\\n choices=nest_envs.VALID_RUNLEVEL_NAMES,\\n default=nest_envs.DEFAULT_RUNLEVEL_NAME, \\\\\\n )\\n\\n #super ugly callback mechanism from argparse\\n parser.set_defaults(func=_run_compile_cmd)\\n return\",\n \"def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs) -> ArgumentParser:\\n return add_argparse_args(cls, parent_parser, **kwargs)\",\n \"def get_command_line_parser():\\n command_line_parser = argparse.ArgumentParser(\\n description=\\\"Execute data workflows defined in flo.yaml files\\\",\\n )\\n subcommand_creator = command_line_parser.add_subparsers(\\n title='SUBCOMMANDS',\\n )\\n for command_module in COMMAND_MODULES:\\n command = command_module.Command(subcommand_creator)\\n\\n # this sets a default value for the command \\\"option\\\" so\\n # that, when this Command is selected by argparse from the\\n # command line, we know which comman instance it\\n # corresponds with. See run_subcommand function below.\\n command.option_parser.set_defaults(command=command)\\n return command_line_parser\",\n \"def import_additional_parser():\\n # Parse arguments\\n try:\\n global add_parser\\n import add_parser\\n except ImportError as e:\\n print('No additional parser found.')\\n pass\",\n \"def arg_parser(self):\\n print\\n\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers()\\n\\n status = subparsers.add_parser('status', help=self.viewer.status.__doc__)\\n status.set_defaults(func=self.viewer.status)\\n\\n view_catalog = subparsers.add_parser('catalog', help=self.viewer.view_full_catalog.__doc__)\\n view_catalog.set_defaults(func=self.viewer.view_full_catalog)\\n\\n view_tags = subparsers.add_parser('tags', help=self.viewer.view_all_tags.__doc__)\\n view_tags.add_argument('-a', '--alpha', action='store_true', help=\\\"List tags alphabetically\\\")\\n view_tags.set_defaults(func=self.viewer.view_all_tags)\\n\\n search = subparsers.add_parser('search', help=self.viewer.search_tags.__doc__)\\n search.add_argument('-t', '--tags', nargs='+', required=True, help=\\\"Tags to search for\\\")\\n search.add_argument('-o', action='store_true', help=\\\"Search using 'or' logic ('and' logic by default)\\\")\\n search.set_defaults(func=self.viewer.search_tags) \\n\\n add_file = subparsers.add_parser('addfile', help=self.editor.add_files.__doc__)\\n add_file.add_argument('filename', help=\\\"Name of file to add catalog\\\")\\n add_file.set_defaults(func=self.editor.add_files)\\n\\n add_tags = subparsers.add_parser('addtags', help=self.editor.add_tag.__doc__)\\n add_tags.add_argument('filename', help=\\\"Name of file to add catalog\\\")\\n add_tags.add_argument('-t', '--tags', nargs='+', help=\\\"Tags to add to catalog\\\")\\n add_tags.set_defaults(func=self.editor.add_tag)\\n\\n edit_entry = subparsers.add_parser('edit', help=self.editor.edit_entry.__doc__)\\n edit_entry.add_argument('filename', help=\\\"Name of file to add catalog\\\")\\n edit_entry.set_defaults(func=self.editor.edit_entry)\\n\\n clean_catalog = subparsers.add_parser('clean', help=self.editor.clean_catalog.__doc__)\\n clean_catalog.add_argument('-t', '--tags', nargs='+', help=\\\"Tags to be deleted from catalog entirely\\\")\\n clean_catalog.set_defaults(func=self.editor.clean_catalog)\\n\\n delete_entry = subparsers.add_parser('delete', help=self.editor.delete_entry.__doc__)\\n delete_entry.add_argument('filename', help=\\\"File from which to delete specified tags\\\")\\n delete_entry.add_argument('-t', '--tags', nargs='+', help=\\\"Tags to be deleted\\\")\\n delete_entry.set_defaults(func=self.editor.delete_entry)\\n\\n merge_tags = subparsers.add_parser('merge', help=self.editor.merge_tags.__doc__)\\n merge_tags.add_argument('--source', required=True, help=\\\"File from which tags are being taken\\\")\\n merge_tags.add_argument('--dest', required=True, help=\\\"Destination file to which tags from --source are added\\\")\\n merge_tags.set_defaults(func=self.editor.merge_tags)\\n\\n \\n args = parser.parse_args()\\n\\n args.func(args)\",\n \"def register_command(subparser):\\n update_parser = subparser.add_parser('update', help='Run the log files through an updater. Used to update '\\n 'between versions of autology')\\n update_parser.set_defaults(func=_main)\\n\\n # Arguments\\n update_parser.add_argument('-f', '--files', help='Update the files that are currently defined in the log '\\n 'directories', action='store_true')\\n update_parser.add_argument('-t', '--templates', help='Install a new output template', action='store_true')\\n update_parser.add_argument('-T', '--template-definition', help='Define a template definition to install',\\n default=template_utilities.DEFAULT_TEMPLATES_URL)\",\n \"def add_commands(subparsers, path):\\n log.debug('importing %s', path)\\n try:\\n del sys.modules[path]\\n except KeyError:\\n pass\\n try:\\n package = importlib.import_module(path)\\n except Exception as e:\\n log.warning('failed to import commands package %s',\\n path, exc_info=True)\\n return\\n log.debug('commands package: %s', path)\\n for (finder, name, ispkg) in pkgutil.iter_modules(package.__path__):\\n if ispkg:\\n continue\\n try:\\n command = importlib.import_module('.' + name, path)\\n except Exception as e:\\n log.warning('failed to import %s command: %s',\\n path, name, exc_info=True)\\n continue\\n if not getattr(command, 'run', None):\\n log.warning('skipping command module without run function: %s',\\n name)\\n continue\\n log.debug('command: %s'%(name))\\n name = command.__name__.split('.')[-1]\\n parser_help = getattr(command, 'parser_help', None)\\n if parser_help is None:\\n log.warning('command %s missing help text'%(command.__name__))\\n parser = subparsers.add_parser(name, help=parser_help)\\n command.add_arguments(parser)\\n parser.set_defaults(run=command.run)\",\n \"def create_parser():\\n helpdict = create_parser.helpdict\\n # Customized usage, for more verbosity concerning these subparsers options.\\n usage = \\\"\\\"\\\"%(prog)s [-h] [--version] {run,info} ... \\\"\\\"\\\"\\n usage += tw.dedent(\\\"\\\"\\\"\\\\n\\n From more help on each of the subcommands, type:\\n %(prog)s run -h\\n %(prog)s info -h\\\\n\\\\n\\\"\\\"\\\")\\n\\n # parser = ap.ArgumentParser(\\n #parser = MpArgumentParser(\\n #formatter_class=ap.ArgumentDefaultsHelpFormatter,\\n #description='Monte Python, a Monte Carlo code in Python',\\n #usage=usage)\\n parser = initialise_parser(\\n description='Monte Python, a Monte Carlo code in Python', usage=usage)\\n\\n # -- add the subparsers\\n subparser = parser.add_subparsers(dest='subparser_name')\\n\\n ###############\\n # run the MCMC\\n runparser = add_subparser(subparser, 'run', help=\\\"run the MCMC chains\\\")\\n\\n # -- number of steps (OPTIONAL)\\n runparser.add_argument('-N', help=helpdict['N'], type=positive_int,\\n dest='N')\\n # -- output folder (OBLIGATORY)\\n runparser.add_argument('-o', '--output', help=helpdict['o'], type=str,\\n dest='folder')\\n # -- parameter file (OBLIGATORY)\\n runparser.add_argument('-p', '--param', help=helpdict['p'],\\n type=existing_file, dest='param')\\n # -- covariance matrix (OPTIONAL)\\n runparser.add_argument('-c', '--covmat', help=helpdict['c'],\\n type=existing_file, dest='cov')\\n # -- jumping method (OPTIONAL)\\n runparser.add_argument('-j', '--jumping', help=helpdict['j'],\\n dest='jumping', default='fast',\\n choices=['global', 'sequential', 'fast'])\\n # -- sampling method (OPTIONAL)\\n runparser.add_argument('-m', '--method', help=helpdict['m'],\\n dest='method', default='MH',\\n choices=['MH', 'NS', 'PC', 'CH', 'IS', 'Der', 'Fisher'])\\n # -- update Metropolis Hastings (OPTIONAL)\\n runparser.add_argument('--update', help=helpdict['update'], type=int,\\n dest='update', default=50)\\n # -- update Metropolis Hastings with an adaptive jumping factor (OPTIONAL)\\n runparser.add_argument('--superupdate', help=helpdict['superupdate'], type=int,\\n dest='superupdate', default=0)\\n # -- superupdate acceptance rate argument (OPTIONAL)\\n runparser.add_argument('--superupdate-ar', help=helpdict['superupdate-ar'], type=float,\\n dest='superupdate_ar', default=0.26)\\n # -- superupdate acceptance rate tolerance argument (OPTIONAL)\\n runparser.add_argument('--superupdate-ar-tol', help=helpdict['superupdate-ar-tol'], type=float,\\n dest='superupdate_ar_tol', default=0.01)\\n # -- adaptive jumping factor Metropolis Hastings (OPTIONAL)\\n runparser.add_argument('--adaptive', help=helpdict['adaptive'], type=int,\\n dest='adaptive', default=0)\\n # -- adaptive ts argument (OPTIONAL)\\n runparser.add_argument('--adaptive-ts', help=helpdict['adaptive-ts'], type=int,\\n dest='adaptive_ts', default=1000)\\n\\n # -- jumping factor (OPTIONAL)\\n runparser.add_argument('-f', help=helpdict['f'], type=float,\\n dest='jumping_factor', default=2.4)\\n # -- temperature (OPTIONAL)\\n runparser.add_argument('-T', help=helpdict['T'], type=float,\\n dest='temperature', default=1.0)\\n # -- minimize (OPTIONAL)\\n runparser.add_argument('--minimize', help=helpdict['minimize'],\\n action='store_true')\\n # -- minimize argument, minimization tolerance (OPTIONAL)\\n runparser.add_argument('--minimize-tol', help=helpdict['minimize-tol'], type=float,\\n dest='minimize_tol', default=0.00001)\\n # -- fisher (OPTIONAL)\\n runparser.add_argument('--fisher', help=helpdict['fisher'],\\n action='store_true')\\n # -- fisher argument (OPTIONAL)\\n runparser.add_argument('--fisher-asymmetric', help=helpdict['fisher-asymmetric'],\\n dest='fisher_asymmetric',action='store_true')\\n # -- fisher step iteration (OPTIONAL)\\n runparser.add_argument('--fisher-step-it', help=helpdict['fisher-step-it'],\\n dest='fisher_step_it', default=10)\\n # -- fisher step iteration argument, -deltaloglkl target (OPTIONAL)\\n runparser.add_argument('--fisher-delta', help=helpdict['fisher-delta'], type=float,\\n dest='fisher_delta', default=0.1)\\n # -- fisher step iteration argument, -deltaloglkl tolerance (OPTIONAL)\\n runparser.add_argument('--fisher-tol', help=helpdict['fisher-tol'], type=float,\\n dest='fisher_tol', default=0.05)\\n # -- fisher symmetric likelihood assumption threshold (OPTIONAL)\\n runparser.add_argument('--fisher-sym-lkl', help=helpdict['fisher-sym-lkl'], type=float,\\n dest='fisher_sym_lkl', default=0.1)\\n # -- configuration file (OPTIONAL)\\n runparser.add_argument('--conf', help=helpdict['conf'],\\n type=str, dest='config_file',\\n default='default.conf')\\n # -- arbitrary numbering of an output chain (OPTIONAL)\\n runparser.add_argument('--chain-number', help=helpdict['chain-number'])\\n # -- stop run after first successful update using --update (EXPERIMENTAL)\\n runparser.add_argument('--stop-after-update', help=helpdict['stop-after-update'],\\n dest='stop_after_update', action='store_true')\\n # display option\\n runparser.add_argument('--display-each-chi2', help=helpdict['display-each-chi2'],\\n dest='display_each_chi2', action='store_true')\\n # -- parallel chains without MPI (OPTIONAL)\\n runparser.add_argument('--parallel-chains', help=helpdict['parallel-chains'],\\n action='store_true')\\n\\n ###############\\n # MCMC restart from chain or best fit file\\n runparser.add_argument('-r', '--restart', help=helpdict['r'],\\n type=existing_file, dest='restart')\\n runparser.add_argument('-b', '--bestfit', dest='bf', help=helpdict['b'],\\n type=existing_file)\\n\\n ###############\\n # Silence the output (no print on the console)\\n runparser.add_argument('--silent', help=helpdict['silent'],\\n action='store_true')\\n ###############\\n # Adding new derived parameters to a run\\n runparser.add_argument(\\n '--Der-target-folder', dest=\\\"Der_target_folder\\\",\\n help=helpdict['Der-target-folder'], type=str, default='')\\n runparser.add_argument(\\n '--Der-param-list', dest='derived_parameters',\\n help=helpdict['Der-param-list'], type=str, default='', nargs='+')\\n\\n ###############\\n # Importance Sampling Arguments\\n runparser.add_argument(\\n '--IS-starting-folder', dest='IS_starting_folder',\\n help=helpdict['IS-starting-folder'], type=str, default='', nargs='+')\\n\\n ###############\\n # We need the following so the run does not crash if one of the external\\n # samplers is not correctly installed despite not being used\\n from contextlib import contextmanager\\n import sys, os\\n\\n @contextmanager\\n def suppress_stdout():\\n with open(os.devnull, \\\"w\\\") as devnull:\\n old_stdout = sys.stdout\\n sys.stdout = devnull\\n try:\\n yield\\n finally:\\n sys.stdout = old_stdout\\n\\n ###############\\n # MultiNest arguments (all OPTIONAL and ignored if not \\\"-m=NS\\\")\\n # The default values of -1 mean to take the PyMultiNest default values\\n try:\\n with suppress_stdout():\\n from MultiNest import NS_prefix, NS_user_arguments\\n NSparser = runparser.add_argument_group(\\n title=\\\"MultiNest\\\",\\n description=\\\"Run the MCMC chains using MultiNest\\\"\\n )\\n for arg in NS_user_arguments:\\n NSparser.add_argument('--'+NS_prefix+arg,\\n default=-1,\\n **NS_user_arguments[arg])\\n except ImportError:\\n # Not defined if not installed\\n pass\\n except:\\n warnings.warn('PyMultiNest detected but MultiNest likely not installed correctly. '\\n 'You can safely ignore this if not running with option -m NS')\\n\\n ###############\\n # PolyChord arguments (all OPTIONAL and ignored if not \\\"-m=PC\\\")\\n # The default values of -1 mean to take the PyPolyChord default values\\n try:\\n with suppress_stdout():\\n from PolyChord import PC_prefix, PC_user_arguments\\n PCparser = runparser.add_argument_group(\\n title=\\\"PolyChord\\\",\\n description=\\\"Run the MCMC chains using PolyChord\\\"\\n )\\n for arg in PC_user_arguments:\\n PCparser.add_argument('--'+PC_prefix+arg,\\n default=-1,\\n **PC_user_arguments[arg])\\n except ImportError:\\n # Not defined if not installed\\n pass\\n except:\\n warnings.warn('PyPolyChord detected but PolyChord likely not installed correctly. '\\n 'You can safely ignore this if not running with option -m PC')\\n\\n ###############\\n # CosmoHammer arguments (all OPTIONAL and ignored if not \\\"-m=CH\\\")\\n # The default values of -1 mean to take the CosmoHammer default values\\n try:\\n with suppress_stdout():\\n from cosmo_hammer import CH_prefix, CH_user_arguments\\n CHparser = runparser.add_argument_group(\\n title=\\\"CosmoHammer\\\",\\n description=\\\"Run the MCMC chains using the CosmoHammer framework\\\")\\n for arg in CH_user_arguments:\\n CHparser.add_argument('--'+CH_prefix+arg,\\n default=-1,\\n **CH_user_arguments[arg])\\n except ImportError:\\n # Not defined if not installed\\n pass\\n except:\\n warnings.warn('CosmoHammer detected but emcee likely not installed correctly. '\\n 'You can safely ignore this if not running with option -m CH')\\n\\n ###############\\n # Information\\n infoparser = add_subparser(subparser, 'info',\\n help=\\\"analyze the MCMC chains\\\")\\n\\n # -- folder to analyze\\n infoparser.add_argument('files', help=helpdict['files'],\\n nargs='+')\\n # Silence the output (no print on the console)\\n infoparser.add_argument('--silent', help=helpdict['silent'],\\n action='store_true')\\n # -- to only write the covmat and bestfit, without computing the posterior\\n infoparser.add_argument('--minimal', help=helpdict['minimal'],\\n action='store_true')\\n # -- number of bins (defaulting to 20)\\n infoparser.add_argument('--bins', help=helpdict['bins'],\\n type=int, default=20)\\n # -- temperature (OPTIONAL)\\n infoparser.add_argument('-T', help=helpdict['T'], type=float,\\n dest='temperature', default=1.0)\\n # -- deprecated: remove the mean-likelihood line\\n infoparser.add_argument('--no-mean', help=helpdict['no-mean'],\\n dest='mean_likelihood_old', action='store_false')\\n # -- plot the mean-likelihood line\\n infoparser.add_argument('--plot-mean', help=helpdict['plot-mean'],\\n dest='mean_likelihood', action='store_true')\\n # -- to remove the mean and 68% limits on top of each 1D plot\\n infoparser.add_argument('--short-title-1d', help=helpdict['short-title-1d'],\\n dest='short_title_1d', action='store_true')\\n # -- possible plot file describing custom commands\\n infoparser.add_argument('--extra', help=helpdict['extra'],\\n dest='optional_plot_file', default='')\\n # -- if you just want the covariance matrix, use this option\\n infoparser.add_argument('--noplot', help=helpdict['noplot'],\\n dest='plot', action='store_false')\\n # -- if you just want to output 1d posterior distributions (faster)\\n infoparser.add_argument('--noplot-2d', help=helpdict['noplot-2d'],\\n dest='plot_2d', action='store_false')\\n # -- if you just want to output triangle with 2d contours\\n infoparser.add_argument('--noplot-2d-diag', help=helpdict['noplot-2d-diag'],\\n dest='plot_diag', action='store_false')\\n # -- when plotting 2d posterior distribution, use contours and not contours\\n # filled (might be useful when comparing several folders)\\n infoparser.add_argument('--contours-only', help=helpdict['contours-only'],\\n dest='contours_only', action='store_true')\\n # -- if you want to output every single subplots\\n infoparser.add_argument('--all', help=helpdict['all'], dest='subplot',\\n action='store_true')\\n # -- to change the extension used to output files (pdf is the default one,\\n # but takes long, valid options are png and eps)\\n infoparser.add_argument('--ext', help=helpdict['ext'],\\n type=str, dest='extension', default='pdf')\\n # -- to set manually the number of plots per hoorizontal raw in 1d plot\\n infoparser.add_argument('--num-columns-1d', help=helpdict['num-columns-1d'],\\n type=int, dest='num_columns_1d')\\n # -- also analyze the non-markovian part of the chains\\n infoparser.add_argument('--keep-non-markovian', help=helpdict['keep-non-markovian'],\\n dest='markovian', action='store_false')\\n # -- force only analyzing the markovian part of the chains\\n infoparser.add_argument('--keep-only-markovian', help=helpdict['keep-only-markovian'],\\n dest='only_markovian', action='store_true')\\n # -- fraction of chains to be analyzed after burn-in removal (defaulting to 1.0)\\n infoparser.add_argument('--keep-fraction', help=helpdict['keep-fraction'],\\n type=float, dest='keep_fraction', default=1.0)\\n # -- calculate the covariant matrix when analyzing the chains\\n infoparser.add_argument('--want-covmat', help=helpdict['want-covmat'],\\n dest='want_covmat', action='store_true')\\n # -------------------------------------\\n # Further customization\\n # -- fontsize of plots (defaulting to 16)\\n infoparser.add_argument('--fontsize', help=helpdict['fontsize'],\\n type=int, default=16)\\n # -- ticksize of plots (defaulting to 14)\\n infoparser.add_argument('--ticksize', help=helpdict['ticksize'],\\n type=int, default=14)\\n # -- linewidth of 1d plots (defaulting to 4, 2 being a bare minimum for\\n # legible graphs\\n infoparser.add_argument('--line-width', help=helpdict['line-width'],\\n type=int, default=4)\\n # -- number of decimal places that appear on the tick legend. If you want\\n # to increase the number of ticks, you should reduce this number\\n infoparser.add_argument('--decimal', help=helpdict['decimal'], type=int,\\n default=3)\\n # -- number of ticks that appear on the graph.\\n infoparser.add_argument('--ticknumber', help=helpdict['ticknumber'],\\n type=int, default=3)\\n # -- legend type, to choose between top (previous style) to sides (new\\n # style). It modifies the place where the name of the variable appear.\\n infoparser.add_argument('--legend-style', help=helpdict['legend-style'],\\n type=str, choices=['sides', 'top'],\\n default='sides')\\n # -- width of gaussian smoothing for plotting posteriors,\\n # in units of bin size, increase for smoother data.\\n infoparser.add_argument('--gaussian-smoothing', help=helpdict['gaussian-smoothing'],\\n type=float, default=0.5)\\n # interpolation factor for plotting posteriors, 1 means no interpolation,\\n # increase for smoother curves (it means that extra bins are created\\n # and interpolated between computed bins)\\n infoparser.add_argument('--interpolation-smoothing', help=helpdict['interpolation-smoothing'],\\n type=int, default=4)\\n # -- plot Fisher ellipses\\n infoparser.add_argument('--plot-fisher', help=helpdict['plot-fisher'],\\n dest='plot_fisher',action='store_true')\\n infoparser.add_argument('--center-fisher', help=helpdict['center-fisher'],\\n dest='center_fisher',action='store_true')\\n\\n infoparser.add_argument('--posterior-smoothing', help=helpdict['posterior-smoothing'],\\n type=int, default=5)\\n\\n return parser\",\n \"def setup_args() -> argparse.ArgumentParser:\\n main_parser = argparse.ArgumentParser(prog=\\\"gh\\\")\\n subparsers = main_parser.add_subparsers(dest=\\\"subparser\\\")\\n command_parser = subparsers.add_parser(\\\"commands\\\", help=\\\"Runs a command\\\")\\n command_parser.add_argument(\\n \\\"choice\\\",\\n help=\\\"The chosen command to run\\\",\\n choices=gh.commands.OPTIONS.keys(),\\n )\\n analytics_parser = subparsers.add_parser(\\\"analytics\\\", help=\\\"Runs an analysis\\\")\\n analytics_parser.add_argument(\\n \\\"choice\\\",\\n help=\\\"The chosen analysis to run\\\",\\n choices=gh.analytics.OPTIONS.keys(),\\n )\\n return main_parser\",\n \"def vultr_cli(parser):\\n return parser\",\n \"def parser(subparsers, repo):\\n desc = 'show status of the repo'\\n status_parser = subparsers.add_parser(\\n 'status', help=desc, description=desc.capitalize(), aliases=['st'])\\n status_parser.add_argument(\\n 'paths', nargs='*', help='the specific path(s) to status',\\n action=helpers.PathProcessor, repo=repo)\\n status_parser.set_defaults(func=main)\",\n \"def add_hic_interval_subparser(subparsers):\\n parser = subparsers.add_parser(\\\"hic-interval\\\",\\n help=\\\"HiFive Binning Function: Create a tabular interaction file containing data from a HiFive HiC project. Data are a genomic-interval format (chr1 start1 stop1 chr2 start2 stop2).\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--chromosome\\\", dest=\\\"chrom\\\", default=None, required=True, type=str,\\n help=\\\"The chromosome from which to pull interaction data from.\\\")\\n parser.add_argument(\\\"--chromosome2\\\", dest=\\\"chrom2\\\", default=None, required=False, type=str,\\n help=\\\"The second chromosome from which to pull interaction data from if pulling trans data.\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--start\\\", dest=\\\"start\\\", default=None, required=False, type=int,\\n help=\\\"The start coordinate of the pulled region to return. (None indicates the first valid bin on the chromosome) [default %(default)s]\\\")\\n parser.add_argument(\\\"-e\\\", \\\"--stop\\\", dest=\\\"stop\\\", default=None, required=False, type=int,\\n help=\\\"The stop coordinate of the pulled region to return. (None indicates the last valid bin on the chromosome) [default %(default)s]\\\")\\n parser.add_argument(\\\"--start2\\\", dest=\\\"start2\\\", default=None, required=False, type=int,\\n help=\\\"The start coordinate of the second chromosome pulled region to return. (None indicates the first valid bin on the chromosome) [default %(default)s]\\\")\\n parser.add_argument(\\\"--stop2\\\", dest=\\\"stop2\\\", default=None, required=False, type=int,\\n help=\\\"The stop coordinate of the second chromosome pulled region to return. (None indicates the last valid bin on the chromosome) [default %(default)s]\\\")\\n parser.add_argument(\\\"-b\\\", \\\"--binsize\\\", dest=\\\"binsize\\\", default=10000, type=int,\\n help=\\\"The size of bins, in base pairs, to group data into. [default: %(default)s]\\\")\\n parser.add_argument(\\\"-m\\\", \\\"--max-distance\\\", dest=\\\"maxdist\\\", default=None, type=int,\\n help=\\\"The maximum interaction distance to return (None indicates no maximum). [default: %(default)s]\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--data-type\\\", dest=\\\"datatype\\\", default=\\\"fend\\\",\\n help=\\\"Which corrections (if any) to apply to counts. [default: %(default)s]\\\",\\n choices=[\\\"raw\\\", \\\"fend\\\", \\\"distance\\\", \\\"enrichment\\\", \\\"expected\\\"])\\n parser.add_argument(\\\"-M\\\", \\\"--matrix\\\", dest=\\\"matrix\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Store output as a tab-separated matrix of values.\\\")\\n parser.add_argument(\\\"-y\\\", \\\"--dynamically-bin\\\", dest=\\\"dynamic\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Dynamically bin heatmap.\\\")\\n parser.add_argument(\\\"-x\\\", \\\"--expansion-binsize\\\", dest=\\\"expbinsize\\\", default=10000, type=int,\\n help=\\\"The size of bins, in base pairs, to group data into for expanding under-populated bins. [default: %(default)s]\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--minobservations\\\", dest=\\\"minobs\\\", default=20, type=int,\\n help=\\\"The minimum number of observed reads in a bin for it to be considered valid. [default: %(default)s]\\\")\\n parser.add_argument(\\\"-a\\\", \\\"--search-distance\\\", dest=\\\"search\\\", default=0, type=int,\\n help=\\\"The furthest distance from the bin minpoint to expand bounds. If set to zero, there is no limit on expansion distance. [default: %(default)s]\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--remove-failed\\\", dest=\\\"remove\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"If a non-zero 'search-distance' is given, it is possible for a bin not to meet the 'minobservations' criteria before stopping looking. If this occurs and 'remove-failed' is set, the observed and expected values for that bin are zero.\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--image-file\\\", dest=\\\"image\\\", default=None, type=str,\\n help=\\\"Save the data as an image to this file.\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--pdf\\\", dest=\\\"pdf\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Format the image in PDF format. [default: %(default)s]\\\")\\n parser.add_argument(\\\"-r\\\", \\\"--rotate\\\", dest=\\\"rotate\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Rotate the plot 45 degrees (cis binned only). [default: %(default)s]\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--ticks\\\", dest=\\\"ticks\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Add tick marks and labels to the plot (pdf format and binned only). [default: %(default)s]\\\")\\n parser.add_argument(\\\"-l\\\", \\\"--legend\\\", dest=\\\"legend\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Add color scale to the plot (pdf format only). [default: %(default)s]\\\")\\n parser.add_argument(\\\"-k\\\", \\\"--keyword\\\", dest=\\\"keywords\\\", default=[], type=str, action='append',\\n help=\\\"Additional keyword arguments to pass to plotting function.\\\")\\n add_silent_argument(parser)\\n parser.add_argument(dest=\\\"project\\\", type=str,\\n help=\\\"The name of a HiFive HiC project file to pull data from.\\\")\\n parser.add_argument(dest=\\\"output\\\", type=str,\\n help=\\\"The name of the file to write HiC interval to.\\\")\\n return\",\n \"def get_parser():\\n parser = ArgumentParser(description=__doc__,\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n prog='pv2')\\n subparsers = parser.add_subparsers(dest='cmd')\\n # subparsers.add_parser('selfcheck',\\n # add_help=False,\\n # help=\\\"Self-check of the sst toolkit.\\\")\\n # parser.add_argument('--version',\\n # action='version',\\n # version=('sst %s' % str(sst.__version__)))\\n subparsers.add_parser('eval',\\n add_help=False,\\n parents=[evaluate.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Evaluate a single image\\\"))\\n subparsers.add_parser('train',\\n add_help=False,\\n parents=[train.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Train a new model.\\\"))\\n subparsers.add_parser('plot',\\n add_help=False,\\n parents=[plot.get_parser()],\\n formatter_class=ArgumentDefaultsHelpFormatter,\\n help=(\\\"Plot summary information.\\\"))\\n return parser\",\n \"def add_list_parser(subparsers):\\n list_parser = subparsers.add_parser(\\\"list\\\")\\n list_parser.set_defaults(func=list_cli.main)\\n list_parser.add_argument('--scenarios', '-s',\\n dest=\\\"scenarios\\\",\\n action='store_true',\\n help='List Scenarios')\",\n \"def create_parser(general_defaults={}, constants={}, subcommand=MAIN):\\n\\n defaults = general_defaults['BigMLer']\\n\\n version = pkg_resources.require(\\\"BigMLer\\\")[0].version\\n version_text = \\\"\\\"\\\"\\\\\\nBigMLer %s - A Higher Level API to BigML's API\\nCopyright 2012-2015 BigML\\n\\nLicensed under the Apache License, Version 2.0 (the \\\\\\\"License\\\\\\\"); you may\\nnot use this file except in compliance with the License. You may obtain\\na copy of the License at http://www.apache.org/licenses/LICENSE-2.0\\n\\nUnless required by applicable law or agreed to in writing, software\\ndistributed under the License is distributed on an \\\\\\\"AS IS\\\\\\\" BASIS, WITHOUT\\nWARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\\nLicense for the specific language governing permissions and limitations\\nunder the License.\\\"\\\"\\\" % version\\n constants['version_text'] = version_text\\n main_parser = argparse.ArgumentParser(\\n description=\\\"A higher level API to BigML's API.\\\",\\n epilog=\\\"Happy predictive modeling!\\\",\\n version=version_text,\\n formatter_class=argparse.RawTextHelpFormatter)\\n subparsers = main_parser.add_subparsers()\\n\\n # list of options\\n common_options = get_common_options(defaults=defaults, constants=constants)\\n delete_options = get_delete_options(defaults=defaults)\\n source_options = get_source_options(defaults=defaults)\\n dataset_options = get_dataset_options(defaults=defaults)\\n test_options = get_test_options(defaults=defaults)\\n multi_label_options = get_multi_label_options(defaults=defaults)\\n\\n # subcommand options\\n subcommand_options = {}\\n # specific options\\n subcommand_options[\\\"main\\\"] = get_main_options(defaults=defaults,\\n constants=constants)\\n # general options\\n subcommand_options[\\\"main\\\"].update(common_options)\\n subcommand_options[\\\"main\\\"].update(source_options)\\n subcommand_options[\\\"main\\\"].update(dataset_options)\\n subcommand_options[\\\"main\\\"].update(multi_label_options)\\n subcommand_options[\\\"main\\\"].update(test_options)\\n subcommand_options[\\\"main\\\"].update({\\n '--source-tag': delete_options['--source-tag'],\\n '--dataset-tag': delete_options['--dataset-tag'],\\n '--model-tag': delete_options['--model-tag'],\\n '--ensemble-tag': delete_options['--ensemble-tag'],\\n '--prediction-tag': delete_options['--prediction-tag'],\\n '--batch-prediction-tag': delete_options['--batch-prediction-tag']})\\n\\n main_options = subcommand_options[\\\"main\\\"]\\n\\n defaults = general_defaults[\\\"BigMLer analyze\\\"]\\n subcommand_options[\\\"analyze\\\"] = get_analyze_options(defaults=defaults)\\n subcommand_options[\\\"analyze\\\"].update(common_options)\\n # we add the options that should be transmitted to bigmler main subcommands\\n # in analyze\\n subcommand_options[\\\"analyze\\\"].update({\\n '--objective': main_options['--objective'],\\n '--max-parallel-models': main_options['--max-parallel-models'],\\n '--max-parallel-evaluations': main_options[\\n '--max-parallel-evaluations'],\\n '--model-fields': main_options['--model-fields'],\\n '--balance': main_options['--balance'],\\n '--no-balance': main_options['--no-balance'],\\n '--number-of-models': main_options['--number-of-models'],\\n '--sample-rate': main_options['--sample-rate'],\\n '--replacement': main_options['--replacement'],\\n '--missing-splits': main_options['--missing-splits'],\\n '--pruning': main_options['--pruning'],\\n '--weight-field': main_options['--weight-field'],\\n '--replacement': main_options['--replacement'],\\n '--objective-weights': main_options['--objective-weights'],\\n '--replacement': main_options['--replacement'],\\n '--model-attributes': main_options['--model-attributes'],\\n '--ensemble-attributes': main_options['--ensemble-attributes'],\\n '--tlp': main_options['--tlp'],\\n '--randomize': main_options['--randomize']})\\n\\n defaults = general_defaults[\\\"BigMLer cluster\\\"]\\n subcommand_options[\\\"cluster\\\"] = get_cluster_options(defaults=defaults)\\n # general options\\n subcommand_options[\\\"cluster\\\"].update(common_options)\\n subcommand_options[\\\"cluster\\\"].update(source_options)\\n subcommand_options[\\\"cluster\\\"].update(dataset_options)\\n subcommand_options[\\\"cluster\\\"].update(test_options)\\n subcommand_options[\\\"cluster\\\"].update({\\n '--cpp': main_options['--cpp'],\\n '--fields-map': main_options['--fields-map'],\\n '--source-tag': delete_options['--source-tag'],\\n '--dataset-tag': delete_options['--dataset-tag'],\\n '--cluster-tag': delete_options['--cluster-tag'],\\n '--centroid-tag': delete_options['--centroid-tag'],\\n '--batch-centroid-tag': delete_options['--batch-centroid-tag'],\\n '--prediction-info': main_options['--prediction-info'],\\n '--prediction-header': main_options['--prediction-header'],\\n '--prediction-fields': main_options['--prediction-fields'],\\n '--reports': main_options['--reports'],\\n '--remote': main_options['--remote'],\\n '--no-batch': main_options['--no-batch']})\\n\\n defaults = general_defaults[\\\"BigMLer anomaly\\\"]\\n subcommand_options[\\\"anomaly\\\"] = get_anomaly_options(defaults=defaults)\\n # general options\\n subcommand_options[\\\"anomaly\\\"].update(common_options)\\n subcommand_options[\\\"anomaly\\\"].update(source_options)\\n subcommand_options[\\\"anomaly\\\"].update(dataset_options)\\n subcommand_options[\\\"anomaly\\\"].update(test_options)\\n subcommand_options[\\\"anomaly\\\"].update({\\n '--cpp': main_options['--cpp'],\\n '--fields-map': main_options['--fields-map'],\\n '--source-tag': delete_options['--source-tag'],\\n '--dataset-tag': delete_options['--dataset-tag'],\\n '--anomaly-tag': delete_options['--anomaly-tag'],\\n '--anomaly-score-tag': delete_options['--anomaly-score-tag'],\\n '--batch-anomaly-score-tag': delete_options['--batch-anomaly-score-tag'],\\n '--prediction-info': main_options['--prediction-info'],\\n '--prediction-header': main_options['--prediction-header'],\\n '--prediction-fields': main_options['--prediction-fields'],\\n '--reports': main_options['--reports'],\\n '--remote': main_options['--remote'],\\n '--no-batch': main_options['--no-batch']})\\n\\n\\n subcommand_options[\\\"delete\\\"] = delete_options\\n subcommand_options[\\\"delete\\\"].update(common_options)\\n\\n for subcommand in SUBCOMMANDS:\\n subparser = subparsers.add_parser(subcommand)\\n parser_add_options(subparser, subcommand_options[subcommand])\\n\\n # options to be transmitted from analyze to main\\n chained_options = [\\n \\\"--debug\\\", \\\"--dev\\\", \\\"--username\\\", \\\"--api-key\\\", \\\"--resources-log\\\",\\n \\\"--store\\\", \\\"--clear-logs\\\", \\\"--max-parallel-models\\\",\\n \\\"--max-parallel-evaluations\\\", \\\"--objective\\\", \\\"--tag\\\",\\n \\\"--no-tag\\\", \\\"--no-debug\\\", \\\"--no-dev\\\", \\\"--model-fields\\\", \\\"--balance\\\",\\n \\\"--verbosity\\\", \\\"--resume\\\", \\\"--stack_level\\\", \\\"--no-balance\\\",\\n \\\"--args-separator\\\", \\\"--name\\\"]\\n\\n return main_parser, chained_options\",\n \"def add_rm_parser(subparsers):\\n rm_parser = subparsers.add_parser(\\\"rm\\\")\\n rm_parser.set_defaults(func=rm_cli.main)\\n rm_parser.add_argument('--scenario', '-s',\\n dest=\\\"scenario\\\",\\n help='Predefined scenario to use for exection')\\n rm_parser.add_argument('--platform', dest=\\\"platform\\\",\\n help=\\\"The platform to use \\\\\\n(podman, docker, terraform, shell, python)\\\")\\n rm_parser.add_argument('--vars', dest=\\\"vars\\\",\\n default=\\\"\\\",\\n help=\\\"extra variables\\\")\\n rm_parser.add_argument('--debug', dest=\\\"debug\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Enable debug level logging\\\")\",\n \"def generate_main_parser() -> ArgumentParser:\\n # Create parser\\n parser = ArgumentParser(\\n description=\\\"Command line interface tool for iic2343.\\\",\\n )\\n\\n # Add version flag\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--version\\\",\\n action=\\\"version\\\",\\n version=f\\\"iic2343 version {iic2343.__version__}\\\",\\n )\\n\\n # Create subparsers\\n subparsers = parser.add_subparsers(help=\\\"Action to be executed.\\\")\\n\\n # Serial ports subparser\\n generate_serial_ports_subparser(subparsers)\\n\\n return parser\",\n \"def subdivide_parser(subparser):\\n parser = subparser.add_parser('subdivide',\\\\\\n help='will subdivide a mesh by one iteration '\\\\\\n '(splitting all triangles in four others)'\\\\\\n ' deprecated use converter.py refine instead')\\n parser = add_arg(parser, 'replace')\\n parser.add_argument(\\\"args\\\", metavar='SELAFIN file', nargs=\\\"+\\\")\\n\\n return subparser\",\n \"def build_parser(self, add_help=True):\\n self.parser = argparse.ArgumentParser(\\n description=self.description, add_help=add_help\\n )\\n self.parser.prog = f\\\"python -m {self.package}.{self.module_name}\\\"\\n self.parser.add_argument(\\n \\\"config_file\\\", help=\\\"Path/name of YAML configuration file for NEMO nowcast.\\\"\\n )\",\n \"def add_command(subparsers):\\n\\n parser = subparsers.add_parser('create', help=create.__doc__)\\n\\n parser.add_argument('-r', '--recreate', action='store_true', help='If set, I\\\\'ll first erase the current database')\\n parser.add_argument('-v', '--verbose', action='count', help='Increase verbosity?')\\n parser.add_argument('-d', '--image-dir', default='/idiap/project/hface/databases/polimetric_thermal_database/Registered/', help=\\\"Change the relative path to the directory containing the images of the Polarimetric database.\\\")\\n\\n parser.set_defaults(func=create) #action\",\n \"def _spawn_cli() -> GooeyParser:\\n parser = _add_gui_arg(_cli())\\n\\n return vars(parser.parse_args())\",\n \"def _cli() -> GooeyParser:\\n parser = GooeyParser(description='A template CLI app with optional GUI.')\\n\\n return parser\",\n \"def get_parser(subparsers):\\n parser = subparsers.add_parser(\\\"compile\\\", description=\\\"Compile model using ML on MCU flow.\\\")\\n parser.set_defaults(flow_func=handle)\\n add_compile_options(parser)\\n return parser\",\n \"def internal_setup_options_parser(self, argparser):\\n self.setup_options_parser(argparser)\\n subparser = argparser.add_subparsers(dest='verb')\\n for (name, verb) in self.verbs.items():\\n vparser = subparser.add_parser(name, help=verb.help)\\n verb.setup_options_parser(vparser)\",\n \"def __init__(self, *args, **kwargs):\\n # The subcommand array, with the help command included.\\n self.subcommands = list(kwargs.pop('subcommands', []))\\n self.subcommands.append(self._HelpSubcommand)\\n\\n # A more helpful default usage.\\n if 'usage' not in kwargs:\\n kwargs['usage'] = \\\"\\\"\\\"\\n %prog [global options] COMMAND [ARGS...]\\n %prog help COMMAND\\\"\\\"\\\"\\n\\n # Super constructor.\\n optparse.OptionParser.__init__(self, *args, **kwargs)\\n\\n # Adjust the help-visible name of each subcommand.\\n for subcommand in self.subcommands:\\n subcommand.parser.prog = '%s %s' % \\\\\\n (self.get_prog_name(), subcommand.name)\\n\\n # Our root parser needs to stop on the first unrecognized argument.\\n self.disable_interspersed_args()\",\n \"def add_arguments(self, sub_parser):\\n sp = sub_parser\\n sp.add_argument('--fork', nargs='?')\",\n \"def get_parser(subparsers):\\n parser = subparsers.add_parser(\\\"models\\\", description=\\\"Manage ML on MCU models.\\\")\\n parser.set_defaults(func=handle)\\n add_common_options(parser)\\n add_context_options(parser)\\n add_models_options(parser)\\n return parser\",\n \"def add_command_parsers(parser, logparser):\\n subparsers = parser.add_subparsers(metavar='Command')\\n help_text = 'ONE OF THE FOLLOWING:\\\\n'\\n available_commands = find_commands(\\n os.path.join(os.path.abspath(os.path.dirname(__file__)), os.path.pardir, 'functions'))\\n max_length = max([len(a) for a in available_commands])\\n for command in available_commands:\\n child_parser = subparsers.add_parser(command, parents=[logparser])\\n call = importlib.import_module('functions.%s'% command)\\n if hasattr(call, 'set_argparser'):\\n call.set_argparser(child_parser)\\n else:\\n child_parser.description = 'Description is missing'\\n help_text += command + \\\": \\\" + \\\" \\\"*(max_length-len(command)) + ('\\\\n'+' '*(max_length+2)\\n ).join(textwrap.wrap(child_parser.description,60)) + '\\\\n'\\n child_parser.set_defaults(func=call.main)\\n subparsers.help = help_text + '\\\\nType \\\"Command --help\\\" for more information about given command'\",\n \"def make_parser():\\n\\n parser = argparse.ArgumentParser(description='Inference engine.')\\n subparsers = parser.add_subparsers(dest=\\\"subcommand\\\")\\n subparsers.required = True\\n solver_subparser = subparsers.add_parser('run')\\n solver_subparser.add_argument(\\n '-v', '--verbose', help='enable verbose mode.', action='store_true'\\n )\\n solver_subparser.add_argument(\\n '-d', '--debug', help='enable debug mode.', action='store_true'\\n )\\n solver_subparser.add_argument(\\n 'filename', type=str,\\n help='filename containing the instructions to process.'\\n )\\n return parser\",\n \"def get_argument_parser(self):\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers(dest='command')\\n fetch_parser = subparsers.add_parser('fetch', help='fetches and displays a release from discogs')\\n fetch_parser.add_argument('discogs_id', help='the ID of the release')\\n rip_parser = subparsers.add_parser('rip', help='rips the current CD to WAV')\\n rip_parser.add_argument('--destination', help='optional destination for the CD rip')\\n search_parser = subparsers.add_parser(\\n 'search',\\n prog='search',\\n help='performs a very simple search on discogs')\\n search_parser.add_argument('term', help='the term to search for')\\n encode_parser = subparsers.add_parser(\\n 'encode', help='Encodes a CD or a set of WAV files to mp3.')\\n encode_parser.add_argument(\\n 'encoding_from', choices=['cd', 'wav'], help='The source to encode from.')\\n encode_parser.add_argument(\\n 'encoding_to', choices=['mp3', 'flac'], help='The destination to encode to.')\\n encode_parser.add_argument(\\n '--source', help='The destination of the source wav file. This can be a file or directory.')\\n encode_parser.add_argument(\\n '--destination', help='The destination of the resulting mp3 or flac. This can be a file or directory.')\\n encode_parser.add_argument(\\n '--keep-source', action='store_true', help='If encoding from wav, use this to keep the original wav being removed.')\\n encode_parser.add_argument(\\n '--collapse-index-tracks', action='store_true', help='If set this will collapse any subtracks to a single track.')\\n encode_parser.add_argument(\\n '--discogs-id', help='The discogs ID for the release. When this is used metadata from the discogs release will be applied to the encoded files.')\\n decode_parser = subparsers.add_parser('decode', help='Decodes a set of FLAC or MP3 files to WAV.')\\n decode_parser.add_argument(\\n 'decode_from', choices=['flac', 'mp3'], help='The source to decode from.')\\n decode_parser.add_argument(\\n '--source', help='The destination of the source file. This can be a file or directory.')\\n decode_parser.add_argument(\\n '--destination', help='The destination of the resulting wav. This can be a file or directory.')\\n tag_parser = subparsers.add_parser('tag', help='Tags an audio file')\\n tag_parser.add_argument(\\n 'action', choices=['add', 'remove'], help='The tagging action to be performed. A tag can be added or removed.')\\n tag_parser.add_argument(\\n 'format', choices=['mp3', 'flac'], help='The file format of the audio file being tagged.')\\n tag_parser.add_argument(\\n '--collapse-index-tracks', action='store_true', help='If set this will collapse any subtracks to a single track.')\\n tag_parser.add_argument(\\n '--source',\\n help='The source audio files to tag. This can be a file or a directory. If the source is omitted, the files in the current working directory will be used.')\\n tag_parser.add_argument('--discogs-id', help='The discogs ID for the release. When this is used metadata from the discogs release will be applied to the tagged files.')\\n tag_parser.add_argument('--artist', help='The artist to use for the tag.')\\n tag_parser.add_argument('--album-artist', help='The album artist to use for the tag.')\\n tag_parser.add_argument('--album', help='The album to use for the tag.')\\n tag_parser.add_argument('--title', help='The title to use for the tag.')\\n tag_parser.add_argument('--year', help='The year to use for the tag.')\\n tag_parser.add_argument('--genre', help='The year to use for the tag.')\\n tag_parser.add_argument('--track-number', help='The track number to use for the tag.')\\n tag_parser.add_argument('--track-total', help='The track total to use for the tag.')\\n tag_parser.add_argument('--disc-number', help='The disc number to use for the tag.')\\n tag_parser.add_argument('--disc-total', help='The disc total to use for the tag.')\\n tag_parser.add_argument('--comment', help='The comment for the tag.')\\n artwork_parser = subparsers.add_parser('artwork', help='adds or removes artwork from a file')\\n artwork_parser.add_argument(\\n 'action', choices=['add', 'remove'], help='The artwork action to be performed. The artwork can be added or removed.')\\n artwork_parser.add_argument(\\n 'type', choices=['mp3', 'flac'], help='The type of file to apply the artwork to.')\\n artwork_parser.add_argument(\\n '--source', help='The destination file or directory to apply the artwork to. If there is no source then any artwork in the current directory will be used.')\\n artwork_parser.add_argument(\\n '--destination', help='The destination file or directory to apply the artwork to. If there is no destination then the current directory will be used.')\\n mix_parser = subparsers.add_parser('mix', help='adds a mix')\\n mix_parser.add_argument('source', help='the source of the mix')\\n mix_parser.add_argument('--artist', help='The artist to use for the tag.')\\n mix_parser.add_argument('--album', help='The album to use for the mix.')\\n mix_parser.add_argument('--title', help='The title to use for the mix.')\\n mix_parser.add_argument('--year', help='The year to use for the mix.')\\n mix_parser.add_argument('--comment', help='The comment for the mix.')\\n return parser\",\n \"def add_generate_token_subcommand(\\n subparsers: Any,\\n) -> None:\\n generate_token_sp = subparsers.add_parser(\\n \\\"generate-token\\\",\\n formatter_class=Formatter,\\n description=dedent( # noqa: WPS462 -- docs\\n \\\"\\\"\\\"\\\\\\n Generate token.\\n\\n Token is required to consume the protected endpoints.\\n\\n Example:\\n ```shell\\n # Generate a rsa key pair\\n openssl genpkey -algorithm RSA -out private_key.pem \\\\\\\\\\n -pkeyopt rsa_keygen_bits:2048\\n openssl rsa -pubout -in private_key.pem -out public_key.pem\\n # Generate token\\n bartender generate-token --format header > token.txt\\n # Use token\\n curl -X 'GET' \\\\\\\\\\n 'http://127.0.0.1:8000/api/whoami' \\\\\\\\\\n -H 'accept: application/json' \\\\\\\\\\n -H @token.txt | jq .\\n ```\\n \\\"\\\"\\\",\\n ),\\n help=\\\"Generate token.\\\",\\n )\\n generate_token_sp.add_argument(\\n \\\"--private-key\\\",\\n default=Path(\\\"private_key.pem\\\"),\\n type=Path,\\n help=\\\"Path to RSA private key file\\\",\\n )\\n generate_token_sp.add_argument(\\n \\\"--username\\\",\\n default=\\\"someone\\\",\\n help=\\\"Username to use in token\\\",\\n )\\n generate_token_sp.add_argument(\\n \\\"--roles\\\",\\n nargs=\\\"+\\\",\\n default=[\\\"expert\\\", \\\"guru\\\"],\\n help=\\\"Roles to use in token\\\",\\n )\\n onehour_in_minutes = 60\\n generate_token_sp.add_argument(\\n \\\"--lifetime\\\",\\n default=onehour_in_minutes,\\n type=int,\\n help=\\\"Lifetime of token in minutes\\\",\\n )\\n generate_token_sp.add_argument(\\n \\\"--issuer\\\",\\n default=\\\"bartendercli\\\",\\n help=\\\"Issuer of token\\\",\\n )\\n generate_token_sp.add_argument(\\n \\\"--oformat\\\",\\n default=\\\"plain\\\",\\n choices=[\\\"header\\\", \\\"plain\\\"],\\n help=\\\"Format of output\\\",\\n )\\n generate_token_sp.set_defaults(func=generate_token_subcommand)\",\n \"def setup_parser():\\r\\n parser = argparse.ArgumentParser(description='Freeseer Recording Utility',\\r\\n formatter_class=argparse.RawTextHelpFormatter)\\r\\n parser.add_argument(\\\"-v\\\", \\\"--version\\\", action='version',\\r\\n version=textwrap.dedent('''\\\\\\r\\n Freeseer {version} ({platform})\\r\\n Python {pymajor}.{pyminor}.{pymicro}\\r\\n PyGst {pygst_version}\\r\\n PyQt {pyqt_version}\\r\\n Qt {qt_version}\\r\\n Yapsy {yapsy_version}\\r\\n '''.format(version=__version__,\\r\\n platform=sys.platform,\\r\\n pymajor=sys.version_info.major,\\r\\n pyminor=sys.version_info.minor,\\r\\n pymicro=sys.version_info.micro,\\r\\n pygst_version=pygst._pygst_version,\\r\\n pyqt_version=QtCore.PYQT_VERSION_STR,\\r\\n qt_version=QtCore.QT_VERSION_STR,\\r\\n yapsy_version=yapsy.__version__)))\\r\\n\\r\\n # Configure Subparsers\\r\\n subparsers = parser.add_subparsers(dest='app', help='Command List')\\r\\n setup_parser_record(subparsers)\\r\\n setup_parser_config(subparsers)\\r\\n setup_parser_talk(subparsers)\\r\\n setup_parser_report(subparsers)\\r\\n setup_parser_upload(subparsers)\\r\\n return parser\",\n \"def setup_argparse():\\n prsr = argparse.ArgumentParser( description=__description__,\\n version=__version__ )\\n subprsrs = prsr.add_subparsers(dest='subparser_name')\\n \\n sub_export = subprsrs.add_parser(prsr_EXPORT,description=prsr_EXPORT_desc,\\n help=prsr_EXPORT_help)\\n sub_update = subprsrs.add_parser(prsr_UPDATE, description=prsr_UPDATE_desc,\\n help=prsr_UPDATE_help)\\n sub_script= subprsrs.add_parser(prsr_SCRIPT,description=prsr_SCRIPT_desc,\\n help=prsr_SCRIPT_help) \\n \\n # toplevel options \\n # --debug turn on debug logging.\\n # --warnings let warnings be logged too, errors are the only thing logged by default.\\n # --logfile set the path to the log file.\\n prsr.add_argument('--debug', action='store_true',help=\\\"Turn on debug logging.\\\",dest='log_debug')\\n prsr.add_argument('--warnings',action='store_true',help=\\\"Let warnings be logged too, errors are the only thing logged by default.\\\",dest='log_warnings')\\n prsr.add_argument('--logfile', default=\\\"errors.log\\\",help=\\\"Set the path to the log file.\\\",dest='log_path')\\n prsr.add_argument('--save', metavar=\\\"path\\\",help=\\\"Set the path to where the current query can be saved.\\\",dest='script_save_path')\\n prsr.add_argument('--time', action='store_true',help=\\\"LiVSs will report how long it took to run your command.\\\",dest='run_timer')\\n \\n #export subcommand\\n # input the file/directory that should be the target of an export\\n # output the file/directory that should be the location of said export\\n #\\n # Subcommands of Export\\n # util options for dumping utility files from the input target\\n # translator options for dumping a translator file from the input target\\n #\\n esubprsrs = sub_export.add_subparsers(dest='subsubparser_name')\\n util_sub_parse = esubprsrs.add_parser(subprsr_UTIL, description=subprsr_UTIL_desc, \\n help=subprsr_UTIL_help)\\n translator_sub_parse = esubprsrs.add_parser(subprsr_TRANSLATOR,description=subprsr_TRANSLATOR_desc,\\n help=subprsr_TRANSLATOR_help)\\n sub_export.add_argument('-l','--level', choices=['sys','proj','lang'], help=\\\"set the level to build the export to, read the HOWTO if you don't know.\\\",dest='export_level')\\n sub_export.add_argument('--mem', action='store_true', help=\\\"Keep everything in memory and only saves the level specified, generates new files.\\\",dest='export_mem')\\n sub_export.add_argument('--keep', action='store_true', help=\\\"Use what was currently generated in the input path, don't generate new files.\\\",dest='export_existing')\\n sub_export.add_argument('input', help='the file/directory that should be the target of an export')\\n sub_export.add_argument('output', help='the file/directory that should be the location of said export')\\n \\n # export util subcommand\\n # -m, --menus export the menus out of the target\\n # -d, --dialogs export the dialogs out of a target\\n # -s, --strings export the string tables out of a target\\n # -a, --all export all utils from a target (output must be a directory)\\n util_sub_parse.add_argument('-m','--menus', action='store_true', help='export the menus out of the target',dest='export_util_menus')\\n util_sub_parse.add_argument('-d','--dialogs', action='store_true', help='export the dialogs out of a target',dest='export_util_dialogs')\\n util_sub_parse.add_argument('-s','--strings', action='store_true', help='export the string tables out of a target',dest='export_util_strings')\\n util_sub_parse.add_argument('-a','--all', action='store_true', help='export all utils from a target (output must be a directory)',dest='export_util_all')\\n \\n # export translator subcommand\\n # -s, --sort Sort the list of strings alphabetically\\n # -c, --langcodes Provide a list of language codes you want in the translator file\\n # -p, --prune Takes a path to a file that has a list of strings that shouldn't be in the translation file\\n translator_sub_parse.add_argument('-s','--sort', action='store_true', help=\\\"Sort the list of strings alphabetically\\\", dest='export_translator_order')\\n translator_sub_parse.add_argument('-c','--langcodes', metavar='code', nargs='+', help=\\\"Provide a list of language codes you want in the translator file\\\",dest='export_translator_langcodes')\\n translator_sub_parse.add_argument('-p','--prune', metavar='path', nargs=1, help=\\\"Takes a path to a file that has a list of strings that shouldn't be in the translation file\\\",dest='export_translator_prunepath')\\n translator_sub_parse.add_argument('-m','--mark', action='store_true', help=\\\"Highlights possible conflicts in the new translation file\\\", dest='export_translator_markconflicts')\\n\\n #update subcommand\\n # -t, --translator Location of input translator file\\n # -d, --dialogs Location of input dialogs file\\n # -m, --menus Location of input menus file\\n # -s, --strings Location of input string table file\\n # -c, --langcode Limit what lang code in the input file gets updated, this can be a list.\\n # -o, --outtype What should we be updating? (sys, proj, rcs)\\n # -n, --new Generates new files for whatever outtype is set to (cannot be rcs).\\n # output The directory/file to update\\n locations = sub_update.add_mutually_exclusive_group(required=True)\\n locations.add_argument('-t','--translator',metavar='path',help='Location of input translator file',dest='update_translator')\\n locations.add_argument('-d','--dialogs',metavar='path',help='Location of input dialogs file',dest='update_dialogs')\\n locations.add_argument('-m','--menus',metavar='path',help='Location of input menus file',dest='update_menus')\\n locations.add_argument('-s','--strings',metavar='path',help='Location of input string table file',dest='update_strings')\\n sub_update.add_argument('-c','--langcode', metavar='code', nargs='+', help=\\\"Limit what lang code in the input file gets updated, this can be a list.\\\", dest='update_langcodes')\\n sub_update.add_argument('-o','--outtype', metavar='type', choices=['sys','proj','rcs'], help='What should we be updating?', dest='update_to')\\n sub_update.add_argument('-n','--new',action='store_true', help='Generates new files for whatever outtype is set to (cannot be rcs).', dest='update_new')\\n sub_update.add_argument('output',help='The directory/file to update')\\n \\n #script subcommand\\n # --dump instead of reading in a file, all default configurations are dumped to the given file\\n # path takes a path to a file to run it. For more information on scripting, see the howto file.\\n sub_script.add_argument(\\\"--dump\\\", action=\\\"store_true\\\", help=\\\"instead of reading in a file, all default configurations are dumped to the given file\\\")\\n sub_script.add_argument(\\\"input\\\", help=\\\"takes a path to a headshot script to run it.\\\")\\n \\n return prsr\",\n \"def __init__(self, subparsers, region_arg: bool = True, expects_extra_args: bool = False, **argparse_kwargs):\\n parser_name = argparse_kwargs.pop(\\\"name\\\")\\n parser = subparsers.add_parser(parser_name, **argparse_kwargs)\\n parser.add_argument(\\\"--debug\\\", action=\\\"store_true\\\", help=\\\"Turn on debug logging.\\\", default=False)\\n if region_arg:\\n parser.add_argument(\\\"-r\\\", \\\"--region\\\", help=\\\"AWS Region this operation corresponds to.\\\")\\n self.register_command_args(parser)\\n parser.set_defaults(func=self.execute, expects_extra_args=expects_extra_args)\",\n \"def make_cli_parser(self):\\n super(BplnArgParser, self).make_cli_parser()\\n links_opt = self.cli_parser.add_option('--selected-links',\\n help=(\\\"A CSV-formatted file containing pairs of \\\"\\n \\\"terms to test. Tests will be done to decide \\\"\\n \\\"if the annotation term from the first column \\\"\\n \\\"\\\\\\\"is linked to\\\\\\\" the annotation term from the \\\"\\n \\\"second column. [NOTE: Selecting this option \\\"\\n \\\"restricts the program to only test the matches \\\"\\n \\\"designated in the file.] [NOTE: This option \\\"\\n \\\"conflicts with '--selected-terms' and \\\"\\n \\\"'--selected-terms-with-all'.]\\\"\\n )\\n )\\n anns_opt = self.cli_parser.add_option('--selected-terms',\\n help=(\\\"A file containing annotation terms to test \\\"\\n \\\"linkage to each other. The file should contain one \\\"\\n \\\"term per line. Selecting this option restricts the \\\"\\n \\\"program to only testing the given terms against \\\"\\n \\\"each other. [NOTE: This option conflicts with \\\"\\n \\\"'--selected-links' and \\\"\\n \\\"'--selected-terms-with-all'.]\\\"\\n )\\n )\\n anns_all_opt = self.cli_parser.add_option(\\n '--selected-terms-with-all',\\n help=(\\\"A file containing annotation terms to test \\\"\\n \\\"linkage to all other terms (one-against-all and \\\"\\n \\\"all-against-one). The file should contain one \\\"\\n \\\"term per line. Selecting this option restricts \\\"\\n \\\"the program to only testing the given terms \\\"\\n \\\"against all other terms. [NOTE: \\\"\\n \\\"This option conflicts with '--selected-links' and \\\"\\n \\\"'--selected-terms'.]\\\"\\n )\\n )\\n self.cli_parser.register_conflict(\\n (links_opt, anns_opt, anns_all_opt))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8356323","0.7613267","0.7532559","0.75132465","0.74843043","0.7461825","0.7458073","0.7289405","0.71724516","0.7165431","0.7135265","0.7031047","0.6956534","0.6943993","0.69056773","0.688262","0.68790203","0.6860334","0.6815078","0.67114484","0.67108387","0.6692973","0.6692194","0.66842544","0.66556716","0.664244","0.6632538","0.65947324","0.65842444","0.6562371","0.6539486","0.6503882","0.6503623","0.64993274","0.64651495","0.6452243","0.64471865","0.6407968","0.6396124","0.6394142","0.63911366","0.6344063","0.63273406","0.632696","0.6318256","0.6306399","0.6293927","0.6256494","0.6256343","0.62277085","0.6218185","0.6210251","0.618248","0.61797833","0.6179422","0.61605614","0.6159979","0.6145137","0.61332655","0.6131321","0.61182","0.61054975","0.61049104","0.6095218","0.6093803","0.6089879","0.60774523","0.6077219","0.6074431","0.60508054","0.6045958","0.60451156","0.6044384","0.6042533","0.6039149","0.60386866","0.6023419","0.60221237","0.6020217","0.6008185","0.60007936","0.59925944","0.59884405","0.59877557","0.5985673","0.59796655","0.59777594","0.5974108","0.59698445","0.5966211","0.59546626","0.5953078","0.5947242","0.5946412","0.59445643","0.59414375","0.59327054","0.5929507","0.5917773","0.5912016"],"string":"[\n \"0.8356323\",\n \"0.7613267\",\n \"0.7532559\",\n \"0.75132465\",\n \"0.74843043\",\n \"0.7461825\",\n \"0.7458073\",\n \"0.7289405\",\n \"0.71724516\",\n \"0.7165431\",\n \"0.7135265\",\n \"0.7031047\",\n \"0.6956534\",\n \"0.6943993\",\n \"0.69056773\",\n \"0.688262\",\n \"0.68790203\",\n \"0.6860334\",\n \"0.6815078\",\n \"0.67114484\",\n \"0.67108387\",\n \"0.6692973\",\n \"0.6692194\",\n \"0.66842544\",\n \"0.66556716\",\n \"0.664244\",\n \"0.6632538\",\n \"0.65947324\",\n \"0.65842444\",\n \"0.6562371\",\n \"0.6539486\",\n \"0.6503882\",\n \"0.6503623\",\n \"0.64993274\",\n \"0.64651495\",\n \"0.6452243\",\n \"0.64471865\",\n \"0.6407968\",\n \"0.6396124\",\n \"0.6394142\",\n \"0.63911366\",\n \"0.6344063\",\n \"0.63273406\",\n \"0.632696\",\n \"0.6318256\",\n \"0.6306399\",\n \"0.6293927\",\n \"0.6256494\",\n \"0.6256343\",\n \"0.62277085\",\n \"0.6218185\",\n \"0.6210251\",\n \"0.618248\",\n \"0.61797833\",\n \"0.6179422\",\n \"0.61605614\",\n \"0.6159979\",\n \"0.6145137\",\n \"0.61332655\",\n \"0.6131321\",\n \"0.61182\",\n \"0.61054975\",\n \"0.61049104\",\n \"0.6095218\",\n \"0.6093803\",\n \"0.6089879\",\n \"0.60774523\",\n \"0.6077219\",\n \"0.6074431\",\n \"0.60508054\",\n \"0.6045958\",\n \"0.60451156\",\n \"0.6044384\",\n \"0.6042533\",\n \"0.6039149\",\n \"0.60386866\",\n \"0.6023419\",\n \"0.60221237\",\n \"0.6020217\",\n \"0.6008185\",\n \"0.60007936\",\n \"0.59925944\",\n \"0.59884405\",\n \"0.59877557\",\n \"0.5985673\",\n \"0.59796655\",\n \"0.59777594\",\n \"0.5974108\",\n \"0.59698445\",\n \"0.5966211\",\n \"0.59546626\",\n \"0.5953078\",\n \"0.5947242\",\n \"0.5946412\",\n \"0.59445643\",\n \"0.59414375\",\n \"0.59327054\",\n \"0.5929507\",\n \"0.5917773\",\n \"0.5912016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5897082"},"document_rank":{"kind":"string","value":"100"}}},{"rowIdx":94594,"cells":{"query":{"kind":"string","value":"Check that all protected branches are a single index unicity of name version must be increased `force` option ignored"},"document":{"kind":"string","value":"def check_pkg_consistency():\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def commit_check(ctx):\n result = ctx.run(f\"{VENV_PREFIX} cz check --rev-range master..\", warn=True)\n if result.exited == 3: # NO_COMMIT_FOUND\n exit(0)\n else:\n exit(result.exited)","def validate(cfg: defs.Config) -> List[str]: # noqa: C901\n res: List[str] = []\n\n def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n \"\"\"Validate versions within a single branch.\"\"\"\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")\n\n def check_component(comp_name: str, comp: defs.Component) -> None:\n \"\"\"Validate the definition of a single component.\"\"\"\n if not RE_COMP_NAME.match(comp_name):\n res.append(f\"Invalid component name: {comp_name}\")\n\n for branch_name, branch in sorted(comp.branches.items()):\n check_branch(comp_name, branch_name, branch)\n\n for comp_name, comp in sorted(cfg.all_components.components.items()):\n check_component(comp_name, comp)\n\n return res","def needs_patch():\n return (IndexName is not None and\n hasattr(BaseDatabaseSchemaEditor, '_create_unique_sql'))","def check_fast_forward():\n\n if oldrev != zero: # ignore new branches\n cmd = ['git', 'rev-list', newrev + '..' + oldrev]\n missed_refs = call(cmd)\n missed_ref_count = len(missed_refs)\n if missed_ref_count > 0:\n add_error(\n \"[POLICY] Non fast-forward updates are not allowed for this branch\")","def version_update_has_wrong_order(versions, new_versions):\n branches = find_branches(versions)\n if not branches:\n return False\n\n tip = branches[-1]\n\n def find_branch_for_version(v):\n for branch in branches:\n if on_same_branch(branch, v):\n return branch\n return tip\n\n chunks = groupby(new_versions, find_branch_for_version)\n chunks = [[key] + list(values)\n for key, values\n in chunks]\n\n some_chunk_has_hole = any(map(has_hole, chunks))\n return some_chunk_has_hole","def checkConflicts(self):\n\t\treturn","def ensure_tracking_branches(args):\n man = load_manifest()\n for (name, project) in man.projects.iteritems():\n repo = GitRepo(workdir_for_project(project))\n branch_missing = repo.command(\n [\"rev-parse\", \"--verify\", \"-q\", project.refspec],\n capture_stdout=True)\n \n if branch_missing:\n logging.warn(\"Branch %s does not exist in project %s. checking out.\" %\n (project.refspec, name))\n repo.command([\"branch\", \"--track\",\n project.tracking_branch, project.remote_refspec])","def _get_index_if_valid(self, course_key, force=False):\n if course_key.org is None or course_key.course is None or course_key.run is None or course_key.branch is None:\n return None\n else:\n index_entry = self.get_course_index(course_key)\n is_head = (\n course_key.version_guid is None or\n index_entry['versions'][course_key.branch] == course_key.version_guid\n )\n if is_head or force:\n return index_entry\n else:\n raise VersionConflictError(\n course_key,\n index_entry['versions'][course_key.branch]\n )","def ensure_index(cls, *a, **ka):\n cls._dbag.ensure_index(*a, **ka)","def resolve_conflicts(self, commit=True):\n pass # pragma: no cover","def enforce_clean_option(args, run):\n repos = run.experiment_info[\"repositories\"]\n if not repos:\n raise RuntimeError(\n \"No version control detected. \"\n \"Cannot enforce clean repository.\\n\"\n \"Make sure that your sources under VCS and the \"\n \"corresponding python package is installed.\"\n )\n else:\n for repo in repos:\n if repo[\"dirty\"]:\n raise RuntimeError(\n \"EnforceClean: Uncommited changes in \"\n 'the \"{}\" repository.'.format(repo)\n )","def __gitCheckPatches(self):\n self.vcs.gitApplyCheckPatches(self.project.getProjectPath(),\n check=True)","def check_branch(\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\n ) -> None:\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\n\n if not RE_BRANCH_NAME.match(branch_name):\n res.append(f\"{comp_name}: Invalid branch name: {branch_name}\")\n\n for ver, version in sorted(branch.items()):\n if not RE_VERSION_STRING.match(ver):\n res.append(f\"{comp_name}/{branch_name}: Invalid version string: {ver}\")\n\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\n if version.outdated:\n update_to = [\n o_version\n for o_version in branch.values()\n if not o_version.outdated\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\n == other_cksums\n ]\n if len(update_to) != 1:\n res.append(\n f\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \"\n f\"versions to update to instead of exactly one\"\n )\n else:\n bad_files = sorted(\n relpath\n for relpath, (path, fdata) in driver_cksums.items()\n if util.file_sha256sum(path) != fdata.sha256\n )\n if bad_files:\n res.append(f\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\")\n\n if not uptodate_files:\n uptodate_files = driver_cksums\n elif uptodate_files != driver_cksums:\n res.append(\n f\"{comp_name}/{branch_name}: All the up-to-date versions should \"\n f\"define the same set of files with the same checksums\"\n )\n\n if not any(not version.outdated for version in branch.values()):\n res.append(f\"{comp_name}/{branch_name}: No non-outdated versions\")","def check_heads(repo, their_heads, context):\n heads = repo.heads()\n heads_hash = hashutil.sha1(b''.join(sorted(heads))).digest()\n if not (\n their_heads == [b'force']\n or their_heads == heads\n or their_heads == [b'hashed', heads_hash]\n ):\n # someone else committed/pushed/unbundled while we\n # were transferring data\n raise error.PushRaced(\n b'repository changed while %s - please try again' % context\n )","def _does_require_force_update(self):\n\n if self.current_version[0][0] > self.version_yaml[0]:\n # The current version first index is greater than the one we have in the\n # current version.yaml.\n\n # We return True.\n return True\n\n # We return False, we do not need to force the update for\n # the current version number.\n return False","def __gitVerify(self):\n self.vcs.gitVerify(self.project.getProjectPath())","def test_resetcherrypick_resets_index(repository: Repository, path: Path) -> None:\n createconflict(repository, path, ours=\"a\", theirs=\"b\")\n\n repository.resetcherrypick()\n\n index = repository._repository.index\n assert index.write_tree() == repository.head.commit.tree.id","def check_fast_forward(self, branch):\n proc = run_cmd(self.git + ['rev-list', '%s-tmp..%s' %\n (branch, branch), '--'])\n if proc.stdout.strip():\n # Commits have been made on the main branch since the last update\n # command.\n raise EmtError('cannot fast-forward the %s branch, please '\n 'run again the update command' % branch)","def index_is_dirty():\n result, output = popen('git diff --cached', False, False)\n return len(output) > 0","def index_ddl_if_exists(self):\n return exclusions.closed()","def _prepare_manual_resolve(self):\n # Files that have been deleted between branch and cherry-pick will not have\n # their skip-worktree bit set so set it manually for those files to avoid\n # git status incorrectly listing them as unstaged deletes.\n repo_status = self._run_git_command(\n ['-c', 'core.quotePath=false', 'status', '--porcelain']).splitlines()\n extra_files = [f[3:] for f in repo_status if f[:2] == ' D']\n if extra_files:\n self._run_git_command_with_stdin(\n ['update-index', '--skip-worktree', '--stdin'],\n stdin='\\n'.join(extra_files) + '\\n')","def validate_shared_index_options(options):\n \n if options.vcf_phasing:\n require(all([vcf.endswith('.vcf.gz') for vcf in options.vcf_phasing]),\n 'input phasing files must end with .vcf.gz')\n if 'gbwt' in options.indexes:\n require(len(options.vcf_phasing) > 0, 'generating a GBWT requires a VCF with phasing information')\n if options.gbwt_prune:\n require(('gbwt' in options.indexes) or options.gbwt_input, '--gbwt_index or --gbwt_input required for --gbwt_prune')\n if options.vcf_phasing_regions:\n require('gbwt' in options.indexes, \"cannot hint regions to GBWT indexer without building a GBWT index\")","def replace_old(self):\n # Tell the client the index is ready to be used and to no longer show it as building.\n self.db.indexes.find_one_and_update({\"_id\": self.params[\"index_id\"]}, {\n \"$set\": {\n \"ready\": True\n }\n })\n\n self.dispatch(\"indexes\", \"update\", [self.params[\"index_id\"]])\n\n active_indexes = virtool.db.sync.get_active_index_ids(self.db, self.params[\"ref_id\"])\n\n remove_unused_index_files(self.params[\"reference_path\"], active_indexes)\n\n query = {\n \"_id\": {\n \"$not\": {\n \"$in\": active_indexes\n }\n }\n }\n\n self.db.indexes.update_many(query, {\n \"$set\": {\n \"has_files\": False\n }\n })\n\n id_list = self.db.indexes.distinct(\"_id\", query)\n\n self.dispatch(\"indexes\", \"update\", id_list)\n\n # Find OTUs with changes.\n pipeline = [\n {\"$project\": {\n \"reference\": True,\n \"version\": True,\n \"last_indexed_version\": True,\n \"comp\": {\"$cmp\": [\"$version\", \"$last_indexed_version\"]}\n }},\n {\"$match\": {\n \"reference.id\": self.params[\"ref_id\"],\n \"comp\": {\"$ne\": 0}\n }},\n {\"$group\": {\n \"_id\": \"$version\",\n \"id_list\": {\n \"$addToSet\": \"$_id\"\n }\n }}\n ]\n\n id_version_key = {agg[\"_id\"]: agg[\"id_list\"] for agg in self.db.otus.aggregate(pipeline)}\n\n # For each version number\n for version, id_list in id_version_key.items():\n self.db.otus.update_many({\"_id\": {\"$in\": id_list}}, {\n \"$set\": {\n \"last_indexed_version\": version\n }\n })","def _rebuild_index(self):\n from django.core.management import call_command\n call_command('rebuild_index', interactive=False, verbosity=0)","def test_multiple_packages_same_version(self):\n with patch.object(self.request.access, \"allow_overwrite\", []):\n name, version = \"a\", \"1\"\n path1 = \"old_package_path-1.tar.gz\"\n self.db.upload(path1, BytesIO(b\"test1234\"), name, version)\n path2 = \"new_path-1.whl\"\n self.db.upload(path2, BytesIO(b\"test1234\"), name, version)\n\n all_versions = self.db.all(name)\n self.assertEqual(len(all_versions), 2)","def test_multiple_packages_same_version(self):\n with patch.object(self.request.access, \"allow_overwrite\", []):\n name, version = \"a\", \"1\"\n path1 = \"old_package_path-1.tar.gz\"\n self.db.upload(path1, BytesIO(b\"test1234\"), name, version)\n path2 = \"new_path-1.whl\"\n self.db.upload(path2, BytesIO(b\"test1234\"), name, version)\n\n all_versions = self.db.all(name)\n self.assertEqual(len(all_versions), 2)","def test_multiple_packages_same_version(self):\n with patch.object(self.request.access, \"allow_overwrite\", []):\n name, version = \"a\", \"1\"\n path1 = \"old_package_path-1.tar.gz\"\n self.db.upload(path1, BytesIO(b\"test1234\"), name, version)\n path2 = \"new_path-1.whl\"\n self.db.upload(path2, BytesIO(b\"test1234\"), name, version)\n\n all_versions = self.db.all(name)\n self.assertEqual(len(all_versions), 2)","def get_all_changes_for_package(\n provider_package_id: str,\n verbose: bool,\n base_branch: str,\n force: bool,\n) -> tuple[bool, list[list[Change]] | Change | None, str]:\n provider_details = get_provider_details(provider_package_id)\n current_version = provider_details.versions[0]\n current_tag_no_suffix = get_version_tag(current_version, provider_package_id)\n if verbose:\n console.print(f\"Checking if tag '{current_tag_no_suffix}' exist.\")\n if not force and not subprocess.call(\n get_git_tag_check_command(current_tag_no_suffix),\n cwd=provider_details.source_provider_package_path,\n stderr=subprocess.DEVNULL,\n ):\n if verbose:\n console.print(f\"The tag {current_tag_no_suffix} exists.\")\n # The tag already exists\n changes = subprocess.check_output(\n get_git_log_command(verbose, f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\", current_tag_no_suffix),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n if changes:\n provider_details = get_provider_details(provider_package_id)\n doc_only_change_file = os.path.join(\n provider_details.source_provider_package_path, \".latest-doc-only-change.txt\"\n )\n if os.path.exists(doc_only_change_file):\n with open(doc_only_change_file) as f:\n last_doc_only_hash = f.read().strip()\n try:\n changes_since_last_doc_only_check = subprocess.check_output(\n get_git_log_command(\n verbose, f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\", last_doc_only_hash\n ),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n if not changes_since_last_doc_only_check:\n console.print()\n console.print(\n \"[yellow]The provider has doc-only changes since the last release. Skipping[/]\"\n )\n # Returns 66 in case of doc-only changes\n sys.exit(66)\n if len(changes) > len(changes_since_last_doc_only_check):\n # if doc-only was released after previous release - use it as starting point\n # but if before - stay with the releases from last tag.\n changes = changes_since_last_doc_only_check\n except subprocess.CalledProcessError:\n # ignore when the commit mentioned as last doc-only change is obsolete\n pass\n\n console.print(f\"[yellow]The provider {provider_package_id} has changes since last release[/]\")\n console.print()\n console.print(f\"[bright_blue]Provider: {provider_package_id}[/]\\n\")\n changes_table, array_of_changes = convert_git_changes_to_table(\n f\"NEXT VERSION AFTER + {provider_details.versions[0]}\",\n changes,\n base_url=\"https://github.com/apache/airflow/commit/\",\n markdown=False,\n )\n print_changes_table(changes_table)\n return False, array_of_changes[0], changes_table\n else:\n console.print(f\"No changes for {provider_package_id}\")\n return False, None, \"\"\n if verbose:\n console.print(\"The tag does not exist. \")\n if len(provider_details.versions) == 1:\n console.print(\n f\"The provider '{provider_package_id}' has never been released but it is ready to release!\\n\"\n )\n else:\n console.print(f\"New version of the '{provider_package_id}' package is ready to be released!\\n\")\n next_version_tag = f\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\"\n changes_table = \"\"\n current_version = provider_details.versions[0]\n list_of_list_of_changes: list[list[Change]] = []\n for version in provider_details.versions[1:]:\n version_tag = get_version_tag(version, provider_package_id)\n changes = subprocess.check_output(\n get_git_log_command(verbose, next_version_tag, version_tag),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\n current_version, changes, base_url=\"https://github.com/apache/airflow/commit/\", markdown=False\n )\n changes_table += changes_table_for_version\n list_of_list_of_changes.append(array_of_changes_for_version)\n next_version_tag = version_tag\n current_version = version\n changes = subprocess.check_output(\n get_git_log_command(verbose, next_version_tag),\n cwd=provider_details.source_provider_package_path,\n text=True,\n )\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\n current_version, changes, base_url=\"https://github.com/apache/airflow/commit/\", markdown=False\n )\n changes_table += changes_table_for_version\n if verbose:\n print_changes_table(changes_table)\n return True, list_of_list_of_changes or None, changes_table","def _check_integrity(self):\n try:\n for q in self:\n pass\n except TreeIntegrityError:\n raise ValueError('Invalid \"tree\" parameter.')","def check_consistency(self, es):","def verify_branch(root, index, proof, output_as_int=False):\n index = get_index_in_permuted(index, 2**len(proof) // 2)\n # I think this is a bug. Should be 2**len(proof) // 2\n # But I think it's OK since we're only doing parity checks\n #index += 2**len(proof)\n index += 2**len(proof) // 2\n v = proof[0]\n for p in proof[1:]:\n if index % 2:\n v = blake(p + v)\n else:\n v = blake(v + p)\n index //= 2\n assert v == root\n return int.from_bytes(proof[0], 'big') if output_as_int else proof[0]","def flag_all_commit(self):\n\t\tfor k in self.data.keys():\n\t\t\tindex = 0\n\t\t\tfor item in self[k]:\n\t\t\t\tself.data[k][index]['meta']['needs_commit'] = True\n\t\t\t\tindex += 1","def checkConflicts(self):\n\t\tapDisplay.printError(\"you did not create a 'checkConflicts' function in your script\")\n\t\traise NotImplementedError()","def svn_fs_check_related(*args):\r\n return _fs.svn_fs_check_related(*args)","def test_heads_create_existing_branch_force(repository: Repository) -> None:\n head, heads = repository.head, repository.heads\n branch = heads.create(\"branch\", head.commit)\n updatefile(repository.path / \"a\")\n heads.create(branch.name, head.commit, force=True)\n assert head.commit == branch.commit","def _get_index_if_valid(self, locator, force=False, continue_version=False):\r\n if locator.org is None or locator.offering is None or locator.branch is None:\r\n if continue_version:\r\n raise InsufficientSpecificationError(\r\n \"To continue a version, the locator must point to one ({}).\".format(locator)\r\n )\r\n else:\r\n return None\r\n else:\r\n index_entry = self.db_connection.get_course_index(locator)\r\n is_head = (\r\n locator.version_guid is None or\r\n index_entry['versions'][locator.branch] == locator.version_guid\r\n )\r\n if (is_head or (force and not continue_version)):\r\n return index_entry\r\n else:\r\n raise VersionConflictError(\r\n locator,\r\n index_entry['versions'][locator.branch]\r\n )","def commit(self, force=False):\n if len(self.changed) > 0 or force: \n self.things_obj.set_all(self.things_dict, self.parent)\n logging.error('commited!!!!!!!!')","def __checkFeatureIndex__(self, index, indexes):\n if index is not False:\n indexes.append(index)","def unprotect(args):\n\n for branch in args.branch:\n logging.info(\"Unprotecting %s...\", branch)\n #pylint: disable=no-value-for-parameter, cell-var-from-loop\n manage_repos(args, lambda repo, _: repo.unprotect(branch))","def test_heads_contains_true(repository: Repository) -> None:\n assert repository.head.name in repository.heads","def rebuild_index(self):\n with warnings.catch_warnings():\n warnings.simplefilter('ignore')\n with get_db_connection() as db:\n c = db.cursor()\n execute_with_retry(db, c, self._rebuild_index)","def verify_index(yaml_data, build_index=True):\n print('='*10, 'Verifying Index', '='*10)\n index_utils_command = [\n os.path.join(yaml_data['root'], yaml_data['index_utils_command']),\n '-index', os.path.join(yaml_data['index_root'] if yaml_data['index_root'] else '',\n 'lucene-index.{0}.pos+docvectors{1}'.format(yaml_data['name'], '+rawdocs' if 'storeRawdocs' in yaml_data['index_options'] else ''))\n if build_index else yaml_data['index_path'],\n '-stats'\n ]\n out = check_output(' '.join(index_utils_command)).decode('utf-8').split('\\n')\n for line in out:\n stat = line.split(':')[0]\n if stat in yaml_data['index_stats']:\n value = int(line.split(':')[1])\n assert value == yaml_data['index_stats'][stat]\n print(line)\n print(OKBLUE, '='*10, 'Verifying Index Succeed', '='*10, ENDC)","def test_major(scraper, version_parts):\n\n new_version_parts = list(version_parts)\n new_version_parts[0] = int(new_version_parts[0]) + 1\n\n assert scraper.is_compatible_with(generate_version(new_version_parts)) is False","def test_verify_recursive_and_transverse_acl_options():","def test_force_and_update(self, _get_repo_name, # pylint: disable=unused-argument\n set_version_from_git_tag_mock):\n force_semver_git_tag_plugin(self.project, self.logger)\n self.project.set_property('semver_git_tag_increment_part', 'minor')\n update_version_from_git_tag(self.project, self.logger)\n self.assertEqual(set_version_from_git_tag_mock.call_count, 2)\n self.assertEqual(self.logger.info.call_count, 2)\n self.logger.warn.assert_called_once_with(\n \"Property `semver_git_tag_increment_part` was changed. \"\n \"For better compatibility recommended to use \"\n \"command line `pyb ... -P semver_git_tag_increment_part=...`, \"\n \"otherwise some version-related properties could \"\n \"be spoiled.\"\n )","def commit():\n return _run_indexer_func(\"commit\")","def createVersions(self, force = False):\n\t\tlow = self.makeLow(force)\n\t\tlow.makeTx(force)\n\t\tmid = self.makeMid(force)\n\t\tmid.makeTx(force)\n\t\tself.makeTx(force)","def test_force(self, db, course_dir):\n run_nbgrader([\"db\", \"assignment\", \"add\", \"ps1\", \"--db\", db])\n run_nbgrader([\"db\", \"student\", \"add\", \"foo\", \"--db\", db])\n\n self._copy_file(join(\"files\", \"submitted-unchanged.ipynb\"), join(course_dir, \"source\", \"ps1\", \"p1.ipynb\"))\n self._make_file(join(course_dir, \"source\", \"ps1\", \"foo.txt\"), \"foo\")\n self._make_file(join(course_dir, \"source\", \"ps1\", \"data\", \"bar.txt\"), \"bar\")\n run_nbgrader([\"generate_assignment\", \"ps1\", \"--db\", db])\n\n self._copy_file(join(\"files\", \"submitted-unchanged.ipynb\"), join(course_dir, \"submitted\", \"foo\", \"ps1\", \"p1.ipynb\"))\n self._make_file(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"foo.txt\"), \"foo\")\n self._make_file(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"data\", \"bar.txt\"), \"bar\")\n run_nbgrader([\"autograde\", \"ps1\", \"--db\", db])\n\n self._make_file(join(course_dir, \"autograded\", \"foo\", \"ps1\", \"blah.pyc\"), \"asdf\")\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db])\n\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"p1.html\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"data\", \"bar.txt\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"blah.pyc\"))\n\n # check that it skips the existing directory\n remove(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db])\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n\n # force overwrite the supplemental files\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db, \"--force\"])\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n\n # force overwrite\n remove(join(course_dir, \"autograded\", \"foo\", \"ps1\", \"foo.txt\"))\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db, \"--force\"])\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"p1.html\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"data\", \"bar.txt\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"blah.pyc\"))","def test_version_control_invalid(self):\n versions = ('Thirteen', '-1', -1, '' , 13)\n # A fresh repository doesn't go up to version 13 yet\n for version in versions:\n #self.assertRaises(ControlledSchema.InvalidVersionError,\n # Can't have custom errors with assertRaises...\n try:\n ControlledSchema.create(self.engine, self.repos, version)\n self.assertTrue(False, repr(version))\n except exceptions.InvalidVersionError:\n pass","def test_incorrect_case_file_index(data: TestData) -> None:\n req = install_req_from_line(\"dinner\")\n finder = make_test_finder(index_urls=[data.find_links3])\n found = finder.find_requirement(req, False)\n assert found is not None\n assert found.link.url.endswith(\"Dinner-2.0.tar.gz\")","def test_sort_git_master_and_latest(self):\n identifiers = [\"latest\", \"master\", \"1.0\", \"2.0\", \"1.1\", \"1.9\", \"1.10\"]\n self.project.repo_type = REPO_TYPE_GIT\n self.project.save()\n self.project.versions.get(slug=LATEST).delete()\n\n for identifier in identifiers:\n get(\n Version,\n project=self.project,\n type=BRANCH,\n identifier=identifier,\n verbose_name=identifier,\n slug=identifier,\n )\n\n versions = list(Version.objects.filter(project=self.project))\n self.assertEqual(\n [\"master\", \"latest\", \"2.0\", \"1.10\", \"1.9\", \"1.1\", \"1.0\"],\n [v.slug for v in sort_version_aware(versions)],\n )","def try_cherry_pick_all(rev):\n num_par = len(parents(rev))\n any_success = False\n for i in range(1, num_par + 1):\n any_success = any_success or try_cherry_pick(rev, mainline=i)\n return any_success","def ensure_indexes(self):\n self.db_connection.ensure_indexes()","def __gitVerifyBundle(self):\n self.vcs.gitVerifyBundle(self.project.getProjectPath())","def test_unique_together(self):\n\n for mb_model in self.mb_model_list:\n indexes = connection.introspection.get_indexes(\n self.cursor, mb_model._meta.db_table)\n if not indexes and not is_db_view(mb_model._meta.db_table):\n self.assertTrue(mb_model._meta.unique_together)","def isort_check(ctx):\n ctx.run(f\"{VENV_PREFIX} isort --atomic --check-only .\")","def ensure_sync_master_branch(self):\n # TODO(robertocn): Investigate what causes the states mentioned in the\n # docstring in the first place.\n self.api.m.git('update-ref', 'refs/heads/master',\n 'refs/remotes/origin/master')\n self.api.m.git('checkout', 'master', cwd=self.api.m.path['checkout'])","def dirty_branches(self):\n # If no course index has been set, then no branches have changed\n if self.index is None:\n return []\n\n # If there was no index in the database to start with, then all branches\n # are dirty by definition\n if self.initial_index is None:\n return list(self.index.get('versions', {}).keys())\n\n # Return branches whose ids differ between self.index and self.initial_index\n return [\n branch\n for branch, _id\n in self.index.get('versions', {}).items()\n if self.initial_index.get('versions', {}).get(branch) != _id\n ]","def protect_branch_with_approvals(branch_name: str, count: int = 1,\n require_code_owner_review: bool = False) -> repoproc_t:\n def _protect_branch_with_approvals(org: Organization, repo: Repository,\n branches: Dict[str, Branch]) -> List[Change[str]]:\n if branch_name in branches:\n return _protect_branch(branches[branch_name], count, require_code_owner_review)\n else:\n print_warning(\"Requested to protect branch %s on repo %s, but the branch does not exist.\" %\n (highlight(branch_name), highlight(repo.name)))\n return []\n return _protect_branch_with_approvals","def precommit(exit=True):\n tmpdir = tempfile.mkdtemp()\n\n try:\n copy_index(tmpdir)\n\n modified = check_output(['git', 'diff', '--cached', '--name-only',\n '--diff-filter=ACMRT'])\n modified = [name.strip() for name in modified.splitlines()]\n path = os.environ['PATH']\n with pushd(tmpdir) as prevdir:\n conf = load_conf()\n # Activate the virtualenv before running checks\n if 'env' in conf:\n binpath = os.path.abspath(os.path.join(prevdir,\n conf['env']['path'],\n 'bin'))\n if binpath not in path.split(os.pathsep):\n path = binpath + os.pathsep + path\n retcode = run_checks(conf.get('hooks_all', []),\n conf.get('hooks_modified', []), modified,\n path)\n\n if exit:\n sys.exit(retcode)\n else:\n return retcode\n finally:\n shutil.rmtree(tmpdir)","def validate_index_options(options):\n if len(options.indexes) > 0:\n require(len(options.graphs) == 0 or options.chroms, '--chroms must be specified for --graphs')\n require(len(options.graphs) == 1 or len(options.chroms) == len(options.graphs),\n '--chroms and --graphs must have'\n ' same number of arguments if more than one graph specified if doing anything but xg indexing')\n require(any([len(options.indexes) > 0, \n options.bwa_index_fasta]),\n 'one of --xg_index, --gcsa_index, --snarls_index, --trivial_snarls_index, --id_ranges_index, '\n '--gbwt_index, --minimizer_index, --distance_index, --all_index, --alt_path_gam_index or '\n '--bwa_index_fasta is required')\n require(not options.gbwt_prune or options.node_mapping,\n '--node_mapping required with --gbwt_prune')\n require('gbwt' not in options.indexes or not options.gbwt_input,\n 'only one of --gbwt_index and --gbwt_input can be used at a time')\n if options.gbwt_input:\n require(options.gbwt_prune == 'gbwt', '--gbwt_prune required with --gbwt_input')\n validate_shared_index_options(options)","def test_hash(self):\n ffs = get_available_force_fields()\n\n for ff1, ff2 in itertools.combinations(ffs, 2):\n assert hash(ff1) != hash(ff2)","def forced_checkout_with_faux_obstructions(sbox):\n\n # Make a local tree that partially obstructs the paths coming from the\n # repos but has no true differences.\n expected_output = make_local_tree(sbox, False, False)\n\n expected_wc = svntest.main.greek_state.copy()\n\n svntest.actions.run_and_verify_checkout(sbox.repo_url,\n sbox.wc_dir, expected_output,\n expected_wc, [], '--force')","def branch(self, name=None, clean=None, force=None):\n\n if name and clean:\n raise ValueError('Cannot use both name and clean')\n\n self._client.execute('branch', name, f=force, C=clean)\n\n if name:\n return name\n elif not clean:\n return out.strip()\n else:\n return out[len('reset working directory to branch '):]","def check_versions(ctx, show=False):\n sys.path.insert(0, os.path.join(ROOT_DIR, '_tools'))\n import versions\n versions.main()","def update_repo_all(force=False):\n logger.info(\"attempting to update repo...\")\n for model in kimobjects.Model.all():\n for test in kimobjects.Test.all():\n #logger.info(\"attempting to update test %r\",test)\n if force or not kimobjects.TestResult.test_result_exists(test,model):\n logger.info(\"Running %r vs %r\",test,model)\n try:\n results = run_test_on_model(test,model)\n tr = kimobjects.TestResult(results=results)\n except:\n logger.error(\"WE HAD an error on (%r,%r) with:\\n%r\",test,model,sys.exc_info()[0])\n else:\n logger.info(\"%r vs %r seems current\",test,model)","def test_dependency_order(self):\n sections = self.old_manifest.formula_sections()\n assert sections.index(\"git\") < sections.index(\n \"sub\"\n ), \"Dependency is out of order! git comes after sub\"","def check(force, verbose, packages):\n code = 0\n packages = CFG.package_specs(packages)\n if not packages:\n print(\"No packages installed\")\n sys.exit(0)\n\n for pspec in packages:\n desired = pspec.get_desired_version_info(force=force)\n dv = runez.bold(desired.version)\n manifest = pspec.get_manifest()\n if desired.problem:\n msg = desired.problem\n code = 1\n\n elif not manifest or not manifest.version:\n msg = \"v%s is not installed\" % dv\n code = 1\n\n elif manifest.version == desired.version:\n msg = \"v%s is installed\" % dv\n\n else:\n action = \"upgraded to\" if desired.source == \"latest\" else \"caught up to %s\" % desired.source\n msg = \"v%s installed, can be %s v%s\" % (runez.dim(manifest.version), action, dv)\n\n print(\"%s: %s\" % (pspec.dashed, msg))\n\n sys.exit(code)","def reindex(self):","def reindex(self):","def check_unstaged_changes(self):\n pass","def check_consistency(self):\n raise NotImplementedError()","def vulnerable_versions(self):\n raise NotImplementedError()","def apply_patch():\n assert BaseDatabaseSchemaEditor is not None\n\n def _create_unique_sql(self, *args, **kwargs):\n from django.db.backends.ddl_references import IndexName\n\n statement = orig_create_unique_sql(self, *args, **kwargs)\n\n if statement is not None:\n index_name = statement.parts['name']\n\n if (isinstance(index_name, IndexName) and\n index_name.create_index_name == self._create_index_name):\n # The result will be unquoted. Let's quote it.\n index_name.create_index_name = lambda *args, **kwargs: \\\n self.quote_name(self._create_index_name(*args, **kwargs))\n\n return statement\n\n orig_create_unique_sql = BaseDatabaseSchemaEditor._create_unique_sql\n BaseDatabaseSchemaEditor._create_unique_sql = _create_unique_sql","def checkGit(directory):","async def build_secret_index(self):\n pass","def check_integrity(self) -> None:\n for subset in self.subsets:\n if not self._check_subset_integrity(subset):\n raise ValueError(f\"subset {subset} not found or corrupt\")","def verify_proof(ap, key_index, key, leaf_hash):\n if key_index == len(ap):\n return leaf_hash\n\n if bit_is_set(key, key_index):\n right = verify_proof(ap, key_index+1, key, leaf_hash)\n return hashlib.sha256(ap[len(ap)-key_index-1] + right).digest()\n left = verify_proof(ap, key_index+1, key, leaf_hash)\n return hashlib.sha256(left + ap[len(ap)-key_index-1]).digest()","def test_finder_only_installs_data_require(data: TestData) -> None:\n\n # using a local index (that has pre & dev releases)\n finder = make_test_finder(index_urls=[data.index_url(\"datarequire\")])\n links = finder.find_all_candidates(\"fakepackage\")\n assert {str(v.version) for v in links} == {\"1.0.0\", \"3.3.0\", \"9.9.9\"}","def test_version_control_specified(self):\n # Establish version control on this database\n version = 0\n dbcontrol = ControlledSchema.create(self.engine, self.repos, version)\n self.assertEqual(dbcontrol.version, version)\n\n # Correct when we load it, too\n dbcontrol = ControlledSchema(self.engine, self.repos)\n self.assertEqual(dbcontrol.version, version)\n\n dbcontrol.drop()\n\n # Now try it with a nonzero value\n version = 10\n for i in range(version):\n self.repos.create_script('')\n self.assertEqual(self.repos.latest, version)\n\n # Test with some mid-range value\n dbcontrol = ControlledSchema.create(self.engine,self.repos, 5)\n self.assertEqual(dbcontrol.version, 5)\n dbcontrol.drop()\n\n # Test with max value\n dbcontrol = ControlledSchema.create(self.engine, self.repos, version)\n self.assertEqual(dbcontrol.version, version)\n dbcontrol.drop()","def __gitBisectGood(self):\n pfile = self.project.getProjectFile()\n lastModified = QFileInfo(pfile).lastModified().toString()\n shouldReopen = (\n self.vcs.gitBisect(self.project.getProjectPath(), \"good\") or\n QFileInfo(pfile).lastModified().toString() != lastModified\n )\n if shouldReopen:\n res = E5MessageBox.yesNo(\n self.parent(),\n self.tr(\"Bisect\"),\n self.tr(\"\"\"The project should be reread. Do this now?\"\"\"),\n yesDefault=True)\n if res:\n self.project.reopenProject()","def check_unique_import(\n loaded_db,\n name,\n version,\n nb_trials,\n nb_child_trials=0,\n nb_versions=1,\n algo_state=None,\n trial_links=None,\n ):\n _Helpers.check_db(\n loaded_db,\n nb_exps=1 * nb_versions,\n nb_algos=1 * nb_versions,\n nb_trials=nb_trials * nb_versions,\n nb_benchmarks=0,\n )\n for i in range(nb_versions):\n _Helpers.check_exp(\n loaded_db,\n name,\n version + i,\n nb_trials=nb_trials,\n nb_child_trials=nb_child_trials,\n algo_state=algo_state,\n trial_links=trial_links,\n )","def check_commit(self, commit):\n # pylint: disable=too-many-branches\n if LOG.isEnabledFor(logging.DEBUG):\n LOG.debug('check_commit() Checking mark={} sha1={} file-ct={} -- {}'\n .format( commit['mark']\n , p4gf_util.abbrev(commit['sha1'])\n , len(commit['files'])\n , repr(commit['data'])[:20].splitlines()[0]))\n\n if not commit['author_p4user']:\n raise PreflightException(_(\"User '{user}' not permitted to commit\")\n .format(user=commit['author']['email'].strip('<>')))\n\n if 'merge' in commit:\n ref_is_review = (self.gsreview_coll and\n self.gsreview_coll.ref_in_review_list(self._current_prt.ref))\n if not ref_is_review and not self.ctx.merge_commits:\n raise PreflightException(_('Merge commits are not enabled for this repo.'))\n if (not ref_is_review and\n not self.ctx.branch_creation and self.assigner.have_anonymous_branches):\n msg = _('Git branch creation is prohibited for this repo.')\n p4_branch_names_non_lw = [b.git_branch_name for b in self.ctx.branch_dict().values()\n if b.git_branch_name and not b.is_lightweight]\n if len(p4_branch_names_non_lw) > 1:\n msg += _('\\nThis repo has more than one named branch.'\n '\\nTry altering the push order - '\n 'pushing branches with merge ancestors first.')\n raise PreflightException(msg)\n if LOG.isEnabledFor(logging.DEBUG):\n for parent_mark in commit['merge']:\n parent_sha1 = self.fast_export_marks.get_commit(parent_mark)[:7]\n LOG.debug(\"check_commit() merge mark={} sha1={}\"\n .format(parent_mark, parent_sha1))\n\n if not self.ctx.submodules and 'files' in commit:\n for f in commit['files']:\n if f.get('mode') == '160000':\n if 'first_commit' in commit and not self._path_added(f.get('path'), commit):\n LOG.debug2('check_commit() passed {} in {}'.format(\n f.get('path'), p4gf_util.abbrev(commit['sha1'])))\n continue\n raise PreflightException(\n _('Git submodules not permitted: path={path} commit={commit_sha1}')\n .format(path=f.get('path'), commit_sha1=p4gf_util.abbrev(commit['sha1'])))\n\n for f in commit['files']:\n LOG.debug3(\"check_commit : commit files: \" + _log_fe_file(f))\n err = check_valid_filename(f['path'], self.ctx)\n if err:\n raise PreflightException(err)\n if self.ctx.is_lfs_enabled:\n self._check_lfs(commit, f)\n\n # Warn user about any jobs that appear to not exist\n jobs = G2PJob.lookup_jobs(self.ctx, G2PJob.extract_jobs(commit['data']))\n if jobs:\n for job_id in jobs:\n r = self.ctx.p4run('jobs', '-e', 'job={}'.format(job_id))\n if not r:\n _print_error(_(\"Job '{job_id}' doesn't exist\").format(job_id=job_id))\n # Create pending changes for any Git-Swarm reviews","def write_version(settings, version, force=False):\n semver_path = settings['semver_path']\n filename = settings['semver_branch']\n path = os.path.join(semver_path, filename)\n logger.debug(f'write version:{version} to path:{path} with force:{force}')\n\n path_exists = os.path.exists(path)\n if path_exists:\n current_version = read_version(settings)\n if current_version == version:\n logger.debug(f'version is same as current version {current_version}')\n return\n\n if not path_exists or force:\n write_file(path, version)\n semver_repo = Repo(semver_path)\n index = semver_repo.index\n index.add([filename])\n semver_user_name = settings['semver_user_name']\n semver_user_email = settings['semver_user_email']\n author = Actor(semver_user_name, semver_user_email)\n index.commit(f'semver({filename}): {version}', author=author, committer=author, parent_commits=None)","def test_heads_contains_false(repository: Repository) -> None:\n assert \"branch\" not in repository.heads","def commit(self):\n raise multisearch.errors.FeatureNotAvailableError","def stable():\n env.branch = 'stable'","def _update_head(self, index_entry, branch, new_id):\r\n index_entry['versions'][branch] = new_id\r\n self.db_connection.update_course_index(index_entry)","def check_force_generate(self, force):\n if self.exists() and not force:\n self.log.warn(\n '{} already exists, skipping key generation...'.format(self.key_file)\n )\n return False\n else:\n return True","def update_index(self, ref_gen):\n testing = True\n logging.warning('Updating index')\n es_insert.index(es, ref_gen, self.index_name, testing, action=\"update\")\n logging.warning('Finished updating')","def _commit_to_index( env_dict ):\n from indexer.solr import adapter as adapter_file\n\n adapter = adapter_file.adapter(env_dict)\n adapter.commit()\n if env_dict[\"indexer\"][\"optimise\"]:\n adapter.optimise(maxSegments=1)","def release(store, name, constraints, branches):\n session = Session(store)\n\n # Branches is a list of (name, n_buckets, settings) tuples\n all_buckets = [session.get(Bucket, x, default=CREATE) for x in range(NUM_BUCKETS)]\n\n edited_settings = set.union(*[set(x[2].keys()) for x in branches])\n\n conflicting_experiments = set()\n valid_bucket_indices = []\n\n for idx, bucket in enumerate(all_buckets):\n if is_valid_bucket(bucket, edited_settings, constraints):\n valid_bucket_indices.append(idx)\n else:\n for entry in bucket.entries:\n # Determine if this entry is a potential conflict\n if set(entry.settings.keys()).isdisjoint(edited_settings):\n continue\n conflicting_experiment_id, _ = entry.key\n conflicting_experiments.add(conflicting_experiment_id)\n\n random.shuffle(valid_bucket_indices)\n\n for branch_name, n_buckets, settings in branches:\n key = [name, branch_name]\n bucket_indices = valid_bucket_indices[:n_buckets]\n\n if len(bucket_indices) < n_buckets:\n raise NotEnoughBucketsException(conflicts=conflicting_experiments)\n\n valid_bucket_indices = valid_bucket_indices[n_buckets:]\n\n for bucket_idx in bucket_indices:\n bucket = all_buckets[bucket_idx]\n\n bucket.add(key, settings, constraints)\n\n session.flush()","def check_commit_for_branch( self\n , commit\n , branch_id\n , any_locked_files\n , case_conflict_checker ):\n rev = commit['sha1']\n if LOG.isEnabledFor(logging.DEBUG):\n LOG.debug(\"check_commit_for_branch() \"\n \"Checking branch={} mark={} sha1={} file-ct={} -- {}\"\n .format( branch_id\n , commit['mark']\n , p4gf_util.abbrev(rev)\n , len(commit['files'])\n , repr(commit['data'])[:20].splitlines()[0]))\n\n if self._already_copied_commit(rev, branch_id):\n return\n\n # following checks assume client has been set for branch\n self.ensure_branch_preflight(commit, branch_id)\n with self.ctx.switched_to_branch(\n self._current_branch\n , set_client=self.set_client_on_branch_switch\n ):\n if case_conflict_checker:\n case_conflict_checker.read_fast_export_commit(\n commit, self._current_branch)\n\n # Empty commits require root-level .p4gf_placeholder to be mapped\n # in the current branch view.\n if not commit['files'] and not self._is_placeholder_mapped():\n raise PreflightException(\n _(\"Empty commit {sha1} not permitted. Git Fusion branch views\"\n \" must include root to permit empty commits.\")\n .format(sha1=p4gf_util.abbrev(rev)))\n\n with Timer(CHECK_PROTECTS):\n self._check_protects(commit['author_p4user'], commit['files'])\n\n with Timer(CHECK_OVERLAP):\n self._check_overlap(commit)\n\n # fetch the branch setting only, without cascading to repo/global config\n if self._current_branch.is_read_only:\n raise PreflightException(_(\"Push to branch {branch} prohibited.\")\n .format(branch=self._current_branch.git_branch_name))\n self._check_stream_writable(commit)\n self._check_stream_in_classic(commit)\n\n LOG.debug('checking locked files under //{}/...'.format(self.ctx.p4.client))\n if any_locked_files:\n # Convert the git commit paths to depotPaths\n files_in_commit = [self.ctx.gwt_path(f['path']).to_depot()\n for f in commit['files']]\n LOG.debug(\"files_in_commit {0}\".format(files_in_commit))\n for f in files_in_commit:\n if f in any_locked_files:\n # Collect the names (and clients) of users with locked files.\n # Report back to the pusher so they can take appropriate action.\n msg = _('{file} - locked by {user}').format(file=f,\n user=any_locked_files[f])\n LOG.info(msg)\n raise PreflightException(msg)\n\n # +++ Spend time extracting Jobs and P4Changelist owner\n # here if we actually do need to call\n # the preflight-commit hook.\n if self.ctx.preflight_hook.is_callable():\n jobs = G2PJob.extract_jobs(commit['data'])\n jobs2 = G2PJob.lookup_jobs(self.ctx, jobs)\n self.ctx.preflight_hook(\n ctx = self.ctx\n , fe_commit = commit\n , branch_id = branch_id\n , jobs = jobs2\n )","def test_cherrypickhead_progress(repository: Repository, path: Path) -> None:\n createconflict(repository, path, ours=\"a\", theirs=\"b\")\n\n assert repository.cherrypickhead == repository.heads[\"update\"]","def on_same_branch(left, right):\n left = LooseVersion(left)\n right = LooseVersion(right)\n return left.version[:2] == right.version[:2]","def test_works_index_name(self):\n assert \"test_index-v4\" == self.search.works_index_name(self._db)","def package_version_check(args, parser):\n if (args.build or args.check) and args.package_version:\n parser.error('--package-version works only with --create')","def test_force_f(self, db, course_dir):\n run_nbgrader([\"db\", \"assignment\", \"add\", \"ps1\", \"--db\", db])\n run_nbgrader([\"db\", \"student\", \"add\", \"foo\", \"--db\", db])\n\n self._copy_file(join(\"files\", \"submitted-unchanged.ipynb\"), join(course_dir, \"source\", \"ps1\", \"p1.ipynb\"))\n self._make_file(join(course_dir, \"source\", \"ps1\", \"foo.txt\"), \"foo\")\n self._make_file(join(course_dir, \"source\", \"ps1\", \"data\", \"bar.txt\"), \"bar\")\n run_nbgrader([\"generate_assignment\", \"ps1\", \"--db\", db])\n\n self._copy_file(join(\"files\", \"submitted-unchanged.ipynb\"), join(course_dir, \"submitted\", \"foo\", \"ps1\", \"p1.ipynb\"))\n self._make_file(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"foo.txt\"), \"foo\")\n self._make_file(join(course_dir, \"submitted\", \"foo\", \"ps1\", \"data\", \"bar.txt\"), \"bar\")\n run_nbgrader([\"autograde\", \"ps1\", \"--db\", db])\n\n self._make_file(join(course_dir, \"autograded\", \"foo\", \"ps1\", \"blah.pyc\"), \"asdf\")\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db])\n\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"p1.html\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"data\", \"bar.txt\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"blah.pyc\"))\n\n # check that it skips the existing directory\n remove(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db])\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n\n # force overwrite the supplemental files\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db, \"-f\"])\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n\n # force overwrite\n remove(join(course_dir, \"autograded\", \"foo\", \"ps1\", \"foo.txt\"))\n run_nbgrader([\"generate_feedback\", \"ps1\", \"--db\", db, \"--force\"])\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"p1.html\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"foo.txt\"))\n assert isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"data\", \"bar.txt\"))\n assert not isfile(join(course_dir, \"feedback\", \"foo\", \"ps1\", \"blah.pyc\"))","def index(args):\n for level in ['family', 'language', 'dialect']:\n if args.args[0] in [level, 'all']:\n make_index(level)","def test_finder_only_installs_stable_releases(data: TestData) -> None:\n\n req = install_req_from_line(\"bar\")\n\n # using a local index (that has pre & dev releases)\n finder = make_test_finder(index_urls=[data.index_url(\"pre\")])\n found = finder.find_requirement(req, False)\n assert found is not None\n assert found.link.url.endswith(\"bar-1.0.tar.gz\"), found.link.url\n\n # using find-links\n links = [\"https://foo/bar-1.0.tar.gz\", \"https://foo/bar-2.0b1.tar.gz\"]\n\n finder = make_test_finder(links)\n found = finder.find_requirement(req, False)\n assert found is not None\n assert found.link.url == \"https://foo/bar-1.0.tar.gz\"\n\n links.reverse()\n\n finder = make_test_finder(links)\n found = finder.find_requirement(req, False)\n assert found is not None\n assert found.link.url == \"https://foo/bar-1.0.tar.gz\"","def updateDepTree(self,checked):\n\n if( self.name in checked ):\n return\n else:\n checked.append(self.name)\n\n for mod in self.parent.modules:\n if( mod.name != self.name ):\n mods = mod.reqmodules + mod.optmodules + mod.reqmodules_buildonly\n if( self.name in mods ):\n if( mod.mode != \"install\" or not mod.rebuild ):\n #if( mod.mode != \"install\" and not mod.rebuild ):\n if( mod.useLink ):\n print \"***\\t * WARNING: \" + mod.name + \" is in \\\"link\\\" mode, \" \\\n + \"if you want to rebuild it with the new dependencies set it to \\\"use\\\" mode...!!\"\n else:\n if( not self.parent.noAutomaticRebuilds ):\n if( mod.mode != \"install\" ):\n print \"***\\t * \" + mod.name + \" changed to \\\"install\\\" mode and rebuild Flag set to true!!\"\n mod.mode = \"install\"\n mod.rebuild = True\n mod.preCheckDeps()\n mod.updateDepTree(checked)"],"string":"[\n \"def commit_check(ctx):\\n result = ctx.run(f\\\"{VENV_PREFIX} cz check --rev-range master..\\\", warn=True)\\n if result.exited == 3: # NO_COMMIT_FOUND\\n exit(0)\\n else:\\n exit(result.exited)\",\n \"def validate(cfg: defs.Config) -> List[str]: # noqa: C901\\n res: List[str] = []\\n\\n def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n \\\"\\\"\\\"Validate versions within a single branch.\\\"\\\"\\\"\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\\n\\n def check_component(comp_name: str, comp: defs.Component) -> None:\\n \\\"\\\"\\\"Validate the definition of a single component.\\\"\\\"\\\"\\n if not RE_COMP_NAME.match(comp_name):\\n res.append(f\\\"Invalid component name: {comp_name}\\\")\\n\\n for branch_name, branch in sorted(comp.branches.items()):\\n check_branch(comp_name, branch_name, branch)\\n\\n for comp_name, comp in sorted(cfg.all_components.components.items()):\\n check_component(comp_name, comp)\\n\\n return res\",\n \"def needs_patch():\\n return (IndexName is not None and\\n hasattr(BaseDatabaseSchemaEditor, '_create_unique_sql'))\",\n \"def check_fast_forward():\\n\\n if oldrev != zero: # ignore new branches\\n cmd = ['git', 'rev-list', newrev + '..' + oldrev]\\n missed_refs = call(cmd)\\n missed_ref_count = len(missed_refs)\\n if missed_ref_count > 0:\\n add_error(\\n \\\"[POLICY] Non fast-forward updates are not allowed for this branch\\\")\",\n \"def version_update_has_wrong_order(versions, new_versions):\\n branches = find_branches(versions)\\n if not branches:\\n return False\\n\\n tip = branches[-1]\\n\\n def find_branch_for_version(v):\\n for branch in branches:\\n if on_same_branch(branch, v):\\n return branch\\n return tip\\n\\n chunks = groupby(new_versions, find_branch_for_version)\\n chunks = [[key] + list(values)\\n for key, values\\n in chunks]\\n\\n some_chunk_has_hole = any(map(has_hole, chunks))\\n return some_chunk_has_hole\",\n \"def checkConflicts(self):\\n\\t\\treturn\",\n \"def ensure_tracking_branches(args):\\n man = load_manifest()\\n for (name, project) in man.projects.iteritems():\\n repo = GitRepo(workdir_for_project(project))\\n branch_missing = repo.command(\\n [\\\"rev-parse\\\", \\\"--verify\\\", \\\"-q\\\", project.refspec],\\n capture_stdout=True)\\n \\n if branch_missing:\\n logging.warn(\\\"Branch %s does not exist in project %s. checking out.\\\" %\\n (project.refspec, name))\\n repo.command([\\\"branch\\\", \\\"--track\\\",\\n project.tracking_branch, project.remote_refspec])\",\n \"def _get_index_if_valid(self, course_key, force=False):\\n if course_key.org is None or course_key.course is None or course_key.run is None or course_key.branch is None:\\n return None\\n else:\\n index_entry = self.get_course_index(course_key)\\n is_head = (\\n course_key.version_guid is None or\\n index_entry['versions'][course_key.branch] == course_key.version_guid\\n )\\n if is_head or force:\\n return index_entry\\n else:\\n raise VersionConflictError(\\n course_key,\\n index_entry['versions'][course_key.branch]\\n )\",\n \"def ensure_index(cls, *a, **ka):\\n cls._dbag.ensure_index(*a, **ka)\",\n \"def resolve_conflicts(self, commit=True):\\n pass # pragma: no cover\",\n \"def enforce_clean_option(args, run):\\n repos = run.experiment_info[\\\"repositories\\\"]\\n if not repos:\\n raise RuntimeError(\\n \\\"No version control detected. \\\"\\n \\\"Cannot enforce clean repository.\\\\n\\\"\\n \\\"Make sure that your sources under VCS and the \\\"\\n \\\"corresponding python package is installed.\\\"\\n )\\n else:\\n for repo in repos:\\n if repo[\\\"dirty\\\"]:\\n raise RuntimeError(\\n \\\"EnforceClean: Uncommited changes in \\\"\\n 'the \\\"{}\\\" repository.'.format(repo)\\n )\",\n \"def __gitCheckPatches(self):\\n self.vcs.gitApplyCheckPatches(self.project.getProjectPath(),\\n check=True)\",\n \"def check_branch(\\n comp_name: str, branch_name: str, branch: Dict[str, defs.ComponentVersion]\\n ) -> None:\\n uptodate_files: Dict[pathlib.Path, Tuple[pathlib.Path, defs.ComponentFile]] = {}\\n\\n if not RE_BRANCH_NAME.match(branch_name):\\n res.append(f\\\"{comp_name}: Invalid branch name: {branch_name}\\\")\\n\\n for ver, version in sorted(branch.items()):\\n if not RE_VERSION_STRING.match(ver):\\n res.append(f\\\"{comp_name}/{branch_name}: Invalid version string: {ver}\\\")\\n\\n other_cksums, driver_cksums = _split_by_existence(comp_name, branch_name, version.files)\\n if version.outdated:\\n update_to = [\\n o_version\\n for o_version in branch.values()\\n if not o_version.outdated\\n and _split_by_existence(comp_name, branch_name, o_version.files)[0]\\n == other_cksums\\n ]\\n if len(update_to) != 1:\\n res.append(\\n f\\\"{comp_name}/{branch_name}/{ver}: Got {len(update_to)} possible \\\"\\n f\\\"versions to update to instead of exactly one\\\"\\n )\\n else:\\n bad_files = sorted(\\n relpath\\n for relpath, (path, fdata) in driver_cksums.items()\\n if util.file_sha256sum(path) != fdata.sha256\\n )\\n if bad_files:\\n res.append(f\\\"{comp_name}/{branch_name}/{ver}: Bad checksum for {bad_files}\\\")\\n\\n if not uptodate_files:\\n uptodate_files = driver_cksums\\n elif uptodate_files != driver_cksums:\\n res.append(\\n f\\\"{comp_name}/{branch_name}: All the up-to-date versions should \\\"\\n f\\\"define the same set of files with the same checksums\\\"\\n )\\n\\n if not any(not version.outdated for version in branch.values()):\\n res.append(f\\\"{comp_name}/{branch_name}: No non-outdated versions\\\")\",\n \"def check_heads(repo, their_heads, context):\\n heads = repo.heads()\\n heads_hash = hashutil.sha1(b''.join(sorted(heads))).digest()\\n if not (\\n their_heads == [b'force']\\n or their_heads == heads\\n or their_heads == [b'hashed', heads_hash]\\n ):\\n # someone else committed/pushed/unbundled while we\\n # were transferring data\\n raise error.PushRaced(\\n b'repository changed while %s - please try again' % context\\n )\",\n \"def _does_require_force_update(self):\\n\\n if self.current_version[0][0] > self.version_yaml[0]:\\n # The current version first index is greater than the one we have in the\\n # current version.yaml.\\n\\n # We return True.\\n return True\\n\\n # We return False, we do not need to force the update for\\n # the current version number.\\n return False\",\n \"def __gitVerify(self):\\n self.vcs.gitVerify(self.project.getProjectPath())\",\n \"def test_resetcherrypick_resets_index(repository: Repository, path: Path) -> None:\\n createconflict(repository, path, ours=\\\"a\\\", theirs=\\\"b\\\")\\n\\n repository.resetcherrypick()\\n\\n index = repository._repository.index\\n assert index.write_tree() == repository.head.commit.tree.id\",\n \"def check_fast_forward(self, branch):\\n proc = run_cmd(self.git + ['rev-list', '%s-tmp..%s' %\\n (branch, branch), '--'])\\n if proc.stdout.strip():\\n # Commits have been made on the main branch since the last update\\n # command.\\n raise EmtError('cannot fast-forward the %s branch, please '\\n 'run again the update command' % branch)\",\n \"def index_is_dirty():\\n result, output = popen('git diff --cached', False, False)\\n return len(output) > 0\",\n \"def index_ddl_if_exists(self):\\n return exclusions.closed()\",\n \"def _prepare_manual_resolve(self):\\n # Files that have been deleted between branch and cherry-pick will not have\\n # their skip-worktree bit set so set it manually for those files to avoid\\n # git status incorrectly listing them as unstaged deletes.\\n repo_status = self._run_git_command(\\n ['-c', 'core.quotePath=false', 'status', '--porcelain']).splitlines()\\n extra_files = [f[3:] for f in repo_status if f[:2] == ' D']\\n if extra_files:\\n self._run_git_command_with_stdin(\\n ['update-index', '--skip-worktree', '--stdin'],\\n stdin='\\\\n'.join(extra_files) + '\\\\n')\",\n \"def validate_shared_index_options(options):\\n \\n if options.vcf_phasing:\\n require(all([vcf.endswith('.vcf.gz') for vcf in options.vcf_phasing]),\\n 'input phasing files must end with .vcf.gz')\\n if 'gbwt' in options.indexes:\\n require(len(options.vcf_phasing) > 0, 'generating a GBWT requires a VCF with phasing information')\\n if options.gbwt_prune:\\n require(('gbwt' in options.indexes) or options.gbwt_input, '--gbwt_index or --gbwt_input required for --gbwt_prune')\\n if options.vcf_phasing_regions:\\n require('gbwt' in options.indexes, \\\"cannot hint regions to GBWT indexer without building a GBWT index\\\")\",\n \"def replace_old(self):\\n # Tell the client the index is ready to be used and to no longer show it as building.\\n self.db.indexes.find_one_and_update({\\\"_id\\\": self.params[\\\"index_id\\\"]}, {\\n \\\"$set\\\": {\\n \\\"ready\\\": True\\n }\\n })\\n\\n self.dispatch(\\\"indexes\\\", \\\"update\\\", [self.params[\\\"index_id\\\"]])\\n\\n active_indexes = virtool.db.sync.get_active_index_ids(self.db, self.params[\\\"ref_id\\\"])\\n\\n remove_unused_index_files(self.params[\\\"reference_path\\\"], active_indexes)\\n\\n query = {\\n \\\"_id\\\": {\\n \\\"$not\\\": {\\n \\\"$in\\\": active_indexes\\n }\\n }\\n }\\n\\n self.db.indexes.update_many(query, {\\n \\\"$set\\\": {\\n \\\"has_files\\\": False\\n }\\n })\\n\\n id_list = self.db.indexes.distinct(\\\"_id\\\", query)\\n\\n self.dispatch(\\\"indexes\\\", \\\"update\\\", id_list)\\n\\n # Find OTUs with changes.\\n pipeline = [\\n {\\\"$project\\\": {\\n \\\"reference\\\": True,\\n \\\"version\\\": True,\\n \\\"last_indexed_version\\\": True,\\n \\\"comp\\\": {\\\"$cmp\\\": [\\\"$version\\\", \\\"$last_indexed_version\\\"]}\\n }},\\n {\\\"$match\\\": {\\n \\\"reference.id\\\": self.params[\\\"ref_id\\\"],\\n \\\"comp\\\": {\\\"$ne\\\": 0}\\n }},\\n {\\\"$group\\\": {\\n \\\"_id\\\": \\\"$version\\\",\\n \\\"id_list\\\": {\\n \\\"$addToSet\\\": \\\"$_id\\\"\\n }\\n }}\\n ]\\n\\n id_version_key = {agg[\\\"_id\\\"]: agg[\\\"id_list\\\"] for agg in self.db.otus.aggregate(pipeline)}\\n\\n # For each version number\\n for version, id_list in id_version_key.items():\\n self.db.otus.update_many({\\\"_id\\\": {\\\"$in\\\": id_list}}, {\\n \\\"$set\\\": {\\n \\\"last_indexed_version\\\": version\\n }\\n })\",\n \"def _rebuild_index(self):\\n from django.core.management import call_command\\n call_command('rebuild_index', interactive=False, verbosity=0)\",\n \"def test_multiple_packages_same_version(self):\\n with patch.object(self.request.access, \\\"allow_overwrite\\\", []):\\n name, version = \\\"a\\\", \\\"1\\\"\\n path1 = \\\"old_package_path-1.tar.gz\\\"\\n self.db.upload(path1, BytesIO(b\\\"test1234\\\"), name, version)\\n path2 = \\\"new_path-1.whl\\\"\\n self.db.upload(path2, BytesIO(b\\\"test1234\\\"), name, version)\\n\\n all_versions = self.db.all(name)\\n self.assertEqual(len(all_versions), 2)\",\n \"def test_multiple_packages_same_version(self):\\n with patch.object(self.request.access, \\\"allow_overwrite\\\", []):\\n name, version = \\\"a\\\", \\\"1\\\"\\n path1 = \\\"old_package_path-1.tar.gz\\\"\\n self.db.upload(path1, BytesIO(b\\\"test1234\\\"), name, version)\\n path2 = \\\"new_path-1.whl\\\"\\n self.db.upload(path2, BytesIO(b\\\"test1234\\\"), name, version)\\n\\n all_versions = self.db.all(name)\\n self.assertEqual(len(all_versions), 2)\",\n \"def test_multiple_packages_same_version(self):\\n with patch.object(self.request.access, \\\"allow_overwrite\\\", []):\\n name, version = \\\"a\\\", \\\"1\\\"\\n path1 = \\\"old_package_path-1.tar.gz\\\"\\n self.db.upload(path1, BytesIO(b\\\"test1234\\\"), name, version)\\n path2 = \\\"new_path-1.whl\\\"\\n self.db.upload(path2, BytesIO(b\\\"test1234\\\"), name, version)\\n\\n all_versions = self.db.all(name)\\n self.assertEqual(len(all_versions), 2)\",\n \"def get_all_changes_for_package(\\n provider_package_id: str,\\n verbose: bool,\\n base_branch: str,\\n force: bool,\\n) -> tuple[bool, list[list[Change]] | Change | None, str]:\\n provider_details = get_provider_details(provider_package_id)\\n current_version = provider_details.versions[0]\\n current_tag_no_suffix = get_version_tag(current_version, provider_package_id)\\n if verbose:\\n console.print(f\\\"Checking if tag '{current_tag_no_suffix}' exist.\\\")\\n if not force and not subprocess.call(\\n get_git_tag_check_command(current_tag_no_suffix),\\n cwd=provider_details.source_provider_package_path,\\n stderr=subprocess.DEVNULL,\\n ):\\n if verbose:\\n console.print(f\\\"The tag {current_tag_no_suffix} exists.\\\")\\n # The tag already exists\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\", current_tag_no_suffix),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n if changes:\\n provider_details = get_provider_details(provider_package_id)\\n doc_only_change_file = os.path.join(\\n provider_details.source_provider_package_path, \\\".latest-doc-only-change.txt\\\"\\n )\\n if os.path.exists(doc_only_change_file):\\n with open(doc_only_change_file) as f:\\n last_doc_only_hash = f.read().strip()\\n try:\\n changes_since_last_doc_only_check = subprocess.check_output(\\n get_git_log_command(\\n verbose, f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\", last_doc_only_hash\\n ),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n if not changes_since_last_doc_only_check:\\n console.print()\\n console.print(\\n \\\"[yellow]The provider has doc-only changes since the last release. Skipping[/]\\\"\\n )\\n # Returns 66 in case of doc-only changes\\n sys.exit(66)\\n if len(changes) > len(changes_since_last_doc_only_check):\\n # if doc-only was released after previous release - use it as starting point\\n # but if before - stay with the releases from last tag.\\n changes = changes_since_last_doc_only_check\\n except subprocess.CalledProcessError:\\n # ignore when the commit mentioned as last doc-only change is obsolete\\n pass\\n\\n console.print(f\\\"[yellow]The provider {provider_package_id} has changes since last release[/]\\\")\\n console.print()\\n console.print(f\\\"[bright_blue]Provider: {provider_package_id}[/]\\\\n\\\")\\n changes_table, array_of_changes = convert_git_changes_to_table(\\n f\\\"NEXT VERSION AFTER + {provider_details.versions[0]}\\\",\\n changes,\\n base_url=\\\"https://github.com/apache/airflow/commit/\\\",\\n markdown=False,\\n )\\n print_changes_table(changes_table)\\n return False, array_of_changes[0], changes_table\\n else:\\n console.print(f\\\"No changes for {provider_package_id}\\\")\\n return False, None, \\\"\\\"\\n if verbose:\\n console.print(\\\"The tag does not exist. \\\")\\n if len(provider_details.versions) == 1:\\n console.print(\\n f\\\"The provider '{provider_package_id}' has never been released but it is ready to release!\\\\n\\\"\\n )\\n else:\\n console.print(f\\\"New version of the '{provider_package_id}' package is ready to be released!\\\\n\\\")\\n next_version_tag = f\\\"{HEAD_OF_HTTPS_REMOTE}/{base_branch}\\\"\\n changes_table = \\\"\\\"\\n current_version = provider_details.versions[0]\\n list_of_list_of_changes: list[list[Change]] = []\\n for version in provider_details.versions[1:]:\\n version_tag = get_version_tag(version, provider_package_id)\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, next_version_tag, version_tag),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\\n current_version, changes, base_url=\\\"https://github.com/apache/airflow/commit/\\\", markdown=False\\n )\\n changes_table += changes_table_for_version\\n list_of_list_of_changes.append(array_of_changes_for_version)\\n next_version_tag = version_tag\\n current_version = version\\n changes = subprocess.check_output(\\n get_git_log_command(verbose, next_version_tag),\\n cwd=provider_details.source_provider_package_path,\\n text=True,\\n )\\n changes_table_for_version, array_of_changes_for_version = convert_git_changes_to_table(\\n current_version, changes, base_url=\\\"https://github.com/apache/airflow/commit/\\\", markdown=False\\n )\\n changes_table += changes_table_for_version\\n if verbose:\\n print_changes_table(changes_table)\\n return True, list_of_list_of_changes or None, changes_table\",\n \"def _check_integrity(self):\\n try:\\n for q in self:\\n pass\\n except TreeIntegrityError:\\n raise ValueError('Invalid \\\"tree\\\" parameter.')\",\n \"def check_consistency(self, es):\",\n \"def verify_branch(root, index, proof, output_as_int=False):\\n index = get_index_in_permuted(index, 2**len(proof) // 2)\\n # I think this is a bug. Should be 2**len(proof) // 2\\n # But I think it's OK since we're only doing parity checks\\n #index += 2**len(proof)\\n index += 2**len(proof) // 2\\n v = proof[0]\\n for p in proof[1:]:\\n if index % 2:\\n v = blake(p + v)\\n else:\\n v = blake(v + p)\\n index //= 2\\n assert v == root\\n return int.from_bytes(proof[0], 'big') if output_as_int else proof[0]\",\n \"def flag_all_commit(self):\\n\\t\\tfor k in self.data.keys():\\n\\t\\t\\tindex = 0\\n\\t\\t\\tfor item in self[k]:\\n\\t\\t\\t\\tself.data[k][index]['meta']['needs_commit'] = True\\n\\t\\t\\t\\tindex += 1\",\n \"def checkConflicts(self):\\n\\t\\tapDisplay.printError(\\\"you did not create a 'checkConflicts' function in your script\\\")\\n\\t\\traise NotImplementedError()\",\n \"def svn_fs_check_related(*args):\\r\\n return _fs.svn_fs_check_related(*args)\",\n \"def test_heads_create_existing_branch_force(repository: Repository) -> None:\\n head, heads = repository.head, repository.heads\\n branch = heads.create(\\\"branch\\\", head.commit)\\n updatefile(repository.path / \\\"a\\\")\\n heads.create(branch.name, head.commit, force=True)\\n assert head.commit == branch.commit\",\n \"def _get_index_if_valid(self, locator, force=False, continue_version=False):\\r\\n if locator.org is None or locator.offering is None or locator.branch is None:\\r\\n if continue_version:\\r\\n raise InsufficientSpecificationError(\\r\\n \\\"To continue a version, the locator must point to one ({}).\\\".format(locator)\\r\\n )\\r\\n else:\\r\\n return None\\r\\n else:\\r\\n index_entry = self.db_connection.get_course_index(locator)\\r\\n is_head = (\\r\\n locator.version_guid is None or\\r\\n index_entry['versions'][locator.branch] == locator.version_guid\\r\\n )\\r\\n if (is_head or (force and not continue_version)):\\r\\n return index_entry\\r\\n else:\\r\\n raise VersionConflictError(\\r\\n locator,\\r\\n index_entry['versions'][locator.branch]\\r\\n )\",\n \"def commit(self, force=False):\\n if len(self.changed) > 0 or force: \\n self.things_obj.set_all(self.things_dict, self.parent)\\n logging.error('commited!!!!!!!!')\",\n \"def __checkFeatureIndex__(self, index, indexes):\\n if index is not False:\\n indexes.append(index)\",\n \"def unprotect(args):\\n\\n for branch in args.branch:\\n logging.info(\\\"Unprotecting %s...\\\", branch)\\n #pylint: disable=no-value-for-parameter, cell-var-from-loop\\n manage_repos(args, lambda repo, _: repo.unprotect(branch))\",\n \"def test_heads_contains_true(repository: Repository) -> None:\\n assert repository.head.name in repository.heads\",\n \"def rebuild_index(self):\\n with warnings.catch_warnings():\\n warnings.simplefilter('ignore')\\n with get_db_connection() as db:\\n c = db.cursor()\\n execute_with_retry(db, c, self._rebuild_index)\",\n \"def verify_index(yaml_data, build_index=True):\\n print('='*10, 'Verifying Index', '='*10)\\n index_utils_command = [\\n os.path.join(yaml_data['root'], yaml_data['index_utils_command']),\\n '-index', os.path.join(yaml_data['index_root'] if yaml_data['index_root'] else '',\\n 'lucene-index.{0}.pos+docvectors{1}'.format(yaml_data['name'], '+rawdocs' if 'storeRawdocs' in yaml_data['index_options'] else ''))\\n if build_index else yaml_data['index_path'],\\n '-stats'\\n ]\\n out = check_output(' '.join(index_utils_command)).decode('utf-8').split('\\\\n')\\n for line in out:\\n stat = line.split(':')[0]\\n if stat in yaml_data['index_stats']:\\n value = int(line.split(':')[1])\\n assert value == yaml_data['index_stats'][stat]\\n print(line)\\n print(OKBLUE, '='*10, 'Verifying Index Succeed', '='*10, ENDC)\",\n \"def test_major(scraper, version_parts):\\n\\n new_version_parts = list(version_parts)\\n new_version_parts[0] = int(new_version_parts[0]) + 1\\n\\n assert scraper.is_compatible_with(generate_version(new_version_parts)) is False\",\n \"def test_verify_recursive_and_transverse_acl_options():\",\n \"def test_force_and_update(self, _get_repo_name, # pylint: disable=unused-argument\\n set_version_from_git_tag_mock):\\n force_semver_git_tag_plugin(self.project, self.logger)\\n self.project.set_property('semver_git_tag_increment_part', 'minor')\\n update_version_from_git_tag(self.project, self.logger)\\n self.assertEqual(set_version_from_git_tag_mock.call_count, 2)\\n self.assertEqual(self.logger.info.call_count, 2)\\n self.logger.warn.assert_called_once_with(\\n \\\"Property `semver_git_tag_increment_part` was changed. \\\"\\n \\\"For better compatibility recommended to use \\\"\\n \\\"command line `pyb ... -P semver_git_tag_increment_part=...`, \\\"\\n \\\"otherwise some version-related properties could \\\"\\n \\\"be spoiled.\\\"\\n )\",\n \"def commit():\\n return _run_indexer_func(\\\"commit\\\")\",\n \"def createVersions(self, force = False):\\n\\t\\tlow = self.makeLow(force)\\n\\t\\tlow.makeTx(force)\\n\\t\\tmid = self.makeMid(force)\\n\\t\\tmid.makeTx(force)\\n\\t\\tself.makeTx(force)\",\n \"def test_force(self, db, course_dir):\\n run_nbgrader([\\\"db\\\", \\\"assignment\\\", \\\"add\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n run_nbgrader([\\\"db\\\", \\\"student\\\", \\\"add\\\", \\\"foo\\\", \\\"--db\\\", db])\\n\\n self._copy_file(join(\\\"files\\\", \\\"submitted-unchanged.ipynb\\\"), join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"p1.ipynb\\\"))\\n self._make_file(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"foo.txt\\\"), \\\"foo\\\")\\n self._make_file(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"), \\\"bar\\\")\\n run_nbgrader([\\\"generate_assignment\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n self._copy_file(join(\\\"files\\\", \\\"submitted-unchanged.ipynb\\\"), join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.ipynb\\\"))\\n self._make_file(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"), \\\"foo\\\")\\n self._make_file(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"), \\\"bar\\\")\\n run_nbgrader([\\\"autograde\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n self._make_file(join(course_dir, \\\"autograded\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"), \\\"asdf\\\")\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.html\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"))\\n\\n # check that it skips the existing directory\\n remove(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n\\n # force overwrite the supplemental files\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db, \\\"--force\\\"])\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n\\n # force overwrite\\n remove(join(course_dir, \\\"autograded\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db, \\\"--force\\\"])\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.html\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"))\",\n \"def test_version_control_invalid(self):\\n versions = ('Thirteen', '-1', -1, '' , 13)\\n # A fresh repository doesn't go up to version 13 yet\\n for version in versions:\\n #self.assertRaises(ControlledSchema.InvalidVersionError,\\n # Can't have custom errors with assertRaises...\\n try:\\n ControlledSchema.create(self.engine, self.repos, version)\\n self.assertTrue(False, repr(version))\\n except exceptions.InvalidVersionError:\\n pass\",\n \"def test_incorrect_case_file_index(data: TestData) -> None:\\n req = install_req_from_line(\\\"dinner\\\")\\n finder = make_test_finder(index_urls=[data.find_links3])\\n found = finder.find_requirement(req, False)\\n assert found is not None\\n assert found.link.url.endswith(\\\"Dinner-2.0.tar.gz\\\")\",\n \"def test_sort_git_master_and_latest(self):\\n identifiers = [\\\"latest\\\", \\\"master\\\", \\\"1.0\\\", \\\"2.0\\\", \\\"1.1\\\", \\\"1.9\\\", \\\"1.10\\\"]\\n self.project.repo_type = REPO_TYPE_GIT\\n self.project.save()\\n self.project.versions.get(slug=LATEST).delete()\\n\\n for identifier in identifiers:\\n get(\\n Version,\\n project=self.project,\\n type=BRANCH,\\n identifier=identifier,\\n verbose_name=identifier,\\n slug=identifier,\\n )\\n\\n versions = list(Version.objects.filter(project=self.project))\\n self.assertEqual(\\n [\\\"master\\\", \\\"latest\\\", \\\"2.0\\\", \\\"1.10\\\", \\\"1.9\\\", \\\"1.1\\\", \\\"1.0\\\"],\\n [v.slug for v in sort_version_aware(versions)],\\n )\",\n \"def try_cherry_pick_all(rev):\\n num_par = len(parents(rev))\\n any_success = False\\n for i in range(1, num_par + 1):\\n any_success = any_success or try_cherry_pick(rev, mainline=i)\\n return any_success\",\n \"def ensure_indexes(self):\\n self.db_connection.ensure_indexes()\",\n \"def __gitVerifyBundle(self):\\n self.vcs.gitVerifyBundle(self.project.getProjectPath())\",\n \"def test_unique_together(self):\\n\\n for mb_model in self.mb_model_list:\\n indexes = connection.introspection.get_indexes(\\n self.cursor, mb_model._meta.db_table)\\n if not indexes and not is_db_view(mb_model._meta.db_table):\\n self.assertTrue(mb_model._meta.unique_together)\",\n \"def isort_check(ctx):\\n ctx.run(f\\\"{VENV_PREFIX} isort --atomic --check-only .\\\")\",\n \"def ensure_sync_master_branch(self):\\n # TODO(robertocn): Investigate what causes the states mentioned in the\\n # docstring in the first place.\\n self.api.m.git('update-ref', 'refs/heads/master',\\n 'refs/remotes/origin/master')\\n self.api.m.git('checkout', 'master', cwd=self.api.m.path['checkout'])\",\n \"def dirty_branches(self):\\n # If no course index has been set, then no branches have changed\\n if self.index is None:\\n return []\\n\\n # If there was no index in the database to start with, then all branches\\n # are dirty by definition\\n if self.initial_index is None:\\n return list(self.index.get('versions', {}).keys())\\n\\n # Return branches whose ids differ between self.index and self.initial_index\\n return [\\n branch\\n for branch, _id\\n in self.index.get('versions', {}).items()\\n if self.initial_index.get('versions', {}).get(branch) != _id\\n ]\",\n \"def protect_branch_with_approvals(branch_name: str, count: int = 1,\\n require_code_owner_review: bool = False) -> repoproc_t:\\n def _protect_branch_with_approvals(org: Organization, repo: Repository,\\n branches: Dict[str, Branch]) -> List[Change[str]]:\\n if branch_name in branches:\\n return _protect_branch(branches[branch_name], count, require_code_owner_review)\\n else:\\n print_warning(\\\"Requested to protect branch %s on repo %s, but the branch does not exist.\\\" %\\n (highlight(branch_name), highlight(repo.name)))\\n return []\\n return _protect_branch_with_approvals\",\n \"def precommit(exit=True):\\n tmpdir = tempfile.mkdtemp()\\n\\n try:\\n copy_index(tmpdir)\\n\\n modified = check_output(['git', 'diff', '--cached', '--name-only',\\n '--diff-filter=ACMRT'])\\n modified = [name.strip() for name in modified.splitlines()]\\n path = os.environ['PATH']\\n with pushd(tmpdir) as prevdir:\\n conf = load_conf()\\n # Activate the virtualenv before running checks\\n if 'env' in conf:\\n binpath = os.path.abspath(os.path.join(prevdir,\\n conf['env']['path'],\\n 'bin'))\\n if binpath not in path.split(os.pathsep):\\n path = binpath + os.pathsep + path\\n retcode = run_checks(conf.get('hooks_all', []),\\n conf.get('hooks_modified', []), modified,\\n path)\\n\\n if exit:\\n sys.exit(retcode)\\n else:\\n return retcode\\n finally:\\n shutil.rmtree(tmpdir)\",\n \"def validate_index_options(options):\\n if len(options.indexes) > 0:\\n require(len(options.graphs) == 0 or options.chroms, '--chroms must be specified for --graphs')\\n require(len(options.graphs) == 1 or len(options.chroms) == len(options.graphs),\\n '--chroms and --graphs must have'\\n ' same number of arguments if more than one graph specified if doing anything but xg indexing')\\n require(any([len(options.indexes) > 0, \\n options.bwa_index_fasta]),\\n 'one of --xg_index, --gcsa_index, --snarls_index, --trivial_snarls_index, --id_ranges_index, '\\n '--gbwt_index, --minimizer_index, --distance_index, --all_index, --alt_path_gam_index or '\\n '--bwa_index_fasta is required')\\n require(not options.gbwt_prune or options.node_mapping,\\n '--node_mapping required with --gbwt_prune')\\n require('gbwt' not in options.indexes or not options.gbwt_input,\\n 'only one of --gbwt_index and --gbwt_input can be used at a time')\\n if options.gbwt_input:\\n require(options.gbwt_prune == 'gbwt', '--gbwt_prune required with --gbwt_input')\\n validate_shared_index_options(options)\",\n \"def test_hash(self):\\n ffs = get_available_force_fields()\\n\\n for ff1, ff2 in itertools.combinations(ffs, 2):\\n assert hash(ff1) != hash(ff2)\",\n \"def forced_checkout_with_faux_obstructions(sbox):\\n\\n # Make a local tree that partially obstructs the paths coming from the\\n # repos but has no true differences.\\n expected_output = make_local_tree(sbox, False, False)\\n\\n expected_wc = svntest.main.greek_state.copy()\\n\\n svntest.actions.run_and_verify_checkout(sbox.repo_url,\\n sbox.wc_dir, expected_output,\\n expected_wc, [], '--force')\",\n \"def branch(self, name=None, clean=None, force=None):\\n\\n if name and clean:\\n raise ValueError('Cannot use both name and clean')\\n\\n self._client.execute('branch', name, f=force, C=clean)\\n\\n if name:\\n return name\\n elif not clean:\\n return out.strip()\\n else:\\n return out[len('reset working directory to branch '):]\",\n \"def check_versions(ctx, show=False):\\n sys.path.insert(0, os.path.join(ROOT_DIR, '_tools'))\\n import versions\\n versions.main()\",\n \"def update_repo_all(force=False):\\n logger.info(\\\"attempting to update repo...\\\")\\n for model in kimobjects.Model.all():\\n for test in kimobjects.Test.all():\\n #logger.info(\\\"attempting to update test %r\\\",test)\\n if force or not kimobjects.TestResult.test_result_exists(test,model):\\n logger.info(\\\"Running %r vs %r\\\",test,model)\\n try:\\n results = run_test_on_model(test,model)\\n tr = kimobjects.TestResult(results=results)\\n except:\\n logger.error(\\\"WE HAD an error on (%r,%r) with:\\\\n%r\\\",test,model,sys.exc_info()[0])\\n else:\\n logger.info(\\\"%r vs %r seems current\\\",test,model)\",\n \"def test_dependency_order(self):\\n sections = self.old_manifest.formula_sections()\\n assert sections.index(\\\"git\\\") < sections.index(\\n \\\"sub\\\"\\n ), \\\"Dependency is out of order! git comes after sub\\\"\",\n \"def check(force, verbose, packages):\\n code = 0\\n packages = CFG.package_specs(packages)\\n if not packages:\\n print(\\\"No packages installed\\\")\\n sys.exit(0)\\n\\n for pspec in packages:\\n desired = pspec.get_desired_version_info(force=force)\\n dv = runez.bold(desired.version)\\n manifest = pspec.get_manifest()\\n if desired.problem:\\n msg = desired.problem\\n code = 1\\n\\n elif not manifest or not manifest.version:\\n msg = \\\"v%s is not installed\\\" % dv\\n code = 1\\n\\n elif manifest.version == desired.version:\\n msg = \\\"v%s is installed\\\" % dv\\n\\n else:\\n action = \\\"upgraded to\\\" if desired.source == \\\"latest\\\" else \\\"caught up to %s\\\" % desired.source\\n msg = \\\"v%s installed, can be %s v%s\\\" % (runez.dim(manifest.version), action, dv)\\n\\n print(\\\"%s: %s\\\" % (pspec.dashed, msg))\\n\\n sys.exit(code)\",\n \"def reindex(self):\",\n \"def reindex(self):\",\n \"def check_unstaged_changes(self):\\n pass\",\n \"def check_consistency(self):\\n raise NotImplementedError()\",\n \"def vulnerable_versions(self):\\n raise NotImplementedError()\",\n \"def apply_patch():\\n assert BaseDatabaseSchemaEditor is not None\\n\\n def _create_unique_sql(self, *args, **kwargs):\\n from django.db.backends.ddl_references import IndexName\\n\\n statement = orig_create_unique_sql(self, *args, **kwargs)\\n\\n if statement is not None:\\n index_name = statement.parts['name']\\n\\n if (isinstance(index_name, IndexName) and\\n index_name.create_index_name == self._create_index_name):\\n # The result will be unquoted. Let's quote it.\\n index_name.create_index_name = lambda *args, **kwargs: \\\\\\n self.quote_name(self._create_index_name(*args, **kwargs))\\n\\n return statement\\n\\n orig_create_unique_sql = BaseDatabaseSchemaEditor._create_unique_sql\\n BaseDatabaseSchemaEditor._create_unique_sql = _create_unique_sql\",\n \"def checkGit(directory):\",\n \"async def build_secret_index(self):\\n pass\",\n \"def check_integrity(self) -> None:\\n for subset in self.subsets:\\n if not self._check_subset_integrity(subset):\\n raise ValueError(f\\\"subset {subset} not found or corrupt\\\")\",\n \"def verify_proof(ap, key_index, key, leaf_hash):\\n if key_index == len(ap):\\n return leaf_hash\\n\\n if bit_is_set(key, key_index):\\n right = verify_proof(ap, key_index+1, key, leaf_hash)\\n return hashlib.sha256(ap[len(ap)-key_index-1] + right).digest()\\n left = verify_proof(ap, key_index+1, key, leaf_hash)\\n return hashlib.sha256(left + ap[len(ap)-key_index-1]).digest()\",\n \"def test_finder_only_installs_data_require(data: TestData) -> None:\\n\\n # using a local index (that has pre & dev releases)\\n finder = make_test_finder(index_urls=[data.index_url(\\\"datarequire\\\")])\\n links = finder.find_all_candidates(\\\"fakepackage\\\")\\n assert {str(v.version) for v in links} == {\\\"1.0.0\\\", \\\"3.3.0\\\", \\\"9.9.9\\\"}\",\n \"def test_version_control_specified(self):\\n # Establish version control on this database\\n version = 0\\n dbcontrol = ControlledSchema.create(self.engine, self.repos, version)\\n self.assertEqual(dbcontrol.version, version)\\n\\n # Correct when we load it, too\\n dbcontrol = ControlledSchema(self.engine, self.repos)\\n self.assertEqual(dbcontrol.version, version)\\n\\n dbcontrol.drop()\\n\\n # Now try it with a nonzero value\\n version = 10\\n for i in range(version):\\n self.repos.create_script('')\\n self.assertEqual(self.repos.latest, version)\\n\\n # Test with some mid-range value\\n dbcontrol = ControlledSchema.create(self.engine,self.repos, 5)\\n self.assertEqual(dbcontrol.version, 5)\\n dbcontrol.drop()\\n\\n # Test with max value\\n dbcontrol = ControlledSchema.create(self.engine, self.repos, version)\\n self.assertEqual(dbcontrol.version, version)\\n dbcontrol.drop()\",\n \"def __gitBisectGood(self):\\n pfile = self.project.getProjectFile()\\n lastModified = QFileInfo(pfile).lastModified().toString()\\n shouldReopen = (\\n self.vcs.gitBisect(self.project.getProjectPath(), \\\"good\\\") or\\n QFileInfo(pfile).lastModified().toString() != lastModified\\n )\\n if shouldReopen:\\n res = E5MessageBox.yesNo(\\n self.parent(),\\n self.tr(\\\"Bisect\\\"),\\n self.tr(\\\"\\\"\\\"The project should be reread. Do this now?\\\"\\\"\\\"),\\n yesDefault=True)\\n if res:\\n self.project.reopenProject()\",\n \"def check_unique_import(\\n loaded_db,\\n name,\\n version,\\n nb_trials,\\n nb_child_trials=0,\\n nb_versions=1,\\n algo_state=None,\\n trial_links=None,\\n ):\\n _Helpers.check_db(\\n loaded_db,\\n nb_exps=1 * nb_versions,\\n nb_algos=1 * nb_versions,\\n nb_trials=nb_trials * nb_versions,\\n nb_benchmarks=0,\\n )\\n for i in range(nb_versions):\\n _Helpers.check_exp(\\n loaded_db,\\n name,\\n version + i,\\n nb_trials=nb_trials,\\n nb_child_trials=nb_child_trials,\\n algo_state=algo_state,\\n trial_links=trial_links,\\n )\",\n \"def check_commit(self, commit):\\n # pylint: disable=too-many-branches\\n if LOG.isEnabledFor(logging.DEBUG):\\n LOG.debug('check_commit() Checking mark={} sha1={} file-ct={} -- {}'\\n .format( commit['mark']\\n , p4gf_util.abbrev(commit['sha1'])\\n , len(commit['files'])\\n , repr(commit['data'])[:20].splitlines()[0]))\\n\\n if not commit['author_p4user']:\\n raise PreflightException(_(\\\"User '{user}' not permitted to commit\\\")\\n .format(user=commit['author']['email'].strip('<>')))\\n\\n if 'merge' in commit:\\n ref_is_review = (self.gsreview_coll and\\n self.gsreview_coll.ref_in_review_list(self._current_prt.ref))\\n if not ref_is_review and not self.ctx.merge_commits:\\n raise PreflightException(_('Merge commits are not enabled for this repo.'))\\n if (not ref_is_review and\\n not self.ctx.branch_creation and self.assigner.have_anonymous_branches):\\n msg = _('Git branch creation is prohibited for this repo.')\\n p4_branch_names_non_lw = [b.git_branch_name for b in self.ctx.branch_dict().values()\\n if b.git_branch_name and not b.is_lightweight]\\n if len(p4_branch_names_non_lw) > 1:\\n msg += _('\\\\nThis repo has more than one named branch.'\\n '\\\\nTry altering the push order - '\\n 'pushing branches with merge ancestors first.')\\n raise PreflightException(msg)\\n if LOG.isEnabledFor(logging.DEBUG):\\n for parent_mark in commit['merge']:\\n parent_sha1 = self.fast_export_marks.get_commit(parent_mark)[:7]\\n LOG.debug(\\\"check_commit() merge mark={} sha1={}\\\"\\n .format(parent_mark, parent_sha1))\\n\\n if not self.ctx.submodules and 'files' in commit:\\n for f in commit['files']:\\n if f.get('mode') == '160000':\\n if 'first_commit' in commit and not self._path_added(f.get('path'), commit):\\n LOG.debug2('check_commit() passed {} in {}'.format(\\n f.get('path'), p4gf_util.abbrev(commit['sha1'])))\\n continue\\n raise PreflightException(\\n _('Git submodules not permitted: path={path} commit={commit_sha1}')\\n .format(path=f.get('path'), commit_sha1=p4gf_util.abbrev(commit['sha1'])))\\n\\n for f in commit['files']:\\n LOG.debug3(\\\"check_commit : commit files: \\\" + _log_fe_file(f))\\n err = check_valid_filename(f['path'], self.ctx)\\n if err:\\n raise PreflightException(err)\\n if self.ctx.is_lfs_enabled:\\n self._check_lfs(commit, f)\\n\\n # Warn user about any jobs that appear to not exist\\n jobs = G2PJob.lookup_jobs(self.ctx, G2PJob.extract_jobs(commit['data']))\\n if jobs:\\n for job_id in jobs:\\n r = self.ctx.p4run('jobs', '-e', 'job={}'.format(job_id))\\n if not r:\\n _print_error(_(\\\"Job '{job_id}' doesn't exist\\\").format(job_id=job_id))\\n # Create pending changes for any Git-Swarm reviews\",\n \"def write_version(settings, version, force=False):\\n semver_path = settings['semver_path']\\n filename = settings['semver_branch']\\n path = os.path.join(semver_path, filename)\\n logger.debug(f'write version:{version} to path:{path} with force:{force}')\\n\\n path_exists = os.path.exists(path)\\n if path_exists:\\n current_version = read_version(settings)\\n if current_version == version:\\n logger.debug(f'version is same as current version {current_version}')\\n return\\n\\n if not path_exists or force:\\n write_file(path, version)\\n semver_repo = Repo(semver_path)\\n index = semver_repo.index\\n index.add([filename])\\n semver_user_name = settings['semver_user_name']\\n semver_user_email = settings['semver_user_email']\\n author = Actor(semver_user_name, semver_user_email)\\n index.commit(f'semver({filename}): {version}', author=author, committer=author, parent_commits=None)\",\n \"def test_heads_contains_false(repository: Repository) -> None:\\n assert \\\"branch\\\" not in repository.heads\",\n \"def commit(self):\\n raise multisearch.errors.FeatureNotAvailableError\",\n \"def stable():\\n env.branch = 'stable'\",\n \"def _update_head(self, index_entry, branch, new_id):\\r\\n index_entry['versions'][branch] = new_id\\r\\n self.db_connection.update_course_index(index_entry)\",\n \"def check_force_generate(self, force):\\n if self.exists() and not force:\\n self.log.warn(\\n '{} already exists, skipping key generation...'.format(self.key_file)\\n )\\n return False\\n else:\\n return True\",\n \"def update_index(self, ref_gen):\\n testing = True\\n logging.warning('Updating index')\\n es_insert.index(es, ref_gen, self.index_name, testing, action=\\\"update\\\")\\n logging.warning('Finished updating')\",\n \"def _commit_to_index( env_dict ):\\n from indexer.solr import adapter as adapter_file\\n\\n adapter = adapter_file.adapter(env_dict)\\n adapter.commit()\\n if env_dict[\\\"indexer\\\"][\\\"optimise\\\"]:\\n adapter.optimise(maxSegments=1)\",\n \"def release(store, name, constraints, branches):\\n session = Session(store)\\n\\n # Branches is a list of (name, n_buckets, settings) tuples\\n all_buckets = [session.get(Bucket, x, default=CREATE) for x in range(NUM_BUCKETS)]\\n\\n edited_settings = set.union(*[set(x[2].keys()) for x in branches])\\n\\n conflicting_experiments = set()\\n valid_bucket_indices = []\\n\\n for idx, bucket in enumerate(all_buckets):\\n if is_valid_bucket(bucket, edited_settings, constraints):\\n valid_bucket_indices.append(idx)\\n else:\\n for entry in bucket.entries:\\n # Determine if this entry is a potential conflict\\n if set(entry.settings.keys()).isdisjoint(edited_settings):\\n continue\\n conflicting_experiment_id, _ = entry.key\\n conflicting_experiments.add(conflicting_experiment_id)\\n\\n random.shuffle(valid_bucket_indices)\\n\\n for branch_name, n_buckets, settings in branches:\\n key = [name, branch_name]\\n bucket_indices = valid_bucket_indices[:n_buckets]\\n\\n if len(bucket_indices) < n_buckets:\\n raise NotEnoughBucketsException(conflicts=conflicting_experiments)\\n\\n valid_bucket_indices = valid_bucket_indices[n_buckets:]\\n\\n for bucket_idx in bucket_indices:\\n bucket = all_buckets[bucket_idx]\\n\\n bucket.add(key, settings, constraints)\\n\\n session.flush()\",\n \"def check_commit_for_branch( self\\n , commit\\n , branch_id\\n , any_locked_files\\n , case_conflict_checker ):\\n rev = commit['sha1']\\n if LOG.isEnabledFor(logging.DEBUG):\\n LOG.debug(\\\"check_commit_for_branch() \\\"\\n \\\"Checking branch={} mark={} sha1={} file-ct={} -- {}\\\"\\n .format( branch_id\\n , commit['mark']\\n , p4gf_util.abbrev(rev)\\n , len(commit['files'])\\n , repr(commit['data'])[:20].splitlines()[0]))\\n\\n if self._already_copied_commit(rev, branch_id):\\n return\\n\\n # following checks assume client has been set for branch\\n self.ensure_branch_preflight(commit, branch_id)\\n with self.ctx.switched_to_branch(\\n self._current_branch\\n , set_client=self.set_client_on_branch_switch\\n ):\\n if case_conflict_checker:\\n case_conflict_checker.read_fast_export_commit(\\n commit, self._current_branch)\\n\\n # Empty commits require root-level .p4gf_placeholder to be mapped\\n # in the current branch view.\\n if not commit['files'] and not self._is_placeholder_mapped():\\n raise PreflightException(\\n _(\\\"Empty commit {sha1} not permitted. Git Fusion branch views\\\"\\n \\\" must include root to permit empty commits.\\\")\\n .format(sha1=p4gf_util.abbrev(rev)))\\n\\n with Timer(CHECK_PROTECTS):\\n self._check_protects(commit['author_p4user'], commit['files'])\\n\\n with Timer(CHECK_OVERLAP):\\n self._check_overlap(commit)\\n\\n # fetch the branch setting only, without cascading to repo/global config\\n if self._current_branch.is_read_only:\\n raise PreflightException(_(\\\"Push to branch {branch} prohibited.\\\")\\n .format(branch=self._current_branch.git_branch_name))\\n self._check_stream_writable(commit)\\n self._check_stream_in_classic(commit)\\n\\n LOG.debug('checking locked files under //{}/...'.format(self.ctx.p4.client))\\n if any_locked_files:\\n # Convert the git commit paths to depotPaths\\n files_in_commit = [self.ctx.gwt_path(f['path']).to_depot()\\n for f in commit['files']]\\n LOG.debug(\\\"files_in_commit {0}\\\".format(files_in_commit))\\n for f in files_in_commit:\\n if f in any_locked_files:\\n # Collect the names (and clients) of users with locked files.\\n # Report back to the pusher so they can take appropriate action.\\n msg = _('{file} - locked by {user}').format(file=f,\\n user=any_locked_files[f])\\n LOG.info(msg)\\n raise PreflightException(msg)\\n\\n # +++ Spend time extracting Jobs and P4Changelist owner\\n # here if we actually do need to call\\n # the preflight-commit hook.\\n if self.ctx.preflight_hook.is_callable():\\n jobs = G2PJob.extract_jobs(commit['data'])\\n jobs2 = G2PJob.lookup_jobs(self.ctx, jobs)\\n self.ctx.preflight_hook(\\n ctx = self.ctx\\n , fe_commit = commit\\n , branch_id = branch_id\\n , jobs = jobs2\\n )\",\n \"def test_cherrypickhead_progress(repository: Repository, path: Path) -> None:\\n createconflict(repository, path, ours=\\\"a\\\", theirs=\\\"b\\\")\\n\\n assert repository.cherrypickhead == repository.heads[\\\"update\\\"]\",\n \"def on_same_branch(left, right):\\n left = LooseVersion(left)\\n right = LooseVersion(right)\\n return left.version[:2] == right.version[:2]\",\n \"def test_works_index_name(self):\\n assert \\\"test_index-v4\\\" == self.search.works_index_name(self._db)\",\n \"def package_version_check(args, parser):\\n if (args.build or args.check) and args.package_version:\\n parser.error('--package-version works only with --create')\",\n \"def test_force_f(self, db, course_dir):\\n run_nbgrader([\\\"db\\\", \\\"assignment\\\", \\\"add\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n run_nbgrader([\\\"db\\\", \\\"student\\\", \\\"add\\\", \\\"foo\\\", \\\"--db\\\", db])\\n\\n self._copy_file(join(\\\"files\\\", \\\"submitted-unchanged.ipynb\\\"), join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"p1.ipynb\\\"))\\n self._make_file(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"foo.txt\\\"), \\\"foo\\\")\\n self._make_file(join(course_dir, \\\"source\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"), \\\"bar\\\")\\n run_nbgrader([\\\"generate_assignment\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n self._copy_file(join(\\\"files\\\", \\\"submitted-unchanged.ipynb\\\"), join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.ipynb\\\"))\\n self._make_file(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"), \\\"foo\\\")\\n self._make_file(join(course_dir, \\\"submitted\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"), \\\"bar\\\")\\n run_nbgrader([\\\"autograde\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n self._make_file(join(course_dir, \\\"autograded\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"), \\\"asdf\\\")\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.html\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"))\\n\\n # check that it skips the existing directory\\n remove(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db])\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n\\n # force overwrite the supplemental files\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db, \\\"-f\\\"])\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n\\n # force overwrite\\n remove(join(course_dir, \\\"autograded\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n run_nbgrader([\\\"generate_feedback\\\", \\\"ps1\\\", \\\"--db\\\", db, \\\"--force\\\"])\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"p1.html\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"foo.txt\\\"))\\n assert isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"data\\\", \\\"bar.txt\\\"))\\n assert not isfile(join(course_dir, \\\"feedback\\\", \\\"foo\\\", \\\"ps1\\\", \\\"blah.pyc\\\"))\",\n \"def index(args):\\n for level in ['family', 'language', 'dialect']:\\n if args.args[0] in [level, 'all']:\\n make_index(level)\",\n \"def test_finder_only_installs_stable_releases(data: TestData) -> None:\\n\\n req = install_req_from_line(\\\"bar\\\")\\n\\n # using a local index (that has pre & dev releases)\\n finder = make_test_finder(index_urls=[data.index_url(\\\"pre\\\")])\\n found = finder.find_requirement(req, False)\\n assert found is not None\\n assert found.link.url.endswith(\\\"bar-1.0.tar.gz\\\"), found.link.url\\n\\n # using find-links\\n links = [\\\"https://foo/bar-1.0.tar.gz\\\", \\\"https://foo/bar-2.0b1.tar.gz\\\"]\\n\\n finder = make_test_finder(links)\\n found = finder.find_requirement(req, False)\\n assert found is not None\\n assert found.link.url == \\\"https://foo/bar-1.0.tar.gz\\\"\\n\\n links.reverse()\\n\\n finder = make_test_finder(links)\\n found = finder.find_requirement(req, False)\\n assert found is not None\\n assert found.link.url == \\\"https://foo/bar-1.0.tar.gz\\\"\",\n \"def updateDepTree(self,checked):\\n\\n if( self.name in checked ):\\n return\\n else:\\n checked.append(self.name)\\n\\n for mod in self.parent.modules:\\n if( mod.name != self.name ):\\n mods = mod.reqmodules + mod.optmodules + mod.reqmodules_buildonly\\n if( self.name in mods ):\\n if( mod.mode != \\\"install\\\" or not mod.rebuild ):\\n #if( mod.mode != \\\"install\\\" and not mod.rebuild ):\\n if( mod.useLink ):\\n print \\\"***\\\\t * WARNING: \\\" + mod.name + \\\" is in \\\\\\\"link\\\\\\\" mode, \\\" \\\\\\n + \\\"if you want to rebuild it with the new dependencies set it to \\\\\\\"use\\\\\\\" mode...!!\\\"\\n else:\\n if( not self.parent.noAutomaticRebuilds ):\\n if( mod.mode != \\\"install\\\" ):\\n print \\\"***\\\\t * \\\" + mod.name + \\\" changed to \\\\\\\"install\\\\\\\" mode and rebuild Flag set to true!!\\\"\\n mod.mode = \\\"install\\\"\\n mod.rebuild = True\\n mod.preCheckDeps()\\n mod.updateDepTree(checked)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5562095","0.5386104","0.5354058","0.53346205","0.5217734","0.5204411","0.51927465","0.5189931","0.5149217","0.5106516","0.5092081","0.5082172","0.50748366","0.5031404","0.49861875","0.4984854","0.4967","0.49571994","0.49304572","0.49161586","0.48917702","0.4843911","0.48331442","0.48251724","0.48174083","0.48174083","0.48174083","0.48121828","0.48057586","0.4803555","0.47952485","0.47927728","0.47880954","0.47815922","0.47795552","0.47749045","0.47718036","0.47441894","0.47377223","0.4721839","0.4714696","0.47076532","0.469938","0.46976325","0.46933818","0.4691282","0.46815306","0.46736583","0.4673401","0.4668706","0.46625075","0.4659269","0.4657411","0.46567935","0.46557826","0.4650685","0.46492594","0.46462268","0.46381512","0.4637841","0.46177134","0.4611174","0.4607962","0.4592112","0.45874578","0.45814526","0.45694822","0.45686644","0.45551607","0.45551607","0.45408136","0.45388326","0.45388186","0.45387086","0.4538085","0.45365527","0.45355698","0.45346743","0.45297238","0.45259005","0.45250863","0.45213246","0.4518177","0.45177832","0.4514564","0.45132154","0.45132136","0.45128414","0.45044494","0.4491442","0.44868937","0.44842285","0.44833708","0.4482836","0.44817635","0.44758776","0.44722173","0.4470674","0.44668108","0.44659254","0.44619238"],"string":"[\n \"0.5562095\",\n \"0.5386104\",\n \"0.5354058\",\n \"0.53346205\",\n \"0.5217734\",\n \"0.5204411\",\n \"0.51927465\",\n \"0.5189931\",\n \"0.5149217\",\n \"0.5106516\",\n \"0.5092081\",\n \"0.5082172\",\n \"0.50748366\",\n \"0.5031404\",\n \"0.49861875\",\n \"0.4984854\",\n \"0.4967\",\n \"0.49571994\",\n \"0.49304572\",\n \"0.49161586\",\n \"0.48917702\",\n \"0.4843911\",\n \"0.48331442\",\n \"0.48251724\",\n \"0.48174083\",\n \"0.48174083\",\n \"0.48174083\",\n \"0.48121828\",\n \"0.48057586\",\n \"0.4803555\",\n \"0.47952485\",\n \"0.47927728\",\n \"0.47880954\",\n \"0.47815922\",\n \"0.47795552\",\n \"0.47749045\",\n \"0.47718036\",\n \"0.47441894\",\n \"0.47377223\",\n \"0.4721839\",\n \"0.4714696\",\n \"0.47076532\",\n \"0.469938\",\n \"0.46976325\",\n \"0.46933818\",\n \"0.4691282\",\n \"0.46815306\",\n \"0.46736583\",\n \"0.4673401\",\n \"0.4668706\",\n \"0.46625075\",\n \"0.4659269\",\n \"0.4657411\",\n \"0.46567935\",\n \"0.46557826\",\n \"0.4650685\",\n \"0.46492594\",\n \"0.46462268\",\n \"0.46381512\",\n \"0.4637841\",\n \"0.46177134\",\n \"0.4611174\",\n \"0.4607962\",\n \"0.4592112\",\n \"0.45874578\",\n \"0.45814526\",\n \"0.45694822\",\n \"0.45686644\",\n \"0.45551607\",\n \"0.45551607\",\n \"0.45408136\",\n \"0.45388326\",\n \"0.45388186\",\n \"0.45387086\",\n \"0.4538085\",\n \"0.45365527\",\n \"0.45355698\",\n \"0.45346743\",\n \"0.45297238\",\n \"0.45259005\",\n \"0.45250863\",\n \"0.45213246\",\n \"0.4518177\",\n \"0.45177832\",\n \"0.4514564\",\n \"0.45132154\",\n \"0.45132136\",\n \"0.45128414\",\n \"0.45044494\",\n \"0.4491442\",\n \"0.44868937\",\n \"0.44842285\",\n \"0.44833708\",\n \"0.4482836\",\n \"0.44817635\",\n \"0.44758776\",\n \"0.44722173\",\n \"0.4470674\",\n \"0.44668108\",\n \"0.44659254\",\n \"0.44619238\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94595,"cells":{"query":{"kind":"string","value":"Lists all the blobs in the bucket."},"document":{"kind":"string","value":"def gcs_list_blobs(bucket_name):\n storage_client = client #storage.client()\n bucket = storage_client.get_bucket(bucket_name)\n\n blobs = bucket.list_blobs()\n\n blob_list = []\n for blob in blobs:\n blob_list.append(blob.name)\n return blob_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def list_blobs(bucket_name):\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n return blobs","def list_blobs(bucket_name):\n # bucket_name = \"your-bucket-name\"\n\n storage_client = storage.Client()\n print(storage_client.current_batch)\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n # print(len([1 for blob in blobs]))\n for blob in blobs:\n print(blob.name)","def list_blobs(bucket):\n bucket = default_bucket if bucket is None else bucket\n bucket_name = bucket if isinstance(bucket, str) else bucket.name\n blobs = gcs.list_blobs(bucket_name)\n return blobs","def list_blobs(bucket_name):\n # bucket_name = \"your-bucket-name\"\n\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n return [blob.name for blob in blobs]","def list_blobs(bucket_name):\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n\n blobs = bucket.list_blobs()\n list_blobs = [blob.public_url for blob in blobs]\n return list_blobs","def list_blobs(self, prefix=''):\n return [b.name for b in self.bucket.list_blobs(prefix=prefix)]","def get_list_of_blobs(bucket_name, prefix=None, delimiter=None):\r\n\r\n # initialize client\r\n storage_client = storage.Client()\r\n\r\n # get list blobs\r\n blobs = storage_client.list_blobs(bucket_name, prefix=prefix, delimiter=delimiter)\r\n\r\n for blob in blobs:\r\n print(blob.name)\r\n\r\n if delimiter:\r\n print(\"Prefixes:\")\r\n for prefix in blobs.prefixes:\r\n print(prefix)\r\n\r\n return None","def list_bucket(project: str, bucket: str, prefix: str = None) -> typing.List[typing.Any]:\n client = google.cloud.storage.Client(project)\n return list(client.list_blobs(bucket_or_name=bucket, prefix=prefix))","def list_bucket(self, bucket):\n\n self.response.write(\"Listbucket result:\\n\")\n\n # Production apps should set page_size to a practical value.\n page_size = 1\n stats = cloudstorage.listbucket(bucket + \"/foo\", max_keys=page_size)\n while True:\n count = 0\n for stat in stats:\n count += 1\n self.response.write(repr(stat))\n self.response.write(\"\\n\")\n\n if count != page_size or count == 0:\n break\n stats = cloudstorage.listbucket(\n bucket + \"/foo\", max_keys=page_size, marker=stat.filename\n )","def list_bucket(self, bucket):\n self.response.write('Listbucket result:\\n')\n\n page_size = 1\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size)\n while True:\n count = 0\n for stat in stats:\n count += 1\n self.response.write(repr(stat))\n self.response.write('\\n')\n\n if count != page_size or count == 0:\n break\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size,\n marker=stat.filename)","def list_blobs():\n # bucket_name = \"your-bucket-name\"\n storage_client = storage.Client()\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n numFiles = 0\n for blob in blobs:\n numFiles += 1\n return numFiles","def list_buckets():\n for bucket in s3.buckets.all():\n print(bucket)","def list_buckets():\n for bucket in BUCKET_MANAGER.all_buckets():\n print(bucket)","async def fetch_file_list(client, bucket) -> List:\n # pylint: disable=invalid-name\n PG_HOSTNAME = config('PG_HOSTNAME')\n PG_DATABASE = config('PG_DATABASE')\n folder = f'backup/{PG_HOSTNAME}_{PG_DATABASE}'\n result = await client.list_objects_v2(Bucket=bucket, Prefix=folder)\n contents = result.get('Contents', None)\n file_list = list([])\n if contents:\n for content in contents:\n file_list.append(content.get('Key'))\n return file_list","def blobs(self):\n def blob_iterator():\n with s3conn(self.access_id, self.secret) as s3:\n key_iter = s3.list_bucket(self.bucket, prefix=self.prefix+\"/\")\n for key in key_iter:\n blob = key[len(self.prefix)+1:]\n yield blob\n return blob_iterator","def list_bucket(self, bucket_id=None):\n url = self.prism_endpoint + \"/wBuckets\"\n\n if bucket_id is not None:\n url = url + \"/\" + bucket_id\n\n headers = {\"Authorization\": \"Bearer \" + self.bearer_token}\n\n r = requests.get(url, headers=headers)\n\n if r.status_code == 200:\n logging.info(\"Successfully obtained information about your buckets\")\n return r.json()\n else:\n logging.warning(\"HTTP Error {}\".format(r.status_code))","def get_all_files(self, **keyword_args):\n blobs = self.storage_client.list_blobs(\n self.bucket.name, **keyword_args)\n return blobs","def list_bucket(self, bucket):\n self.response.write('Creating more files for listbucket...\\n')\n self.create_file(bucket + '/foo1')\n self.create_file(bucket + '/foo2')\n self.response.write('\\nListbucket result:\\n')\n\n page_size = 1\n stats = gcs.listbucket(bucket, max_keys=page_size)\n while True:\n count = 0\n for stat in stats:\n count += 1\n self.response.write(repr(stat))\n self.response.write('\\n')\n\n if count != page_size or count == 0:\n break\n last_filename = stat.filename[len(bucket) + 1:]\n stats = gcs.listbucket(bucket, max_keys=page_size, marker=last_filename)","def ls(region_name=DEFAULT_REGION):\n s3conn = s3.connect_to_region(region_name)\n buckets = s3conn.get_all_buckets()\n for bucket in buckets:\n print(bucket.name)","def list_objects(bucket=None):\n hook = GoogleCloudStorageHook()\n storage_objects = hook.list(bucket)\n\n return storage_objects","def list_buckets():\n response = s3.list_buckets()\n # Output the bucket names\n print('Existing buckets:')\n for bucket in response['Buckets']:\n print(bucket[\"Name\"])","def list_bucket_objects(bucket):\n for obj in BUCKET_MANAGER.all_objects(bucket).all():\n print(obj)","def list(self, glob_pattern=\"\"):\r\n # strip relative path so we don't step outside our emulated storage area\r\n glob_pattern = force_local_path(glob_pattern)\r\n\r\n # analyze glob_pattern to determine how to return blob names\r\n\r\n # if glob_pattern is a folder\r\n if not glob_pattern:\r\n # default to all blobs at the root level\r\n glob_pattern = \"*\"\r\n elif is_folder(f\"{self._blob_folder()}/{glob_pattern}\"):\r\n # if glob_pattern is a folder, return all blobs within folder\r\n glob_pattern = f\"{force_trailing_slash(glob_pattern)}*\"\r\n else:\r\n # use glob_pattern as-is\r\n pass\r\n\r\n # retrieve sorted blob names\r\n target_path = f\"{self._blob_folder()}/{glob_pattern}\"\r\n\r\n # build list of blob names with local parent path stripped from names\r\n blob_names = list()\r\n for blob_name in sorted(glob.glob(target_path)):\r\n # format name using Linux path delimiters\r\n blob_name = blob_name.replace(chr(92), \"/\")\r\n blob_name = blob_name.replace(f\"{self._blob_folder()}/\", \"\")\r\n blob_names.append(blob_name)\r\n\r\n blob_count = len(blob_names)\r\n logger.debug(\r\n self._context(f\"list({glob_pattern}) returned {blob_count} blob names\")\r\n )\r\n logger.debug(self._context(f\"list({glob_pattern}) = {blob_names}\"))\r\n return blob_names","def get_list_of_buckets():\r\n\r\n # initialize client\r\n storage_client = storage.Client()\r\n\r\n # get list of buckets\r\n buckets = storage_client.list_buckets()\r\n\r\n list_of_buckets = []\r\n for bucket in buckets:\r\n list_of_buckets.append(bucket.name)\r\n\r\n return list_of_buckets","def blobs(self):\n if not self._blobs:\n workspace = self.attributes.workspace\n # Instantiates a google client, & get all blobs in bucket\n storage_client = storage.Client(project=self._user_project)\n bucket = storage_client.bucket(workspace['bucketName'], user_project=self._user_project)\n # get subset of data\n _blobs = {}\n try:\n for b in bucket.list_blobs(fields='items(size, etag, crc32c, name, timeCreated),nextPageToken'):\n name = f\"gs://{workspace['bucketName']}/{b.name}\"\n # cache.put(name, {'size': b.size, 'etag': b.etag, 'crc32c': b.crc32c, 'time_created': b.time_created, 'name': name})\n _blobs[name] = AttrDict({'size': b.size, 'etag': b.etag, 'crc32c': b.crc32c, 'time_created': b.time_created, 'name': name})\n self._blobs = _blobs\n except Exception as e:\n print(f\"{self.id} {workspace['bucketName']} {e}\")\n self._blobs = _blobs\n return self._blobs","def list_buckets(self):\n return [x[\"Name\"] for x in self.s3_client.list_buckets()[\"Buckets\"]]","def listBucket(self, clientCall):\n\t\ttry:\n\t\t\tresponse \t= clientCall.list_objects(Bucket=self.bucket)\n\t\t\tresponse \t= self.parseJson(\"Contents[].Key\", response)\n\n\t\t\tfor objects in response:\n\t\t\t\twrite(var=f\"{g}#\", color=w, data=f\"{objects}\")\n\t\t\t\tsleep(0.01)\n\n\t\t\treturn(response)\n\n\t\texcept botocore.exceptions.ClientError as e:\n\t\t\tif \"AccessDenied\" and \"ListObjects\" in e.args[0]:\n\t\t\t\treturn(None)","def listAllBuckets(self):\n print self.getAllBuckets()","def list_objects(self, bucket_name, prefix=None):\n url = self.endpoint + '/rest/v2/caches/' + self.cache_name + '?action=keys'\n res = self.infinispan_client.get(url, auth=self.basicAuth)\n data = res.content\n return data","def list_files(bucket):\r\n s3 = boto3.client('s3')\r\n contents = []\r\n for item in s3.list_objects(Bucket=bucket)['Contents']:\r\n contents.append(item)\r\n\r\n return contents","def list_objects(self, bucket_name, prefix='', offset=0, count=10000):\n\n objects, done = h3lib.list_objects(self._handle, bucket_name, prefix, offset, count, self._user_id)\n return H3List(objects, done=done)","def get_list_of_files(bucket_name, path):\n client = get_client()\n bucket = client.get_bucket(bucket_name)\n blob = bucket.blob(filename)\n return blob","async def la() -> Tuple[str]:\n li = []\n async with _create_client() as client:\n for bucket in (await client.list_buckets())['Buckets']:\n _ = await client.list_objects(Bucket=bucket['Name'])\n for item in _['Contents']:\n if item['Key'].endswith('/') is False:\n li.append(bucket['Name'] + '/' + item['Key'])\n logger.info('List all objects in all buckets.')\n return tuple(li)","def get_bucketlist():\n pass","def test_list(self):\n responses.add(\n responses.Response(\n method='GET',\n url='https://connection.keboola.com/v2/storage/buckets',\n json=list_response\n )\n )\n buckets_list = self.buckets.list()\n assert isinstance(buckets_list, list)","def list_buckets():\n buckets = gcs.list_buckets()\n names = []\n for bucket in buckets:\n names.append(bucket.name)\n return names","def list_buckets(self):\n response = self._client.list_buckets()\n return [item['name'] for item in response['Buckets']]","def list(self, prefix='', delimiter='', marker='', headers=None):\r\n return BucketListResultSet(self, prefix, delimiter, marker, headers)","def blob_stats(self):\n def blob_iterator():\n with s3conn(self.access_id, self.secret) as s3:\n key_iter = s3.list_bucket2(self.bucket, prefix=self.prefix+\"/\")\n for head in key_iter:\n blob = head[LIST_BUCKET_KEY][len(self.prefix)+1:]\n head['blob'] = blob\n yield head\n return blob_iterator","def list_files_in_s3_bucket(bucket_name):\n s3 = boto3.client(\"s3\")\n bucket = s3.list_objects(Bucket=bucket_name)\n contents = [c[\"Key\"] for c in bucket[\"Contents\"]]\n return contents","def list_s3_content(bucket, access_id, access_secret, prefix=\"\"):\n try:\n response = s3_client(access_id, access_secret).list_objects(Bucket=bucket, Prefix=prefix)\n objects = []\n if response.get(\"Contents\"):\n for key in response['Contents']:\n objects.append(key['Key'])\n return objects\n except ClientError as error:\n LOGGER.error(error)\n return None","def get_objects(self):\r\n bucket = self._get_bucket()\r\n objs = []\r\n for key in bucket:\r\n objs.append(key)\r\n return objs","def iterate_bucket_objects(self, bucket):\n client = self.credentials.session.client('s3')\n page_iterator = client.list_objects_v2(Bucket=bucket)\n if 'Contents' not in page_iterator:\n return []\n for item in page_iterator['Contents']:\n yield item","def get_files_in_bucket(self, bucket_name):\n s3 = self.credentials.session.resource('s3')\n this_bucket = s3.Bucket(bucket_name)\n list_of_files = [s3file.key for s3file in this_bucket.objects.all()];\n return list_of_files","def list_files_gcp(client: storage.client.Client, bucket_name: str) -> List[str]:\n blobs = client.list_blobs(bucket_name)\n files = [blob.name.split('.')[0] for blob in blobs]\n return files","def list_all_buckets(riak_host,riak_port):\n url='http://%s:%s/buckets?buckets=true' % (riak_host,riak_port)\n r=requests.get(url)\n print json.dumps(r.json(), sort_keys=True, indent=4)","def list_public_s3_objects(self):\n _response = urllib.urlopen(self.options.s3_bucket_url)\n _html = _response.read()\n\n if LOCAL_DEBUG:\n print _html\n\n try:\n assert \"AccessDenied\" not in _html\n assert \"NoSuchBucket\" not in _html\n except:\n print(\"ERROR: AccessDenied or NoSuchBucket for {}\".format(self.options.s3_bucket_url))\n raise","def list_objects(self, bucket, prefix=None, include_metadata=False) -> list:\n objects = []\n kwargs = {'Bucket': bucket}\n if prefix:\n kwargs['Prefix'] = prefix\n\n is_truncated = True\n while is_truncated:\n response = self.client.list_objects_v2(**kwargs)\n if not response.get('Contents'):\n return objects\n\n for item in response['Contents']:\n if include_metadata:\n objects.append(item)\n else:\n objects.append(item['Key'])\n\n is_truncated = response['IsTruncated']\n kwargs['ContinuationToken'] = response.get('NextContinuationToken')\n\n return objects","def read_bucket_objects(bucket_name):\n s3 = boto3.resource('s3')\n bucket = s3.Bucket(bucket_name)\n for obj in bucket.objects.all():\n response = obj.get()\n body = response['Body'].read().decode('utf-8')\n print(body)","def ListFiles(s3):\n response = s3.list_objects(Bucket='mynewbucket123')\n for content in response.get('Contents', []):\n yield content.get('Key')","def list_buckets(self):\n msg = \"list_buckets not implemented\"\n raise NotImplementedError(msg)","def test_list_bucket_content(self):\n conn = boto3.resource('s3', region_name='us-east-1')\n # We need to create the bucket since this is all in Moto's 'virtual' AWS account\n conn.create_bucket(Bucket='foobucket')\n # Upload fake file to bucket\n s3 = boto3.client('s3')\n with open('test/test_resources/test_file', 'rb') as data:\n s3.upload_fileobj(data, 'foobucket', 'foofile')\n\n s3_connector = S3Connector()\n s3_connector.connect(\"default\")\n self.assertEqual(s3_connector.list_bucket_content(\n \"foobucket\"), [\"foofile\"])","async def ls(path: str = None) -> Tuple[str]:\n async with _create_client() as client:\n if path is None:\n resp = await client.list_buckets()\n logger.info('List all buckets.')\n return tuple([item['Name'] for item in resp['Buckets']])\n else:\n _ = path.strip('/').split('/')\n kws = {'Bucket': _[0]}\n if len(_) > 1:\n kws.update({'Prefix': '/'.join(_[1:])})\n try:\n resp = await client.list_objects(**kws)\n logger.info(f'List objects in \"{path}\".')\n return tuple([\n f'/{_[0]}/' + item['Key'] for item in resp['Contents']\n ]) if 'Contents' in resp.keys() else ()\n except ClientError:\n return ()","def getBlobs( self ):\n return self.__blobs;","def ListCommand(self, args, sub_opts=None, headers=None, debug=0):\n listing_style = ListingStyle.SHORT\n get_bucket_info = False\n if sub_opts:\n for o, unused_a in sub_opts:\n if o == '-b':\n get_bucket_info = True\n if o == '-l':\n listing_style = ListingStyle.LONG\n if o == '-L':\n listing_style = ListingStyle.LONG_LONG\n if not args:\n # default to listing all gs buckets\n args = ['gs://']\n\n total_objs = 0\n total_bytes = 0\n for uri_str in args:\n uri = self.StorageUri(uri_str, debug=debug, validate=False)\n\n if not uri.bucket_name:\n # Provider URI: add bucket wildcard to list buckets.\n for uri in self.CmdWildcardIterator('%s://*' % uri.scheme,\n headers=headers, debug=debug):\n (bucket_objs, bucket_bytes) = self.PrintBucketInfo(uri, listing_style,\n headers=headers,\n debug=debug)\n total_bytes += bucket_bytes\n total_objs += bucket_objs\n\n elif not uri.object_name:\n if get_bucket_info:\n # ls -b request on provider+bucket URI: List info about bucket(s).\n for uri in self.CmdWildcardIterator(uri, headers=headers,\n debug=debug):\n (bucket_objs, bucket_bytes) = self.PrintBucketInfo(uri,\n listing_style,\n headers=headers,\n debug=debug)\n total_bytes += bucket_bytes\n total_objs += bucket_objs\n else:\n # ls request on provider+bucket URI: List objects in the bucket(s).\n for obj in self.CmdWildcardIterator(uri.clone_replace_name('*'),\n ResultType.KEYS,\n headers=headers, debug=debug):\n total_bytes += self.PrintObjectInfo(uri, obj, listing_style,\n headers=headers, debug=debug)\n total_objs += 1\n\n else:\n # Provider+bucket+object URI -> list the object(s).\n for obj in self.CmdWildcardIterator(uri, ResultType.KEYS,\n headers=headers, debug=debug):\n total_bytes += self.PrintObjectInfo(uri, obj, listing_style,\n headers=headers, debug=debug)\n total_objs += 1\n if listing_style != ListingStyle.SHORT:\n print ('TOTAL: %d objects, %d bytes (%s)' %\n (total_objs, total_bytes, MakeHumanReadable(float(total_bytes))))","def find_google_cloud_storage_file_names(bucket, prefix=''):\n return list(bucket.list_blobs(prefix=prefix))","def list_keys(self, bucket_name, prefix=None):\n url = self.endpoint + '/rest/v2/caches/' + self.cache_name + '?action=keys'\n res = self.infinispan_client.get(url, auth=self.basicAuth)\n data = res.content\n return data","def list_objects(Bucket=None, Delimiter=None, EncodingType=None, Marker=None, MaxKeys=None, Prefix=None, RequestPayer=None):\n pass","def list_bucket_files(project, bucket_name, referenced_files, verbose):\n if verbose:\n print(\"listing contents of bucket gs://\" + bucket_name)\n\n # set up storage client\n storage_client = storage.Client(project=project)\n\n # check if bucket exists\n try:\n bucket = storage_client.get_bucket(bucket_name)\n except:\n print(f'Bucket {bucket_name} does not exist!')\n exit(1)\n\n # Note: Client.list_bucket_files requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n if verbose:\n print(\"finished listing bucket contents. processing files now in chunks of 1000.\")\n\n bucket_dict = dict()\n\n def extract_file_metadata(blob):\n blob_name = blob.name\n\n if blob_name.endswith('/'): # if this is a directory\n return None\n\n full_file_path = \"gs://\" + bucket_name + \"/\" + blob_name\n # support new submissions directory structure in Terra bucket\n submissions_dir = \"submissions\"\n if full_file_path.split('/', 4)[3] == submissions_dir:\n # new format is gs://bucket_id/submissions/submission_id/remaining_path\n submission_id = full_file_path.split('/', 5)[4]\n else:\n # old format is gs://bucket_id/submission_id/remaining_path\n # Splits the bucket file: \"gs://bucket_Id/submission_id/file_path\", by the '/' symbol\n # and stores values in a 5 length array: ['gs:', '' , 'bucket_Id', submission_id, file_path]\n # to extract the submission id from the 4th element (index 3) of the array\n submission_id = full_file_path.split('/', 4)[3]\n\n file_metadata = {\n \"file_name\": blob_name.split('/')[-1],\n \"file_path\": full_file_path,\n \"submission_id\": submission_id,\n \"size\": blob.size,\n \"is_in_data_table\": full_file_path in referenced_files,\n \"time_created\": blob.time_created\n }\n\n return file_metadata\n\n n_blobs = 0\n for page in blobs.pages: # iterating through pages is way faster than not\n if verbose:\n n_blobs += page.remaining\n print(f'...processing {n_blobs} blobs', end='\\r')\n for blob in page:\n file_metadata = extract_file_metadata(blob)\n if file_metadata:\n full_file_path = file_metadata['file_path']\n bucket_dict[full_file_path] = file_metadata\n\n if verbose:\n print(f'Found {len(bucket_dict)} files in bucket {bucket_name}')\n\n return bucket_dict","def list_s3(bucket, prefix, ext):\n s3 = boto3.resource('s3')\n s3_bucket = s3.Bucket(bucket)\n\n if ext:\n ext = '.' + ext.lstrip('.')\n else:\n ext = ''\n\n counter = 0\n for item in s3_bucket.objects.filter(Prefix=prefix):\n counter += 1\n if counter % 5000 == 0:\n print(f'Found {counter} items so far', file=sys.stderr)\n\n key = item.key\n if not key.endswith(ext):\n continue\n\n # Write to stdout\n print(key)","def list_objects(self, s3_prefix_path):\n bucket_name, prefix = S3Util.get_bucket_and_key(s3_prefix_path)\n bucket = self.s3_resource.Bucket(bucket_name)\n return [\"s3://\" + bucket_name + \"/\" + key.key for key in bucket.objects.filter(Prefix=prefix)]","def ListFiles(bucketname, client, key):\n response = client.list_objects(Bucket=bucketname, Prefix=key)\n for content in response.get('Contents', []):\n yield content.get('Key')","def list_files(self, container_name, dir_path):\n blobs = RetryHandler.retry(lambda: self.blob_client.list_blobs(container_name, prefix=dir_path))\n for b in blobs:\n yield b.name","def list_buckets():\n pass","def buckets(self):\n if self._s3client is None:\n raise BucketStorageUnavailableException()\n\n buckets = [Bucket(self, x.name, create=False) for x in self._s3resource.buckets.all()]\n return buckets","def test_list_blobs(*args, **kwargs):\n bucket_or_name = args[0]\n prefix = kwargs['prefix']\n candidate_path = f'{bucket_or_name}/{prefix}'\n config_paths = []\n\n for c in config_hierarchy:\n if c.startswith(candidate_path):\n fn = '/'.join(c.split('/')[1:])\n b = Blob(bucket='dummy', name=fn)\n config_paths.append(b)\n\n return iter(config_paths)","def List(self, prefix=''):\n\n bucket, bucket_path = self._ParseBucketAndPath(prefix)\n names = []\n request = self._service.objects().list(bucket=bucket, prefix=bucket_path)\n response = self._RunWithRetries(request.execute, self._CommonErrorMatcher)\n\n while response:\n if 'items' in response:\n names += [item['name'] for item in response['items']]\n\n if 'nextPageToken' in response:\n request = self._service.objects().list(\n bucket=bucket, prefix=bucket_path,\n pageToken=response['nextPageToken'])\n response = self._RunWithRetries(request.execute,\n self._CommonErrorMatcher)\n else:\n response = None\n\n return names","def get_objects(self, bucket, s3_client=None):\n\n try:\n all_objects = s3_client.list_objects_v2(Bucket=bucket)\n except botocore.exceptions.EndpointConnectionError:\n logging.error(\"Couldn't connect to an S3 endpoint. If you're using an S3 compatible provider other than AWS, remember to set --s3-endpoint-url\")\n sys.exit(1)\n\n if 'Contents' in all_objects.keys():\n all_objects = [ k['Key'] for k in [ obj for obj in all_objects['Contents'] ]] # pylint: disable=unnecessary-comprehension\n else:\n all_objects = []\n\n return all_objects","def ls(self):\n files = self.drive.files().list().execute().get(\"files\", [])\n for f in files:\n print(f[\"name\"], f[\"mimeType\"])","def list_all_objects_s3(bucket, prefix, profile):\n s3 = boto3.Session(profile_name=profile).client('s3')\n keys = []\n continuation_token = \"\"\n\n while True:\n list_kwargs = dict(Bucket=bucket, Prefix=prefix)\n if continuation_token:\n list_kwargs['ContinuationToken'] = continuation_token\n resp = s3.list_objects_v2(**list_kwargs)\n keys += [x['Key'] for x in resp.get('Contents', [])]\n \n if not resp.get('IsTruncated'):\n break\n continuation_token = resp.get('NextContinuationToken')\n \n return keys","def list(self, prefix=\"\"):\n try:\n list_rep = self.client.listdir(self.bucket + \"/\" + prefix)\n for i in list_rep:\n # Remove preceding bucket name and potential leading slash from returned key value\n i = i.replace(self.bucket, \"\").replace('tar', 'wsp.sz')\n if i[0] == '/': i = i[1:]\n yield i\n except pyhdfs.HdfsFileNotFoundException:\n pass","def get_all(user_id):\n return Bucketlist.query.filter_by(created_by=user_id)","def list_hnd(self, request, **kwargs):\n prefix = request.POST.get(\"prefix\", \"\")\n marker = request.POST.get(\"marker\", \"\")\n delimiter = request.POST.get(\"delimiter\", \"\")\n\n max_keys = int(request.POST.get(\"max_keys\", 1000))\n max_keys = max((1, max_keys)) # >= 1\n max_keys = min((1000, max_keys)) # <= 1000\n\n bucket = store.get_bucket()\n\n # prefix \"prefix\" with user dir\n eff_prefix = store.prefix_with_user_dir(request.user, prefix)\n\n # get list iterator from s3\n file_iter = bucket.list(prefix=eff_prefix, delimiter=delimiter,\n marker=marker, headers=None,\n encoding_type=None)\n\n # convert to list, try to get +1 item to be able\n # to determine if the results are truncated\n files = [key.key.split(\"/\", 1)[1]\n for key in itertools.islice(file_iter, 0, max_keys+1)]\n\n # if max_keys is less then there are more results\n # -> truncated = True\n truncated = len(files) > max_keys\n if truncated:\n # return 1 item less\n files = files[:-1]\n\n return self.create_response(request, {\n \"files\": files,\n \"truncated\": truncated\n })","def files_storage_list(self, prefix='pipeline/', print_paths=False):\n\n return self.backend.files_storage_list(prefix=prefix, print_paths=print_paths)","def list_objects_v2(Bucket=None, Delimiter=None, EncodingType=None, MaxKeys=None, Prefix=None, ContinuationToken=None, FetchOwner=None, StartAfter=None, RequestPayer=None):\n pass","def list_blobs(rsf_file, output_format=None, stream=None):\n\n cmds = rsf.read(rsf_file)\n register = Register(cmds)\n\n utils.check_readiness(register)\n blobs = register.log.blobs\n\n if output_format == \"csv\":\n schema = register.schema()\n headers = [attr.uid for attr in schema.attributes]\n xsv.serialise(stream, blobs, headers)\n\n stream.seek(0)\n\n return None\n\n if output_format == \"json\":\n utils.serialise_json({repr(k): v for k, v in blobs.items()}, stream)\n\n stream.seek(0)\n\n return None\n\n return blobs.values()","def list_buckets(schedule_id):\n\n from mist.api.poller.models import ListBucketsPollingSchedule\n sched = ListBucketsPollingSchedule.objects.get(id=schedule_id)\n try:\n sched.cloud.ctl.objectstorage.list_buckets(persist=False)\n except (PeriodicTaskLockTakenError, PeriodicTaskTooRecentLastRun) as exc:\n list_buckets.logger.warning(\n '%s failed with %r',\n sched.name, exc)\n except Exception as exc:\n list_buckets.logger.error(\n '%s failed with %r',\n sched.name, exc)","def list_objects(self, prefix, limit=None, give_size=False, suffix=''):\n objects = []\n total_size = 0\n if not limit or limit > 1000:\n paginator = self.client.get_paginator(\"list_objects\")\n page_iterator = paginator.paginate(Bucket=self.bucket, EncodingType='url', Prefix=prefix)\n for page in page_iterator:\n if \"Contents\" in page:\n for key in page[\"Contents\"]:\n if suffix != '':\n if key[\"Key\"].endswith(suffix):\n objects.append(urllib.parse.unquote(key[\"Key\"]))\n total_size += key['Size']\n else:\n objects.append(key[\"Key\"])\n total_size += key['Size']\n else:\n response = self.client.list_objects_v2(Bucket=self.bucket, Prefix=prefix)\n if \"Contents\" in response:\n for key in response['Contents']:\n if suffix != '':\n if key[\"Key\"].endswith(suffix):\n objects.append(key[\"Key\"])\n total_size += key['Size']\n else:\n objects.append(key[\"Key\"])\n total_size += key['Size']\n if give_size:\n return list(map(lambda key: key.replace('%3D', '='), objects)), total_size\n else:\n return list(map(lambda key: key.replace('%3D', '='), objects))","def get(self):\n\n upload_info = self.request.GET.get('upload_info')\n if upload_info:\n self.response.headers['content-type'] = 'text/plain'\n self.response.out.write(upload_info)\n return\n\n blobs = reversed(blobstore.BlobInfo.all().fetch(10))\n output = template.render('index.html', {'blobs': blobs})\n self.response.out.write(output)","def get_buckets_for_user(self):\n s3 = self.credentials.session.resource('s3')\n bucket_list = [bucket.name for bucket in s3.buckets.all()]\n\n return bucket_list;","def s3_list_files(prefix, as_generator=False):\n return _do_list_files(S3_BUCKET, prefix, as_generator=as_generator)","def get_list_of_keys(self, bucket_name=None, callback_fn=None):\n list_of_keys = []\n if not callback_fn:\n callback_fn = lambda x: print(x.key)\n if bucket_name:\n buckets = [self.s3_.get_bucket(bucket_name)]\n else:\n buckets = [b for b in self.s3_.get_all_buckets() if b.name not in self.BLACKLIST]\n for bucket in buckets:\n for key in bucket.list():\n callback_fn(key)\n list_of_keys.append(key)\n return list_of_keys","def manipulate_bucketlist():\n pass","def list_buckets(args):\n\n args.suppress_verify_output = True\n if verify(args) != 0:\n # restore stdout\n sys.stdout = sys.__stdout__\n print(\"Config file not valid, please use the verify function to debug\")\n return\n\n with open(args.file, \"r\") as f:\n config_json = json.load(f)\n\n for group in config_json[\"groups\"]:\n print(group[\"name\"] + \":\")\n for bucket in group[\"buckets\"]:\n print(\"\\t\" + bucket[\"name\"])","async def glob(self):\n # get the path string up to the wildcards\n try:\n pi1 = self._path.index(\"*\")\n except ValueError:\n pi1 = len(self._path)\n try:\n pi2 = self._path.index(\"?\")\n except ValueError:\n pi2 = len(self._path)\n pi = min(pi1, pi2)\n # using the prefix will cut down on the search space\n prefix = self._path[:pi]\n # get the wildcard\n wildcard = self._path[pi:]\n # set up the paginator\n paginator = self._conn_obj.conn.get_paginator(\"list_objects_v2\")\n parameters = {\n 'Bucket': self._bucket,\n 'Prefix': prefix\n }\n page_iterator = paginator.paginate(**parameters)\n files = []\n async for page in page_iterator:\n for item in page.get('Contents', []):\n fname = item['Key']\n # check that it matches against wildcard\n if fnmatch(fname, wildcard):\n files.append(item['Key'])\n return files","def describe_objects(bucket_name):\n # Upload the file\n try:\n # Retrieve list of files in bucket\n response = s3.list_objects_v2(Bucket=bucket_name)\n files = response.get(\"Contents\")\n # Output file names\n for file in files:\n print(f\"file_name: {file['Key']}, size: {file['Size']}\")\n except ClientError as e:\n logging.error(e)\n return False\n return True","def aws_s3_ls(s3_uri: str, list_extended=False)->list:\n client = boto3.client(\"s3\")\n bucket, prefix = _extract_bucket_key(s3_uri)\n s3_objects = []\n cont_token = None\n while (True):\n if cont_token is None:\n kwargs = {\n \"Bucket\": bucket,\n \"MaxKeys\": 100,\n \"Prefix\": prefix\n }\n else:\n kwargs = {\n \"Bucket\": bucket,\n \"MaxKeys\": 100,\n \"Prefix\": prefix,\n \"ContinuationToken\": cont_token\n } \n try:\n response = client.list_objects_v2(**kwargs)\n if response[\"KeyCount\"] == 0:\n print (\"Requested s3 object doesn't exist.\")\n break\n for record in response[\"Contents\"]:\n if record[\"Size\"] > 0: # ignore just prefix names\n if list_extended:\n s3_objects.append((record[\"Size\"], \n record[\"LastModified\"].strftime(\"%Y%m%d %H:%M:%S.%s\"), \n record[\"Key\"]))\n else:\n s3_objects.append(record[\"Key\"])\n if response[\"IsTruncated\"]:\n cont_token = response[\"NextContinuationToken\"]\n else:\n break\n except Exception as exc:\n raise Error(\"Error {} occurred while listing objects.\".format(exc))\n return s3_objects","def list_buckets(self, s3_client=None):\n\n s3_client = s3_client or self.s3_client\n\n try:\n return ([ bucket['Name'] for bucket in s3_client.list_buckets()['Buckets'] ])\n except botocore.exceptions.EndpointConnectionError:\n logging.error(\"Couldn't connect to an S3 endpoint. If you're using an S3 compatible provider other than AWS, remember to set --s3-endpoint-url\")\n sys.exit(1)\n except botocore.exceptions.ClientError as e:\n print(e)\n sys.exit(1)","def ls(self, s3uri, **kwargs):\n return self.exec_command('ls %s' % (s3uri,), **kwargs)","def get_keys(self):\n bucket = self.resource.Bucket(self.bucketname)\n return [key.key for key in bucket.objects.all()]","def list(self, path, filename=None, start=None, stop=None, recursive=False, directories=False):\n storageScheme, keys = self.getkeys(\n path, filename=filename, directories=directories, recursive=recursive)\n keys = [storageScheme + \":///\" + key.bucket.name + \"/\" + key.name for key in keys]\n keys.sort()\n keys = select(keys, start, stop)\n return keys","def blobs(self):\n blobs = pipeline(\n ftype_selector([FILE]),\n fmap(first),\n fmap(self.reverser),\n )(self.root.entries())\n return blobs","def list_s3_files(bucket, prefix):\n \n s3 = boto3.client('s3')\n\n if type(prefix) != list:\n prefix = [prefix]\n \n # Loop over prefixes:\n file_list = []\n for p in prefix:\n \n # Load one prefix:\n response = s3.list_objects_v2(Bucket=bucket, Prefix=p)\n if response['KeyCount'] > 0:\n file_list = file_list + [d['Key'] for d in response['Contents']]\n while response['IsTruncated']:\n response = s3.list_objects_v2(Bucket=bucket, Prefix=p, StartAfter=file_list[-1])\n file_list = file_list + [d['Key'] for d in response['Contents']] \n \n return file_list","def list(number=0):\n buckets = get_buckets(number)\n data = [(\"Name\", \"Versioned\", \"LifeCycle\")]\n col_width = [0, 0, 0]\n rows = []\n for name, bucket in buckets.items():\n v = bucket[\"versioning\"]\n l = bucket[\"lifecycle\"]\n v = v if v else \"Disabled\"\n if l:\n l = json.dumps(l, indent=1)\n else:\n l = \"None\"\n data.append((name, v, l))\n for row in data:\n for i, info in enumerate(row):\n col_width[i] = min(max(len(info) + 2, col_width[i]), 48)\n dashes = tuple((\"-\" * (width - 1) for width in col_width))\n data.insert(1, dashes)\n click.echo(f\"The status of the buckets:\")\n for row in data:\n output = \"\"\n for i in range(3):\n output += row[i].ljust(col_width[i])\n if not VERBOSE:\n click.echo(output)\n logger.info(output)","def list_buckets(self, regex=\".*\", verbose=False):\n r = re.compile(regex)\n if verbose:\n return [b for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])]\n else:\n return [b['Name'] for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])]","def list_s3(bucket, prefix, ext=None):\n s3 = boto3.resource('s3')\n s3_bucket = s3.Bucket(bucket)\n\n if ext:\n ext = '.' + ext.lstrip('.')\n else:\n ext = ''\n\n for item in s3_bucket.objects.filter(Prefix=prefix):\n key = item.key\n if not key.endswith(ext):\n continue\n\n yield key","def list_all_keys(riak_host,riak_port,bucket):\n url='http://%s:%s/buckets/%s/keys?keys=true' % (riak_host,riak_port,bucket)\n #print url\n r=requests.get(url)\n print json.dumps(r.json(), sort_keys=True, indent=4)","def list(\n self,\n bucket: str,\n prefix: str=None,\n delimiter: str=None,\n ) -> typing.Iterator[str]:\n raise NotImplementedError()","def list_buckets_from_project(projectname):\n response = jsonify(\n admin.list_buckets_on_project_by_name(current_app.scoped_session(), projectname)\n )\n return response","def list_available_tiles(prefix):\n\n files = []\n generator = hls_container_client.list_blobs(name_starts_with=prefix)\n for blob in generator:\n files.append(blob.name)\n return files"],"string":"[\n \"def list_blobs(bucket_name):\\n storage_client = storage.Client()\\n\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n return blobs\",\n \"def list_blobs(bucket_name):\\n # bucket_name = \\\"your-bucket-name\\\"\\n\\n storage_client = storage.Client()\\n print(storage_client.current_batch)\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n # print(len([1 for blob in blobs]))\\n for blob in blobs:\\n print(blob.name)\",\n \"def list_blobs(bucket):\\n bucket = default_bucket if bucket is None else bucket\\n bucket_name = bucket if isinstance(bucket, str) else bucket.name\\n blobs = gcs.list_blobs(bucket_name)\\n return blobs\",\n \"def list_blobs(bucket_name):\\n # bucket_name = \\\"your-bucket-name\\\"\\n\\n storage_client = storage.Client()\\n\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n return [blob.name for blob in blobs]\",\n \"def list_blobs(bucket_name):\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n\\n blobs = bucket.list_blobs()\\n list_blobs = [blob.public_url for blob in blobs]\\n return list_blobs\",\n \"def list_blobs(self, prefix=''):\\n return [b.name for b in self.bucket.list_blobs(prefix=prefix)]\",\n \"def get_list_of_blobs(bucket_name, prefix=None, delimiter=None):\\r\\n\\r\\n # initialize client\\r\\n storage_client = storage.Client()\\r\\n\\r\\n # get list blobs\\r\\n blobs = storage_client.list_blobs(bucket_name, prefix=prefix, delimiter=delimiter)\\r\\n\\r\\n for blob in blobs:\\r\\n print(blob.name)\\r\\n\\r\\n if delimiter:\\r\\n print(\\\"Prefixes:\\\")\\r\\n for prefix in blobs.prefixes:\\r\\n print(prefix)\\r\\n\\r\\n return None\",\n \"def list_bucket(project: str, bucket: str, prefix: str = None) -> typing.List[typing.Any]:\\n client = google.cloud.storage.Client(project)\\n return list(client.list_blobs(bucket_or_name=bucket, prefix=prefix))\",\n \"def list_bucket(self, bucket):\\n\\n self.response.write(\\\"Listbucket result:\\\\n\\\")\\n\\n # Production apps should set page_size to a practical value.\\n page_size = 1\\n stats = cloudstorage.listbucket(bucket + \\\"/foo\\\", max_keys=page_size)\\n while True:\\n count = 0\\n for stat in stats:\\n count += 1\\n self.response.write(repr(stat))\\n self.response.write(\\\"\\\\n\\\")\\n\\n if count != page_size or count == 0:\\n break\\n stats = cloudstorage.listbucket(\\n bucket + \\\"/foo\\\", max_keys=page_size, marker=stat.filename\\n )\",\n \"def list_bucket(self, bucket):\\n self.response.write('Listbucket result:\\\\n')\\n\\n page_size = 1\\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size)\\n while True:\\n count = 0\\n for stat in stats:\\n count += 1\\n self.response.write(repr(stat))\\n self.response.write('\\\\n')\\n\\n if count != page_size or count == 0:\\n break\\n stats = gcs.listbucket(bucket + '/foo', max_keys=page_size,\\n marker=stat.filename)\",\n \"def list_blobs():\\n # bucket_name = \\\"your-bucket-name\\\"\\n storage_client = storage.Client()\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n numFiles = 0\\n for blob in blobs:\\n numFiles += 1\\n return numFiles\",\n \"def list_buckets():\\n for bucket in s3.buckets.all():\\n print(bucket)\",\n \"def list_buckets():\\n for bucket in BUCKET_MANAGER.all_buckets():\\n print(bucket)\",\n \"async def fetch_file_list(client, bucket) -> List:\\n # pylint: disable=invalid-name\\n PG_HOSTNAME = config('PG_HOSTNAME')\\n PG_DATABASE = config('PG_DATABASE')\\n folder = f'backup/{PG_HOSTNAME}_{PG_DATABASE}'\\n result = await client.list_objects_v2(Bucket=bucket, Prefix=folder)\\n contents = result.get('Contents', None)\\n file_list = list([])\\n if contents:\\n for content in contents:\\n file_list.append(content.get('Key'))\\n return file_list\",\n \"def blobs(self):\\n def blob_iterator():\\n with s3conn(self.access_id, self.secret) as s3:\\n key_iter = s3.list_bucket(self.bucket, prefix=self.prefix+\\\"/\\\")\\n for key in key_iter:\\n blob = key[len(self.prefix)+1:]\\n yield blob\\n return blob_iterator\",\n \"def list_bucket(self, bucket_id=None):\\n url = self.prism_endpoint + \\\"/wBuckets\\\"\\n\\n if bucket_id is not None:\\n url = url + \\\"/\\\" + bucket_id\\n\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + self.bearer_token}\\n\\n r = requests.get(url, headers=headers)\\n\\n if r.status_code == 200:\\n logging.info(\\\"Successfully obtained information about your buckets\\\")\\n return r.json()\\n else:\\n logging.warning(\\\"HTTP Error {}\\\".format(r.status_code))\",\n \"def get_all_files(self, **keyword_args):\\n blobs = self.storage_client.list_blobs(\\n self.bucket.name, **keyword_args)\\n return blobs\",\n \"def list_bucket(self, bucket):\\n self.response.write('Creating more files for listbucket...\\\\n')\\n self.create_file(bucket + '/foo1')\\n self.create_file(bucket + '/foo2')\\n self.response.write('\\\\nListbucket result:\\\\n')\\n\\n page_size = 1\\n stats = gcs.listbucket(bucket, max_keys=page_size)\\n while True:\\n count = 0\\n for stat in stats:\\n count += 1\\n self.response.write(repr(stat))\\n self.response.write('\\\\n')\\n\\n if count != page_size or count == 0:\\n break\\n last_filename = stat.filename[len(bucket) + 1:]\\n stats = gcs.listbucket(bucket, max_keys=page_size, marker=last_filename)\",\n \"def ls(region_name=DEFAULT_REGION):\\n s3conn = s3.connect_to_region(region_name)\\n buckets = s3conn.get_all_buckets()\\n for bucket in buckets:\\n print(bucket.name)\",\n \"def list_objects(bucket=None):\\n hook = GoogleCloudStorageHook()\\n storage_objects = hook.list(bucket)\\n\\n return storage_objects\",\n \"def list_buckets():\\n response = s3.list_buckets()\\n # Output the bucket names\\n print('Existing buckets:')\\n for bucket in response['Buckets']:\\n print(bucket[\\\"Name\\\"])\",\n \"def list_bucket_objects(bucket):\\n for obj in BUCKET_MANAGER.all_objects(bucket).all():\\n print(obj)\",\n \"def list(self, glob_pattern=\\\"\\\"):\\r\\n # strip relative path so we don't step outside our emulated storage area\\r\\n glob_pattern = force_local_path(glob_pattern)\\r\\n\\r\\n # analyze glob_pattern to determine how to return blob names\\r\\n\\r\\n # if glob_pattern is a folder\\r\\n if not glob_pattern:\\r\\n # default to all blobs at the root level\\r\\n glob_pattern = \\\"*\\\"\\r\\n elif is_folder(f\\\"{self._blob_folder()}/{glob_pattern}\\\"):\\r\\n # if glob_pattern is a folder, return all blobs within folder\\r\\n glob_pattern = f\\\"{force_trailing_slash(glob_pattern)}*\\\"\\r\\n else:\\r\\n # use glob_pattern as-is\\r\\n pass\\r\\n\\r\\n # retrieve sorted blob names\\r\\n target_path = f\\\"{self._blob_folder()}/{glob_pattern}\\\"\\r\\n\\r\\n # build list of blob names with local parent path stripped from names\\r\\n blob_names = list()\\r\\n for blob_name in sorted(glob.glob(target_path)):\\r\\n # format name using Linux path delimiters\\r\\n blob_name = blob_name.replace(chr(92), \\\"/\\\")\\r\\n blob_name = blob_name.replace(f\\\"{self._blob_folder()}/\\\", \\\"\\\")\\r\\n blob_names.append(blob_name)\\r\\n\\r\\n blob_count = len(blob_names)\\r\\n logger.debug(\\r\\n self._context(f\\\"list({glob_pattern}) returned {blob_count} blob names\\\")\\r\\n )\\r\\n logger.debug(self._context(f\\\"list({glob_pattern}) = {blob_names}\\\"))\\r\\n return blob_names\",\n \"def get_list_of_buckets():\\r\\n\\r\\n # initialize client\\r\\n storage_client = storage.Client()\\r\\n\\r\\n # get list of buckets\\r\\n buckets = storage_client.list_buckets()\\r\\n\\r\\n list_of_buckets = []\\r\\n for bucket in buckets:\\r\\n list_of_buckets.append(bucket.name)\\r\\n\\r\\n return list_of_buckets\",\n \"def blobs(self):\\n if not self._blobs:\\n workspace = self.attributes.workspace\\n # Instantiates a google client, & get all blobs in bucket\\n storage_client = storage.Client(project=self._user_project)\\n bucket = storage_client.bucket(workspace['bucketName'], user_project=self._user_project)\\n # get subset of data\\n _blobs = {}\\n try:\\n for b in bucket.list_blobs(fields='items(size, etag, crc32c, name, timeCreated),nextPageToken'):\\n name = f\\\"gs://{workspace['bucketName']}/{b.name}\\\"\\n # cache.put(name, {'size': b.size, 'etag': b.etag, 'crc32c': b.crc32c, 'time_created': b.time_created, 'name': name})\\n _blobs[name] = AttrDict({'size': b.size, 'etag': b.etag, 'crc32c': b.crc32c, 'time_created': b.time_created, 'name': name})\\n self._blobs = _blobs\\n except Exception as e:\\n print(f\\\"{self.id} {workspace['bucketName']} {e}\\\")\\n self._blobs = _blobs\\n return self._blobs\",\n \"def list_buckets(self):\\n return [x[\\\"Name\\\"] for x in self.s3_client.list_buckets()[\\\"Buckets\\\"]]\",\n \"def listBucket(self, clientCall):\\n\\t\\ttry:\\n\\t\\t\\tresponse \\t= clientCall.list_objects(Bucket=self.bucket)\\n\\t\\t\\tresponse \\t= self.parseJson(\\\"Contents[].Key\\\", response)\\n\\n\\t\\t\\tfor objects in response:\\n\\t\\t\\t\\twrite(var=f\\\"{g}#\\\", color=w, data=f\\\"{objects}\\\")\\n\\t\\t\\t\\tsleep(0.01)\\n\\n\\t\\t\\treturn(response)\\n\\n\\t\\texcept botocore.exceptions.ClientError as e:\\n\\t\\t\\tif \\\"AccessDenied\\\" and \\\"ListObjects\\\" in e.args[0]:\\n\\t\\t\\t\\treturn(None)\",\n \"def listAllBuckets(self):\\n print self.getAllBuckets()\",\n \"def list_objects(self, bucket_name, prefix=None):\\n url = self.endpoint + '/rest/v2/caches/' + self.cache_name + '?action=keys'\\n res = self.infinispan_client.get(url, auth=self.basicAuth)\\n data = res.content\\n return data\",\n \"def list_files(bucket):\\r\\n s3 = boto3.client('s3')\\r\\n contents = []\\r\\n for item in s3.list_objects(Bucket=bucket)['Contents']:\\r\\n contents.append(item)\\r\\n\\r\\n return contents\",\n \"def list_objects(self, bucket_name, prefix='', offset=0, count=10000):\\n\\n objects, done = h3lib.list_objects(self._handle, bucket_name, prefix, offset, count, self._user_id)\\n return H3List(objects, done=done)\",\n \"def get_list_of_files(bucket_name, path):\\n client = get_client()\\n bucket = client.get_bucket(bucket_name)\\n blob = bucket.blob(filename)\\n return blob\",\n \"async def la() -> Tuple[str]:\\n li = []\\n async with _create_client() as client:\\n for bucket in (await client.list_buckets())['Buckets']:\\n _ = await client.list_objects(Bucket=bucket['Name'])\\n for item in _['Contents']:\\n if item['Key'].endswith('/') is False:\\n li.append(bucket['Name'] + '/' + item['Key'])\\n logger.info('List all objects in all buckets.')\\n return tuple(li)\",\n \"def get_bucketlist():\\n pass\",\n \"def test_list(self):\\n responses.add(\\n responses.Response(\\n method='GET',\\n url='https://connection.keboola.com/v2/storage/buckets',\\n json=list_response\\n )\\n )\\n buckets_list = self.buckets.list()\\n assert isinstance(buckets_list, list)\",\n \"def list_buckets():\\n buckets = gcs.list_buckets()\\n names = []\\n for bucket in buckets:\\n names.append(bucket.name)\\n return names\",\n \"def list_buckets(self):\\n response = self._client.list_buckets()\\n return [item['name'] for item in response['Buckets']]\",\n \"def list(self, prefix='', delimiter='', marker='', headers=None):\\r\\n return BucketListResultSet(self, prefix, delimiter, marker, headers)\",\n \"def blob_stats(self):\\n def blob_iterator():\\n with s3conn(self.access_id, self.secret) as s3:\\n key_iter = s3.list_bucket2(self.bucket, prefix=self.prefix+\\\"/\\\")\\n for head in key_iter:\\n blob = head[LIST_BUCKET_KEY][len(self.prefix)+1:]\\n head['blob'] = blob\\n yield head\\n return blob_iterator\",\n \"def list_files_in_s3_bucket(bucket_name):\\n s3 = boto3.client(\\\"s3\\\")\\n bucket = s3.list_objects(Bucket=bucket_name)\\n contents = [c[\\\"Key\\\"] for c in bucket[\\\"Contents\\\"]]\\n return contents\",\n \"def list_s3_content(bucket, access_id, access_secret, prefix=\\\"\\\"):\\n try:\\n response = s3_client(access_id, access_secret).list_objects(Bucket=bucket, Prefix=prefix)\\n objects = []\\n if response.get(\\\"Contents\\\"):\\n for key in response['Contents']:\\n objects.append(key['Key'])\\n return objects\\n except ClientError as error:\\n LOGGER.error(error)\\n return None\",\n \"def get_objects(self):\\r\\n bucket = self._get_bucket()\\r\\n objs = []\\r\\n for key in bucket:\\r\\n objs.append(key)\\r\\n return objs\",\n \"def iterate_bucket_objects(self, bucket):\\n client = self.credentials.session.client('s3')\\n page_iterator = client.list_objects_v2(Bucket=bucket)\\n if 'Contents' not in page_iterator:\\n return []\\n for item in page_iterator['Contents']:\\n yield item\",\n \"def get_files_in_bucket(self, bucket_name):\\n s3 = self.credentials.session.resource('s3')\\n this_bucket = s3.Bucket(bucket_name)\\n list_of_files = [s3file.key for s3file in this_bucket.objects.all()];\\n return list_of_files\",\n \"def list_files_gcp(client: storage.client.Client, bucket_name: str) -> List[str]:\\n blobs = client.list_blobs(bucket_name)\\n files = [blob.name.split('.')[0] for blob in blobs]\\n return files\",\n \"def list_all_buckets(riak_host,riak_port):\\n url='http://%s:%s/buckets?buckets=true' % (riak_host,riak_port)\\n r=requests.get(url)\\n print json.dumps(r.json(), sort_keys=True, indent=4)\",\n \"def list_public_s3_objects(self):\\n _response = urllib.urlopen(self.options.s3_bucket_url)\\n _html = _response.read()\\n\\n if LOCAL_DEBUG:\\n print _html\\n\\n try:\\n assert \\\"AccessDenied\\\" not in _html\\n assert \\\"NoSuchBucket\\\" not in _html\\n except:\\n print(\\\"ERROR: AccessDenied or NoSuchBucket for {}\\\".format(self.options.s3_bucket_url))\\n raise\",\n \"def list_objects(self, bucket, prefix=None, include_metadata=False) -> list:\\n objects = []\\n kwargs = {'Bucket': bucket}\\n if prefix:\\n kwargs['Prefix'] = prefix\\n\\n is_truncated = True\\n while is_truncated:\\n response = self.client.list_objects_v2(**kwargs)\\n if not response.get('Contents'):\\n return objects\\n\\n for item in response['Contents']:\\n if include_metadata:\\n objects.append(item)\\n else:\\n objects.append(item['Key'])\\n\\n is_truncated = response['IsTruncated']\\n kwargs['ContinuationToken'] = response.get('NextContinuationToken')\\n\\n return objects\",\n \"def read_bucket_objects(bucket_name):\\n s3 = boto3.resource('s3')\\n bucket = s3.Bucket(bucket_name)\\n for obj in bucket.objects.all():\\n response = obj.get()\\n body = response['Body'].read().decode('utf-8')\\n print(body)\",\n \"def ListFiles(s3):\\n response = s3.list_objects(Bucket='mynewbucket123')\\n for content in response.get('Contents', []):\\n yield content.get('Key')\",\n \"def list_buckets(self):\\n msg = \\\"list_buckets not implemented\\\"\\n raise NotImplementedError(msg)\",\n \"def test_list_bucket_content(self):\\n conn = boto3.resource('s3', region_name='us-east-1')\\n # We need to create the bucket since this is all in Moto's 'virtual' AWS account\\n conn.create_bucket(Bucket='foobucket')\\n # Upload fake file to bucket\\n s3 = boto3.client('s3')\\n with open('test/test_resources/test_file', 'rb') as data:\\n s3.upload_fileobj(data, 'foobucket', 'foofile')\\n\\n s3_connector = S3Connector()\\n s3_connector.connect(\\\"default\\\")\\n self.assertEqual(s3_connector.list_bucket_content(\\n \\\"foobucket\\\"), [\\\"foofile\\\"])\",\n \"async def ls(path: str = None) -> Tuple[str]:\\n async with _create_client() as client:\\n if path is None:\\n resp = await client.list_buckets()\\n logger.info('List all buckets.')\\n return tuple([item['Name'] for item in resp['Buckets']])\\n else:\\n _ = path.strip('/').split('/')\\n kws = {'Bucket': _[0]}\\n if len(_) > 1:\\n kws.update({'Prefix': '/'.join(_[1:])})\\n try:\\n resp = await client.list_objects(**kws)\\n logger.info(f'List objects in \\\"{path}\\\".')\\n return tuple([\\n f'/{_[0]}/' + item['Key'] for item in resp['Contents']\\n ]) if 'Contents' in resp.keys() else ()\\n except ClientError:\\n return ()\",\n \"def getBlobs( self ):\\n return self.__blobs;\",\n \"def ListCommand(self, args, sub_opts=None, headers=None, debug=0):\\n listing_style = ListingStyle.SHORT\\n get_bucket_info = False\\n if sub_opts:\\n for o, unused_a in sub_opts:\\n if o == '-b':\\n get_bucket_info = True\\n if o == '-l':\\n listing_style = ListingStyle.LONG\\n if o == '-L':\\n listing_style = ListingStyle.LONG_LONG\\n if not args:\\n # default to listing all gs buckets\\n args = ['gs://']\\n\\n total_objs = 0\\n total_bytes = 0\\n for uri_str in args:\\n uri = self.StorageUri(uri_str, debug=debug, validate=False)\\n\\n if not uri.bucket_name:\\n # Provider URI: add bucket wildcard to list buckets.\\n for uri in self.CmdWildcardIterator('%s://*' % uri.scheme,\\n headers=headers, debug=debug):\\n (bucket_objs, bucket_bytes) = self.PrintBucketInfo(uri, listing_style,\\n headers=headers,\\n debug=debug)\\n total_bytes += bucket_bytes\\n total_objs += bucket_objs\\n\\n elif not uri.object_name:\\n if get_bucket_info:\\n # ls -b request on provider+bucket URI: List info about bucket(s).\\n for uri in self.CmdWildcardIterator(uri, headers=headers,\\n debug=debug):\\n (bucket_objs, bucket_bytes) = self.PrintBucketInfo(uri,\\n listing_style,\\n headers=headers,\\n debug=debug)\\n total_bytes += bucket_bytes\\n total_objs += bucket_objs\\n else:\\n # ls request on provider+bucket URI: List objects in the bucket(s).\\n for obj in self.CmdWildcardIterator(uri.clone_replace_name('*'),\\n ResultType.KEYS,\\n headers=headers, debug=debug):\\n total_bytes += self.PrintObjectInfo(uri, obj, listing_style,\\n headers=headers, debug=debug)\\n total_objs += 1\\n\\n else:\\n # Provider+bucket+object URI -> list the object(s).\\n for obj in self.CmdWildcardIterator(uri, ResultType.KEYS,\\n headers=headers, debug=debug):\\n total_bytes += self.PrintObjectInfo(uri, obj, listing_style,\\n headers=headers, debug=debug)\\n total_objs += 1\\n if listing_style != ListingStyle.SHORT:\\n print ('TOTAL: %d objects, %d bytes (%s)' %\\n (total_objs, total_bytes, MakeHumanReadable(float(total_bytes))))\",\n \"def find_google_cloud_storage_file_names(bucket, prefix=''):\\n return list(bucket.list_blobs(prefix=prefix))\",\n \"def list_keys(self, bucket_name, prefix=None):\\n url = self.endpoint + '/rest/v2/caches/' + self.cache_name + '?action=keys'\\n res = self.infinispan_client.get(url, auth=self.basicAuth)\\n data = res.content\\n return data\",\n \"def list_objects(Bucket=None, Delimiter=None, EncodingType=None, Marker=None, MaxKeys=None, Prefix=None, RequestPayer=None):\\n pass\",\n \"def list_bucket_files(project, bucket_name, referenced_files, verbose):\\n if verbose:\\n print(\\\"listing contents of bucket gs://\\\" + bucket_name)\\n\\n # set up storage client\\n storage_client = storage.Client(project=project)\\n\\n # check if bucket exists\\n try:\\n bucket = storage_client.get_bucket(bucket_name)\\n except:\\n print(f'Bucket {bucket_name} does not exist!')\\n exit(1)\\n\\n # Note: Client.list_bucket_files requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n if verbose:\\n print(\\\"finished listing bucket contents. processing files now in chunks of 1000.\\\")\\n\\n bucket_dict = dict()\\n\\n def extract_file_metadata(blob):\\n blob_name = blob.name\\n\\n if blob_name.endswith('/'): # if this is a directory\\n return None\\n\\n full_file_path = \\\"gs://\\\" + bucket_name + \\\"/\\\" + blob_name\\n # support new submissions directory structure in Terra bucket\\n submissions_dir = \\\"submissions\\\"\\n if full_file_path.split('/', 4)[3] == submissions_dir:\\n # new format is gs://bucket_id/submissions/submission_id/remaining_path\\n submission_id = full_file_path.split('/', 5)[4]\\n else:\\n # old format is gs://bucket_id/submission_id/remaining_path\\n # Splits the bucket file: \\\"gs://bucket_Id/submission_id/file_path\\\", by the '/' symbol\\n # and stores values in a 5 length array: ['gs:', '' , 'bucket_Id', submission_id, file_path]\\n # to extract the submission id from the 4th element (index 3) of the array\\n submission_id = full_file_path.split('/', 4)[3]\\n\\n file_metadata = {\\n \\\"file_name\\\": blob_name.split('/')[-1],\\n \\\"file_path\\\": full_file_path,\\n \\\"submission_id\\\": submission_id,\\n \\\"size\\\": blob.size,\\n \\\"is_in_data_table\\\": full_file_path in referenced_files,\\n \\\"time_created\\\": blob.time_created\\n }\\n\\n return file_metadata\\n\\n n_blobs = 0\\n for page in blobs.pages: # iterating through pages is way faster than not\\n if verbose:\\n n_blobs += page.remaining\\n print(f'...processing {n_blobs} blobs', end='\\\\r')\\n for blob in page:\\n file_metadata = extract_file_metadata(blob)\\n if file_metadata:\\n full_file_path = file_metadata['file_path']\\n bucket_dict[full_file_path] = file_metadata\\n\\n if verbose:\\n print(f'Found {len(bucket_dict)} files in bucket {bucket_name}')\\n\\n return bucket_dict\",\n \"def list_s3(bucket, prefix, ext):\\n s3 = boto3.resource('s3')\\n s3_bucket = s3.Bucket(bucket)\\n\\n if ext:\\n ext = '.' + ext.lstrip('.')\\n else:\\n ext = ''\\n\\n counter = 0\\n for item in s3_bucket.objects.filter(Prefix=prefix):\\n counter += 1\\n if counter % 5000 == 0:\\n print(f'Found {counter} items so far', file=sys.stderr)\\n\\n key = item.key\\n if not key.endswith(ext):\\n continue\\n\\n # Write to stdout\\n print(key)\",\n \"def list_objects(self, s3_prefix_path):\\n bucket_name, prefix = S3Util.get_bucket_and_key(s3_prefix_path)\\n bucket = self.s3_resource.Bucket(bucket_name)\\n return [\\\"s3://\\\" + bucket_name + \\\"/\\\" + key.key for key in bucket.objects.filter(Prefix=prefix)]\",\n \"def ListFiles(bucketname, client, key):\\n response = client.list_objects(Bucket=bucketname, Prefix=key)\\n for content in response.get('Contents', []):\\n yield content.get('Key')\",\n \"def list_files(self, container_name, dir_path):\\n blobs = RetryHandler.retry(lambda: self.blob_client.list_blobs(container_name, prefix=dir_path))\\n for b in blobs:\\n yield b.name\",\n \"def list_buckets():\\n pass\",\n \"def buckets(self):\\n if self._s3client is None:\\n raise BucketStorageUnavailableException()\\n\\n buckets = [Bucket(self, x.name, create=False) for x in self._s3resource.buckets.all()]\\n return buckets\",\n \"def test_list_blobs(*args, **kwargs):\\n bucket_or_name = args[0]\\n prefix = kwargs['prefix']\\n candidate_path = f'{bucket_or_name}/{prefix}'\\n config_paths = []\\n\\n for c in config_hierarchy:\\n if c.startswith(candidate_path):\\n fn = '/'.join(c.split('/')[1:])\\n b = Blob(bucket='dummy', name=fn)\\n config_paths.append(b)\\n\\n return iter(config_paths)\",\n \"def List(self, prefix=''):\\n\\n bucket, bucket_path = self._ParseBucketAndPath(prefix)\\n names = []\\n request = self._service.objects().list(bucket=bucket, prefix=bucket_path)\\n response = self._RunWithRetries(request.execute, self._CommonErrorMatcher)\\n\\n while response:\\n if 'items' in response:\\n names += [item['name'] for item in response['items']]\\n\\n if 'nextPageToken' in response:\\n request = self._service.objects().list(\\n bucket=bucket, prefix=bucket_path,\\n pageToken=response['nextPageToken'])\\n response = self._RunWithRetries(request.execute,\\n self._CommonErrorMatcher)\\n else:\\n response = None\\n\\n return names\",\n \"def get_objects(self, bucket, s3_client=None):\\n\\n try:\\n all_objects = s3_client.list_objects_v2(Bucket=bucket)\\n except botocore.exceptions.EndpointConnectionError:\\n logging.error(\\\"Couldn't connect to an S3 endpoint. If you're using an S3 compatible provider other than AWS, remember to set --s3-endpoint-url\\\")\\n sys.exit(1)\\n\\n if 'Contents' in all_objects.keys():\\n all_objects = [ k['Key'] for k in [ obj for obj in all_objects['Contents'] ]] # pylint: disable=unnecessary-comprehension\\n else:\\n all_objects = []\\n\\n return all_objects\",\n \"def ls(self):\\n files = self.drive.files().list().execute().get(\\\"files\\\", [])\\n for f in files:\\n print(f[\\\"name\\\"], f[\\\"mimeType\\\"])\",\n \"def list_all_objects_s3(bucket, prefix, profile):\\n s3 = boto3.Session(profile_name=profile).client('s3')\\n keys = []\\n continuation_token = \\\"\\\"\\n\\n while True:\\n list_kwargs = dict(Bucket=bucket, Prefix=prefix)\\n if continuation_token:\\n list_kwargs['ContinuationToken'] = continuation_token\\n resp = s3.list_objects_v2(**list_kwargs)\\n keys += [x['Key'] for x in resp.get('Contents', [])]\\n \\n if not resp.get('IsTruncated'):\\n break\\n continuation_token = resp.get('NextContinuationToken')\\n \\n return keys\",\n \"def list(self, prefix=\\\"\\\"):\\n try:\\n list_rep = self.client.listdir(self.bucket + \\\"/\\\" + prefix)\\n for i in list_rep:\\n # Remove preceding bucket name and potential leading slash from returned key value\\n i = i.replace(self.bucket, \\\"\\\").replace('tar', 'wsp.sz')\\n if i[0] == '/': i = i[1:]\\n yield i\\n except pyhdfs.HdfsFileNotFoundException:\\n pass\",\n \"def get_all(user_id):\\n return Bucketlist.query.filter_by(created_by=user_id)\",\n \"def list_hnd(self, request, **kwargs):\\n prefix = request.POST.get(\\\"prefix\\\", \\\"\\\")\\n marker = request.POST.get(\\\"marker\\\", \\\"\\\")\\n delimiter = request.POST.get(\\\"delimiter\\\", \\\"\\\")\\n\\n max_keys = int(request.POST.get(\\\"max_keys\\\", 1000))\\n max_keys = max((1, max_keys)) # >= 1\\n max_keys = min((1000, max_keys)) # <= 1000\\n\\n bucket = store.get_bucket()\\n\\n # prefix \\\"prefix\\\" with user dir\\n eff_prefix = store.prefix_with_user_dir(request.user, prefix)\\n\\n # get list iterator from s3\\n file_iter = bucket.list(prefix=eff_prefix, delimiter=delimiter,\\n marker=marker, headers=None,\\n encoding_type=None)\\n\\n # convert to list, try to get +1 item to be able\\n # to determine if the results are truncated\\n files = [key.key.split(\\\"/\\\", 1)[1]\\n for key in itertools.islice(file_iter, 0, max_keys+1)]\\n\\n # if max_keys is less then there are more results\\n # -> truncated = True\\n truncated = len(files) > max_keys\\n if truncated:\\n # return 1 item less\\n files = files[:-1]\\n\\n return self.create_response(request, {\\n \\\"files\\\": files,\\n \\\"truncated\\\": truncated\\n })\",\n \"def files_storage_list(self, prefix='pipeline/', print_paths=False):\\n\\n return self.backend.files_storage_list(prefix=prefix, print_paths=print_paths)\",\n \"def list_objects_v2(Bucket=None, Delimiter=None, EncodingType=None, MaxKeys=None, Prefix=None, ContinuationToken=None, FetchOwner=None, StartAfter=None, RequestPayer=None):\\n pass\",\n \"def list_blobs(rsf_file, output_format=None, stream=None):\\n\\n cmds = rsf.read(rsf_file)\\n register = Register(cmds)\\n\\n utils.check_readiness(register)\\n blobs = register.log.blobs\\n\\n if output_format == \\\"csv\\\":\\n schema = register.schema()\\n headers = [attr.uid for attr in schema.attributes]\\n xsv.serialise(stream, blobs, headers)\\n\\n stream.seek(0)\\n\\n return None\\n\\n if output_format == \\\"json\\\":\\n utils.serialise_json({repr(k): v for k, v in blobs.items()}, stream)\\n\\n stream.seek(0)\\n\\n return None\\n\\n return blobs.values()\",\n \"def list_buckets(schedule_id):\\n\\n from mist.api.poller.models import ListBucketsPollingSchedule\\n sched = ListBucketsPollingSchedule.objects.get(id=schedule_id)\\n try:\\n sched.cloud.ctl.objectstorage.list_buckets(persist=False)\\n except (PeriodicTaskLockTakenError, PeriodicTaskTooRecentLastRun) as exc:\\n list_buckets.logger.warning(\\n '%s failed with %r',\\n sched.name, exc)\\n except Exception as exc:\\n list_buckets.logger.error(\\n '%s failed with %r',\\n sched.name, exc)\",\n \"def list_objects(self, prefix, limit=None, give_size=False, suffix=''):\\n objects = []\\n total_size = 0\\n if not limit or limit > 1000:\\n paginator = self.client.get_paginator(\\\"list_objects\\\")\\n page_iterator = paginator.paginate(Bucket=self.bucket, EncodingType='url', Prefix=prefix)\\n for page in page_iterator:\\n if \\\"Contents\\\" in page:\\n for key in page[\\\"Contents\\\"]:\\n if suffix != '':\\n if key[\\\"Key\\\"].endswith(suffix):\\n objects.append(urllib.parse.unquote(key[\\\"Key\\\"]))\\n total_size += key['Size']\\n else:\\n objects.append(key[\\\"Key\\\"])\\n total_size += key['Size']\\n else:\\n response = self.client.list_objects_v2(Bucket=self.bucket, Prefix=prefix)\\n if \\\"Contents\\\" in response:\\n for key in response['Contents']:\\n if suffix != '':\\n if key[\\\"Key\\\"].endswith(suffix):\\n objects.append(key[\\\"Key\\\"])\\n total_size += key['Size']\\n else:\\n objects.append(key[\\\"Key\\\"])\\n total_size += key['Size']\\n if give_size:\\n return list(map(lambda key: key.replace('%3D', '='), objects)), total_size\\n else:\\n return list(map(lambda key: key.replace('%3D', '='), objects))\",\n \"def get(self):\\n\\n upload_info = self.request.GET.get('upload_info')\\n if upload_info:\\n self.response.headers['content-type'] = 'text/plain'\\n self.response.out.write(upload_info)\\n return\\n\\n blobs = reversed(blobstore.BlobInfo.all().fetch(10))\\n output = template.render('index.html', {'blobs': blobs})\\n self.response.out.write(output)\",\n \"def get_buckets_for_user(self):\\n s3 = self.credentials.session.resource('s3')\\n bucket_list = [bucket.name for bucket in s3.buckets.all()]\\n\\n return bucket_list;\",\n \"def s3_list_files(prefix, as_generator=False):\\n return _do_list_files(S3_BUCKET, prefix, as_generator=as_generator)\",\n \"def get_list_of_keys(self, bucket_name=None, callback_fn=None):\\n list_of_keys = []\\n if not callback_fn:\\n callback_fn = lambda x: print(x.key)\\n if bucket_name:\\n buckets = [self.s3_.get_bucket(bucket_name)]\\n else:\\n buckets = [b for b in self.s3_.get_all_buckets() if b.name not in self.BLACKLIST]\\n for bucket in buckets:\\n for key in bucket.list():\\n callback_fn(key)\\n list_of_keys.append(key)\\n return list_of_keys\",\n \"def manipulate_bucketlist():\\n pass\",\n \"def list_buckets(args):\\n\\n args.suppress_verify_output = True\\n if verify(args) != 0:\\n # restore stdout\\n sys.stdout = sys.__stdout__\\n print(\\\"Config file not valid, please use the verify function to debug\\\")\\n return\\n\\n with open(args.file, \\\"r\\\") as f:\\n config_json = json.load(f)\\n\\n for group in config_json[\\\"groups\\\"]:\\n print(group[\\\"name\\\"] + \\\":\\\")\\n for bucket in group[\\\"buckets\\\"]:\\n print(\\\"\\\\t\\\" + bucket[\\\"name\\\"])\",\n \"async def glob(self):\\n # get the path string up to the wildcards\\n try:\\n pi1 = self._path.index(\\\"*\\\")\\n except ValueError:\\n pi1 = len(self._path)\\n try:\\n pi2 = self._path.index(\\\"?\\\")\\n except ValueError:\\n pi2 = len(self._path)\\n pi = min(pi1, pi2)\\n # using the prefix will cut down on the search space\\n prefix = self._path[:pi]\\n # get the wildcard\\n wildcard = self._path[pi:]\\n # set up the paginator\\n paginator = self._conn_obj.conn.get_paginator(\\\"list_objects_v2\\\")\\n parameters = {\\n 'Bucket': self._bucket,\\n 'Prefix': prefix\\n }\\n page_iterator = paginator.paginate(**parameters)\\n files = []\\n async for page in page_iterator:\\n for item in page.get('Contents', []):\\n fname = item['Key']\\n # check that it matches against wildcard\\n if fnmatch(fname, wildcard):\\n files.append(item['Key'])\\n return files\",\n \"def describe_objects(bucket_name):\\n # Upload the file\\n try:\\n # Retrieve list of files in bucket\\n response = s3.list_objects_v2(Bucket=bucket_name)\\n files = response.get(\\\"Contents\\\")\\n # Output file names\\n for file in files:\\n print(f\\\"file_name: {file['Key']}, size: {file['Size']}\\\")\\n except ClientError as e:\\n logging.error(e)\\n return False\\n return True\",\n \"def aws_s3_ls(s3_uri: str, list_extended=False)->list:\\n client = boto3.client(\\\"s3\\\")\\n bucket, prefix = _extract_bucket_key(s3_uri)\\n s3_objects = []\\n cont_token = None\\n while (True):\\n if cont_token is None:\\n kwargs = {\\n \\\"Bucket\\\": bucket,\\n \\\"MaxKeys\\\": 100,\\n \\\"Prefix\\\": prefix\\n }\\n else:\\n kwargs = {\\n \\\"Bucket\\\": bucket,\\n \\\"MaxKeys\\\": 100,\\n \\\"Prefix\\\": prefix,\\n \\\"ContinuationToken\\\": cont_token\\n } \\n try:\\n response = client.list_objects_v2(**kwargs)\\n if response[\\\"KeyCount\\\"] == 0:\\n print (\\\"Requested s3 object doesn't exist.\\\")\\n break\\n for record in response[\\\"Contents\\\"]:\\n if record[\\\"Size\\\"] > 0: # ignore just prefix names\\n if list_extended:\\n s3_objects.append((record[\\\"Size\\\"], \\n record[\\\"LastModified\\\"].strftime(\\\"%Y%m%d %H:%M:%S.%s\\\"), \\n record[\\\"Key\\\"]))\\n else:\\n s3_objects.append(record[\\\"Key\\\"])\\n if response[\\\"IsTruncated\\\"]:\\n cont_token = response[\\\"NextContinuationToken\\\"]\\n else:\\n break\\n except Exception as exc:\\n raise Error(\\\"Error {} occurred while listing objects.\\\".format(exc))\\n return s3_objects\",\n \"def list_buckets(self, s3_client=None):\\n\\n s3_client = s3_client or self.s3_client\\n\\n try:\\n return ([ bucket['Name'] for bucket in s3_client.list_buckets()['Buckets'] ])\\n except botocore.exceptions.EndpointConnectionError:\\n logging.error(\\\"Couldn't connect to an S3 endpoint. If you're using an S3 compatible provider other than AWS, remember to set --s3-endpoint-url\\\")\\n sys.exit(1)\\n except botocore.exceptions.ClientError as e:\\n print(e)\\n sys.exit(1)\",\n \"def ls(self, s3uri, **kwargs):\\n return self.exec_command('ls %s' % (s3uri,), **kwargs)\",\n \"def get_keys(self):\\n bucket = self.resource.Bucket(self.bucketname)\\n return [key.key for key in bucket.objects.all()]\",\n \"def list(self, path, filename=None, start=None, stop=None, recursive=False, directories=False):\\n storageScheme, keys = self.getkeys(\\n path, filename=filename, directories=directories, recursive=recursive)\\n keys = [storageScheme + \\\":///\\\" + key.bucket.name + \\\"/\\\" + key.name for key in keys]\\n keys.sort()\\n keys = select(keys, start, stop)\\n return keys\",\n \"def blobs(self):\\n blobs = pipeline(\\n ftype_selector([FILE]),\\n fmap(first),\\n fmap(self.reverser),\\n )(self.root.entries())\\n return blobs\",\n \"def list_s3_files(bucket, prefix):\\n \\n s3 = boto3.client('s3')\\n\\n if type(prefix) != list:\\n prefix = [prefix]\\n \\n # Loop over prefixes:\\n file_list = []\\n for p in prefix:\\n \\n # Load one prefix:\\n response = s3.list_objects_v2(Bucket=bucket, Prefix=p)\\n if response['KeyCount'] > 0:\\n file_list = file_list + [d['Key'] for d in response['Contents']]\\n while response['IsTruncated']:\\n response = s3.list_objects_v2(Bucket=bucket, Prefix=p, StartAfter=file_list[-1])\\n file_list = file_list + [d['Key'] for d in response['Contents']] \\n \\n return file_list\",\n \"def list(number=0):\\n buckets = get_buckets(number)\\n data = [(\\\"Name\\\", \\\"Versioned\\\", \\\"LifeCycle\\\")]\\n col_width = [0, 0, 0]\\n rows = []\\n for name, bucket in buckets.items():\\n v = bucket[\\\"versioning\\\"]\\n l = bucket[\\\"lifecycle\\\"]\\n v = v if v else \\\"Disabled\\\"\\n if l:\\n l = json.dumps(l, indent=1)\\n else:\\n l = \\\"None\\\"\\n data.append((name, v, l))\\n for row in data:\\n for i, info in enumerate(row):\\n col_width[i] = min(max(len(info) + 2, col_width[i]), 48)\\n dashes = tuple((\\\"-\\\" * (width - 1) for width in col_width))\\n data.insert(1, dashes)\\n click.echo(f\\\"The status of the buckets:\\\")\\n for row in data:\\n output = \\\"\\\"\\n for i in range(3):\\n output += row[i].ljust(col_width[i])\\n if not VERBOSE:\\n click.echo(output)\\n logger.info(output)\",\n \"def list_buckets(self, regex=\\\".*\\\", verbose=False):\\n r = re.compile(regex)\\n if verbose:\\n return [b for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])]\\n else:\\n return [b['Name'] for b in self.client.list_buckets()['Buckets'] if r.match(b['Name'])]\",\n \"def list_s3(bucket, prefix, ext=None):\\n s3 = boto3.resource('s3')\\n s3_bucket = s3.Bucket(bucket)\\n\\n if ext:\\n ext = '.' + ext.lstrip('.')\\n else:\\n ext = ''\\n\\n for item in s3_bucket.objects.filter(Prefix=prefix):\\n key = item.key\\n if not key.endswith(ext):\\n continue\\n\\n yield key\",\n \"def list_all_keys(riak_host,riak_port,bucket):\\n url='http://%s:%s/buckets/%s/keys?keys=true' % (riak_host,riak_port,bucket)\\n #print url\\n r=requests.get(url)\\n print json.dumps(r.json(), sort_keys=True, indent=4)\",\n \"def list(\\n self,\\n bucket: str,\\n prefix: str=None,\\n delimiter: str=None,\\n ) -> typing.Iterator[str]:\\n raise NotImplementedError()\",\n \"def list_buckets_from_project(projectname):\\n response = jsonify(\\n admin.list_buckets_on_project_by_name(current_app.scoped_session(), projectname)\\n )\\n return response\",\n \"def list_available_tiles(prefix):\\n\\n files = []\\n generator = hls_container_client.list_blobs(name_starts_with=prefix)\\n for blob in generator:\\n files.append(blob.name)\\n return files\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81295836","0.80809796","0.8063129","0.80104107","0.79991764","0.7891442","0.771796","0.7614527","0.757374","0.7504958","0.74911094","0.74169266","0.73137933","0.72736335","0.7269783","0.72240984","0.7204592","0.7182615","0.7155751","0.71367675","0.71151024","0.7106814","0.6919984","0.6898795","0.6889916","0.68797797","0.6870751","0.6832451","0.6791812","0.67580414","0.67227495","0.6707942","0.6677458","0.66417694","0.6629878","0.6588292","0.6539728","0.65361226","0.6506271","0.6487749","0.6464164","0.6443255","0.64359194","0.64187723","0.64157534","0.6406138","0.63670814","0.63432145","0.6338134","0.633423","0.63275063","0.6287175","0.62742704","0.6270394","0.6270205","0.624025","0.6238867","0.6235499","0.6227802","0.62112206","0.620957","0.6200909","0.61903316","0.61492324","0.6134529","0.6131121","0.61301214","0.60760695","0.607269","0.6048615","0.6013497","0.599046","0.5958408","0.5957556","0.5936025","0.5920029","0.59067225","0.5893506","0.58727837","0.58573127","0.58504134","0.5832183","0.58315194","0.5819633","0.58070934","0.57894206","0.5784265","0.5779386","0.5759396","0.5758678","0.57579935","0.57562685","0.57547694","0.5750166","0.5731678","0.57311475","0.5719248","0.5712901","0.5688824","0.5684758"],"string":"[\n \"0.81295836\",\n \"0.80809796\",\n \"0.8063129\",\n \"0.80104107\",\n \"0.79991764\",\n \"0.7891442\",\n \"0.771796\",\n \"0.7614527\",\n \"0.757374\",\n \"0.7504958\",\n \"0.74911094\",\n \"0.74169266\",\n \"0.73137933\",\n \"0.72736335\",\n \"0.7269783\",\n \"0.72240984\",\n \"0.7204592\",\n \"0.7182615\",\n \"0.7155751\",\n \"0.71367675\",\n \"0.71151024\",\n \"0.7106814\",\n \"0.6919984\",\n \"0.6898795\",\n \"0.6889916\",\n \"0.68797797\",\n \"0.6870751\",\n \"0.6832451\",\n \"0.6791812\",\n \"0.67580414\",\n \"0.67227495\",\n \"0.6707942\",\n \"0.6677458\",\n \"0.66417694\",\n \"0.6629878\",\n \"0.6588292\",\n \"0.6539728\",\n \"0.65361226\",\n \"0.6506271\",\n \"0.6487749\",\n \"0.6464164\",\n \"0.6443255\",\n \"0.64359194\",\n \"0.64187723\",\n \"0.64157534\",\n \"0.6406138\",\n \"0.63670814\",\n \"0.63432145\",\n \"0.6338134\",\n \"0.633423\",\n \"0.63275063\",\n \"0.6287175\",\n \"0.62742704\",\n \"0.6270394\",\n \"0.6270205\",\n \"0.624025\",\n \"0.6238867\",\n \"0.6235499\",\n \"0.6227802\",\n \"0.62112206\",\n \"0.620957\",\n \"0.6200909\",\n \"0.61903316\",\n \"0.61492324\",\n \"0.6134529\",\n \"0.6131121\",\n \"0.61301214\",\n \"0.60760695\",\n \"0.607269\",\n \"0.6048615\",\n \"0.6013497\",\n \"0.599046\",\n \"0.5958408\",\n \"0.5957556\",\n \"0.5936025\",\n \"0.5920029\",\n \"0.59067225\",\n \"0.5893506\",\n \"0.58727837\",\n \"0.58573127\",\n \"0.58504134\",\n \"0.5832183\",\n \"0.58315194\",\n \"0.5819633\",\n \"0.58070934\",\n \"0.57894206\",\n \"0.5784265\",\n \"0.5779386\",\n \"0.5759396\",\n \"0.5758678\",\n \"0.57579935\",\n \"0.57562685\",\n \"0.57547694\",\n \"0.5750166\",\n \"0.5731678\",\n \"0.57311475\",\n \"0.5719248\",\n \"0.5712901\",\n \"0.5688824\",\n \"0.5684758\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7789013"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":94596,"cells":{"query":{"kind":"string","value":"Downloads a blob from the bucket."},"document":{"kind":"string","value":"def gcs_download_blob(bucket_name, source_blob_name, destination_file_name):\n storage_client = client #storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n blob.download_to_filename(destination_file_name)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def download_blob(bucket_name, source_blob_name):\n\n storage_client = storage.Client()\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n return blob","def blob_download(blob_url):\n blob = storage.Object.from_url(blob_url)\n blobc = blob.download()\n return blobc","def download_blob(bucket_name, source_blob_name):\n storage_client = storage.Client()\n\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n return blob.download_as_string().decode()","def download_blob(url: str) -> io.BytesIO:\n storage_client = storage.Client()\n bucket_name = get_bucket_name(url)\n source_blob_name = get_blob_name(url)\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n f = io.BytesIO(blob.download_as_bytes())\n return f","def download_bucket(blob_name, path_to_file):\r\n blob = bucket.blob(blob_name)\r\n blob.download_to_filename(path_to_file)","def download_specific_blob(bucket_name, path_to_storage_file_name, download_file_name):\r\n\r\n try:\r\n # initialize client & get blob\r\n _, _, blob = create_client(bucket_name, path_to_storage_file_name)\r\n\r\n # set the path to source file\r\n blob.download_to_filename(download_file_name)\r\n \r\n except Exception as err:\r\n raise err\r\n sys.exit(1)\r\n \r\n else:\r\n print(f\"download blob '{path_to_storage_file_name}' succeed\")\r\n\r\n return None","def get_blob(self, download_meta):\n bucket_name, key = self._get_bucket_key(download_meta)\n response = self.s3.get_object(Bucket=bucket_name,\n Key=key)\n return response['Body'].read().decode()","def download_blob(bucket_name, source_blob_name, destination_file_name):\n storage_client = storage.Client()\n try:\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n \n blob.download_to_filename(destination_file_name)\n \n print('Blob {} downloaded to {}.'.format(\n source_blob_name,\n destination_file_name)) \n except:\n print(\"User does not have access to that bucket. Trying public link:\")\n gcs_url = 'https://%(bucket)s.storage.googleapis.com/%(file)s' % {'bucket':bucket_name, 'file':source_blob_name}\n urllib.urlretrieve(gcs_url, destination_file_name)\n print (\"Download complete\")","def downloadBlob(self, oid, serial):\n\n key = s3_blob_filename(oid, serial)\n\n # Confirm blob cache directory is locked for writes\n cache_filename = self.fshelper.getBlobFilename(oid, serial)\n lock_filename = os.path.join(os.path.dirname(cache_filename), '.lock')\n assert os.path.exists(lock_filename)\n\n # Download\n self.bucket.download_file(key, cache_filename)\n os.chmod(cache_filename, stat.S_IREAD)\n\n # Cache bookkeeping\n self._blob_data_bytes_loaded += os.path.getsize(cache_filename)\n self._check_blob_size(self._blob_data_bytes_loaded)","def download_blob(bucket_name, source_blob_name, destination_file_name):\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n blob.download_to_filename(destination_file_name)\n\n print('Blob {} downloaded to {}.'.format(\n source_blob_name,\n destination_file_name))","def get_blob(self, blob_name):\n return self.bucket.get_blob(blob_name)","def download(self, bucket, object, filename=None):\n service = self.get_conn()\n downloaded_file_bytes = service \\\n .objects() \\\n .get_media(bucket=bucket, object=object) \\\n .execute()\n\n # Write the file to local file path, if requested.\n if filename:\n write_argument = 'wb' if isinstance(downloaded_file_bytes, bytes) else 'w'\n with open(filename, write_argument) as file_fd:\n file_fd.write(downloaded_file_bytes)\n\n return downloaded_file_bytes","def download_blob(bucket_name, source_blob_name, destination_file_name):\n # bucket_name = \"your-bucket-name\"\n # source_blob_name = \"storage-object-name\"\n # destination_file_name = \"local/path/to/file\"\n\n bucket = storage_client.bucket(bucket_name)\n blobs = storage_client.list_blobs(bucket_name)\n for blob in blobs:\n print(blob.name)\n if (blob.name == source_blob_name):\n # Construct a client side representation of a blob.\n # Note `Bucket.blob` differs from `Bucket.get_blob` as it doesn't retrieve\n # any content from Google Cloud Storage. As we don't need additional data,\n # using `Bucket.blob` is preferred here.\n blob = bucket.blob(source_blob_name)\n blob.download_to_filename(destination_file_name)\n \n print(\n \"Blob {} downloaded to {}.\".format(\n source_blob_name, destination_file_name\n )\n )","def download_blob(bucket_name, source_blob_name, destination_file_name):\n # The ID of your GCS bucket\n # bucket_name = \"your-bucket-name\"\n\n # The ID of your GCS object\n # source_blob_name = \"storage-object-name\"\n\n # The path to which the file should be downloaded\n # destination_file_name = \"local/path/to/file\"\n\n storage_client = storage.Client()\n\n bucket = storage_client.bucket(bucket_name)\n\n # Construct a client side representation of a blob.\n # Note `Bucket.blob` differs from `Bucket.get_blob` as it doesn't retrieve\n # any content from Google Cloud Storage. As we don't need additional data,\n # using `Bucket.blob` is preferred here.\n blob = bucket.blob(source_blob_name)\n blob.download_to_filename(destination_file_name)\n\n print(\n \"Downloaded storage object {} from bucket {} to local file {}.\".format(\n source_blob_name, bucket_name, destination_file_name))","def download_blob(source_blob_name, destination_file_name, bucket_name=\"bts-ml-data\"):\n # bucket_name = \"your-bucket-name\"\n # source_blob_name = \"storage-object-name\"\n # destination_file_name = \"local/path/to/file\"\n\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n blob.download_to_filename(destination_file_name)\n\n print(\n \"Blob {} downloaded to {}.\".format(\n source_blob_name, destination_file_name\n )\n )","def run(\n self,\n bucket: str = None,\n blob: str = None,\n project: str = None,\n chunk_size: int = None,\n credentials: dict = None,\n encryption_key: str = None,\n request_timeout: Union[float, Tuple[float, float]] = 60,\n ) -> str:\n # create client\n client = get_storage_client(project=project, credentials=credentials)\n\n # retrieve bucket\n bucket = self._retrieve_bucket(\n client=client, bucket=bucket, create_bucket=False\n )\n\n # identify blob name\n blob = self._get_blob(\n bucket,\n blob,\n chunk_size=chunk_size,\n encryption_key=encryption_key,\n )\n # Support GCS < 1.31\n return (\n blob.download_as_bytes(timeout=request_timeout)\n if hasattr(blob, \"download_as_bytes\")\n else blob.download_as_string(timeout=request_timeout)\n )","def download(bucket, key):\n validate_bucket_name(bucket)\n validate_key_name(key)\n client = get_client()\n\n # do a buffered download\n bytes_io = io.BytesIO()\n client.download_fileobj(bucket, key, bytes_io)\n\n # hope that stuff is not too big, and just return content\n return bytes_io.getvalue()","def get_blob(self, download_meta):\n blob_id = download_meta['blob_id']\n if isinstance(blob_id, str):\n blob_id = uuid.UUID(blob_id)\n session = self.DBSession()\n blob = session.get(Blob, blob_id) # was session.query(Blob).get(blob_id), rewritten for SA2.0\n return blob.data","def _retrieve_blob(self, object_key):\n return self.s3_resource.Object(self.CVE_BUCKET, object_key).get()['Body'].read()","def download(self, bucket_name, file_name, file_path):\n\n self.client.download_file(bucket_name, file_name, file_path)","def loadS3Blob(self, oid, serial):\n\n # Check if it's already in the cache\n cache_filename = self.fshelper.getBlobFilename(oid, serial)\n if os.path.exists(cache_filename):\n return ZEO.ClientStorage._accessed(cache_filename)\n\n # If not, download from S3...\n # First, we'll create the directory for this oid, if it doesn't exist.\n self.fshelper.createPathForOID(oid)\n\n # OK, it's not here and we (or someone) needs to get it. We\n # want to avoid getting it multiple times. We want to avoid\n # getting it multiple times even accross separate client\n # processes on the same machine. We'll use file locking.\n\n lock = ZEO.ClientStorage._lock_blob(cache_filename)\n try:\n # We got the lock, so it's our job to download it. First,\n # we'll double check that someone didn't download it while we\n # were getting the lock:\n\n if os.path.exists(cache_filename):\n return ZEO.ClientStorage._accessed(cache_filename)\n\n # Actually download the blob. When this function\n # returns, it will have been sent. (The receiving will\n # have been handled by the asyncore thread.)\n self.downloadBlob(oid, serial)\n\n if os.path.exists(cache_filename):\n return ZEO.ClientStorage._accessed(cache_filename)\n\n raise ZODB.POSException.POSKeyError(\"No blob file\", oid, serial)\n finally:\n lock.close()","def download_file(self, bucket_name, object_name, file_name):\n self._client.download_file(bucket_name, object_name, file_name)","def download_blob(blob, file_group, destination, blob_service, progress_callback):\n\n def _wrap_callback(curr, total):\n if progress_callback:\n progress_callback(curr, total, destination)\n\n blob_service.get_blob_to_path(\n get_container_name(file_group), blob, destination,\n progress_callback=_wrap_callback)","def __retrieve_from_bucket(fname):\n blob = BUCKET.blob(fname)\n json_data = json.loads(blob.download_as_string())\n return json_data","def urlgrab(self, url, filename=None, **kwargs):\n blob_location = \"%s/%s\" % (self.base_path, url)\n self.verbose_logger.info(\"downloading gs://%s/%s to %s\" % (self.bucket.name, blob_location, filename))\n url = url.lstrip('/')\n if not filename:\n filename = url\n\n blob = storage.blob.Blob(name=blob_location,bucket = self.bucket)\n blob.download_to_filename(filename)\n return filename","def get_raw(key: str, bucket: google.cloud.storage.bucket.Bucket) -> bytes:\n blob = google.cloud.storage.blob.Blob(name=key, bucket=bucket)\n return blob.download_as_string()","def get_blob_url(self, download_meta):\n bucket_name, key = self._get_bucket_key(download_meta)\n location = self.s3.generate_presigned_url(\n ClientMethod='get_object',\n ExpiresIn=36*60*60,\n Params={'Bucket': bucket_name, 'Key': key})\n return location","def _download_from_bucket(self, ext_filename, local_filename, force=False):\n if os.path.exists(local_filename) and not force:\n logging.info('File {} already exists. Not overwriting...'.format(local_filename))\n return\n if os.path.exists(local_filename) and force:\n logging.info('File {} already exists. Overwriting...'.format(local_filename))\n else:\n logging.info('File {} does not exist. Downloading...'.format(local_filename))\n\n Path(os.path.dirname(local_filename)).mkdir(parents=True, exist_ok=True)\n\n if self.s3:\n self.bucket.download_file(ext_filename, local_filename)\n logging.info('Downloaded {} to {}'.format(ext_filename, local_filename))\n if self.gs:\n try:\n client = storage.Client()\n bucket = client.get_bucket(self.bucket_name)\n blob = storage.Blob(ext_filename, bucket)\n blob.download_to_filename(local_filename)\n logging.info('Downloaded {} to {}'.format(ext_filename, local_filename))\n except:\n logging.warning('Downloading failed')\n\n i += 1","def download_file(bucket_name: str, object_name: str, file_path: str):\n # pylint: disable=import-outside-toplevel\n from sotaque_brasileiro.utils import safe_getenv\n\n minio_client = Minio(\n safe_getenv(constants.MINIO_ENDPOINT.value),\n access_key=safe_getenv(constants.MINIO_ACCESS_KEY.value),\n secret_key=safe_getenv(constants.MINIO_SECRET_KEY.value),\n )\n minio_client.fget_object(bucket_name, object_name, file_path)","def delete_blob(bucket_name, blob_name):\r\n\r\n # initialize client, get bucket, & get blob\r\n _, _, blob = create_client(bucket_name, blob_name)\r\n\r\n # delete blob\r\n blob.delete()\r\n\r\n print(\"blob {} deleted\".format(blob_name))","def download_file(bucket, key, filename):\n validate_bucket_name(bucket)\n validate_key_name(key)\n client = get_client()\n\n client.download_file(bucket, key, filename)","def get_image(filename):\n\n client.download_file(S3_BUCKET, filename, 'uploads/{}'.format(filename))","def download_file(Bucket=None, Key=None, Filename=None, ExtraArgs=None, Callback=None, Config=None):\n pass","def get(self, bucket: str, object_name: str) -> bytes:\n raise NotImplementedError()","def OpenBlob(self, blob_key):\n return StringIO.StringIO(\n self._blobs[blobstore.BlobKey(unicode(blob_key))])","def download_object(self, bucket, key, dest_path) -> None:\n self.resource.Bucket(bucket).download_file(key, dest_path)","def get_file(self, file_name: str) -> BytesIO:\n fl = BytesIO()\n self.client.download_fileobj(self.bucket, file_name, fl)\n fl.seek(0)\n return fl","def download_file(bucket,file_name):\n with open(file_name, 'wb') as f:\n s3.download_fileobj(bucket, file_name,f)\n print(file_name, \": is downloaded\")","def fetch_and_extract(self, filename):\n # type: (Text) -> None\n\n blob = self.bucket.blob(filename)\n blob.download_to_filename(filename)\n\n with tarfile.open(filename, \"r:gz\") as tar:\n tar.extractall(self.data_dir)","def download_from_blob():\n block_blob_service = BlockBlobService(account_name='project3twitter',\n account_key='<YOUR_ACCOUNT_KEY>')\n container_name = 'project3'\n block_blob_service.set_container_acl(container_name, public_access=PublicAccess.Container)\n\n # actual download\n block_blob_service.get_blob_to_path(container_name, 'word-count.txt', 'resource/word-count.txt')","def delete_blob(bucket_name, blob_name):\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(blob_name)\n\n blob.delete()\n\n print('Blob {} deleted.'.format(blob_name))","def download_file_from_s3_public_bucket(bucket, object, output_file):\n botocore_config = Config(signature_version=UNSIGNED)\n s3_client = boto3.client(\"s3\", config=botocore_config)\n s3_client.download_file(bucket, object, output_file)","def _download_file(bucket: str, key: str) -> str:\n tmp_file_name = f\"/tmp/logs\"\n\n try:\n with open(tmp_file_name, \"wb\") as data:\n s3cl.download_fileobj(bucket, key, data)\n except Exception as e:\n print(type(e).__name__, e)\n f = open(tmp_file_name, \"w\")\n f.write(\"\")\n f.close()\n try:\n with gzip.open(tmp_file_name, mode=\"rt\") as f:\n x = f.read()\n return x\n except Exception as e:\n print(type(e).__name__, e, key)\n return \"\"","def loadBlob(self, oid, serial):\n start = time.time()\n try:\n blob_filename = self.storage.loadBlob(oid, serial)\n logger.debug('Fetched blob from ZEO in %ss' % (time.time() - start))\n except ZODB.POSException.POSKeyError:\n blob_filename = self.loadS3Blob(oid, serial)\n logger.debug('Fetched blob from S3 in %ss' % (time.time() - start))\n return blob_filename","def s3_download(path):\n with s3_read(path):\n # Reading the file will cache the file locally.\n pass","def download(self, bucket_name=None,\n object_key=None,\n dest=None):\n\n if bucket_name == None or \\\n object_key == None or \\\n dest == None:\n u_print(\" Error - argument is missing\")\n\n u_print_d('S3.download() - bucket=[{}] key=[{}] dest=[{}]'.format(bucket_name,\n object_key,\n dest))\n return self.s3.Object(bucket_name, object_key).download_file(dest)","def get_bytes(bucket: str, key: str) -> bytes:\n logger.debug(f'Reading from s3://{bucket}/{key}')\n response = client().get_object(Bucket=bucket, Key=key)\n return response['Body'].read()","def s3_get(url, temp_file):\n s3_resource = boto3.resource(\"s3\")\n bucket_name, s3_path = split_s3_path(url)\n s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)","def s3_get(url, temp_file):\n s3_resource = boto3.resource(\"s3\")\n bucket_name, s3_path = split_s3_path(url)\n s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)","async def fetch_file(self, download_url: str) -> bytes:\n log.debug(f\"Fetching file from branding repository: '{download_url}'.\")\n\n async with self.bot.http_session.get(download_url, params=PARAMS, headers=HEADERS) as response:\n if response.status != 200:\n raise RuntimeError(f\"Failed to fetch file due to status: {response.status}\")\n\n log.debug(\"Fetch successful, reading payload.\")\n return await response.read()","def download(self, file_url):\n url = self.base_url + \"/storage-service/cloud-storage/s3/file/download?url={0}\".format(file_url)\n\n headers = {\"ApiKey\": self.api_key}\n response = requests.get(url=url, headers=headers)\n return response","def get_blob(uuid, path=''):\n check_bundles_have_read_permission(local.model, request.user, [uuid])\n bundle = local.model.get_bundle(uuid)\n\n target_info = local.download_manager.get_target_info(uuid, path, 0)\n if target_info is None:\n abort(httplib.NOT_FOUND, 'Not found.')\n\n # Figure out the file name.\n if not path and bundle.metadata.name:\n filename = bundle.metadata.name\n else:\n filename = target_info['name']\n\n if target_info['type'] == 'directory':\n # Always tar and gzip directories.\n filename = filename + '.tar.gz'\n fileobj = local.download_manager.stream_tarred_gzipped_directory(uuid, path)\n elif target_info['type'] == 'file':\n if not zip_util.path_is_archive(filename) and request_accepts_gzip_encoding():\n # Let's gzip to save bandwidth. The browser will transparently decode\n # the file.\n filename = filename + '.gz'\n fileobj = local.download_manager.stream_file(uuid, path, gzipped=True)\n else:\n fileobj = local.download_manager.stream_file(uuid, path, gzipped=False)\n else:\n # Symlinks.\n abort(httplib.FORBIDDEN, 'Cannot download files of this type.')\n \n # Set headers.\n mimetype, _ = mimetypes.guess_type(filename, strict=False)\n response.set_header('Content-Type', mimetype or 'text/plain')\n if zip_util.get_archive_ext(filename) == '.gz' and request_accepts_gzip_encoding():\n filename = zip_util.strip_archive_ext(filename)\n response.set_header('Content-Encoding', 'gzip')\n else:\n response.set_header('Content-Encoding', 'identity')\n response.set_header('Content-Disposition', 'filename=\"%s\"' % filename)\n\n return fileobj","def get_s3_object(bucket, key_name, local_file):\n\n tracer.put_metadata('object', f's3://{bucket}/{key_name}')\n\n try:\n s3_resource.Bucket(bucket).download_file(key_name, local_file)\n result = 'ok'\n tracer.put_annotation('OBJECT_DOWNLOAD', 'SUCCESS')\n except botocore.exceptions.ClientError as e:\n tracer.put_annotation('OBJECT_DOWNLOAD', 'FAILURE')\n if e.response['Error']['Code'] == '404':\n result = f'Error: s3://{bucket}/{key_name} does not exist'\n else:\n result = f'Error: {str(e)}'\n\n return(result)","def _progress_blob(self, bucket, blob_name, progress_callback):\n return _ProgressBlob(\n name=blob_name,\n bucket=bucket,\n progress_callback=progress_callback\n )","def download_from_s3(s3_resource, photo):\n try:\n bucket, key = photo.replace(\"s3://\", \"\").split(\"/\", 1)\n local_file = os.path.basename(photo)\n except ValueError as err:\n logger.exception(\"Couldn't get S3 info for %s: %s\", photo)\n raise\n\n try:\n logger.info(\"Downloading %s\", photo)\n s3_resource.Bucket(bucket).download_file(key, local_file)\n except ClientError:\n logger.exception(\"Couldn't download %s from S3.\", photo)\n raise\n\n return local_file","def downloadObject(bucket:str, object:str, region:str, path:Path) -> None:\n client = boto3.client('s3', region_name=region)\n try:\n with open(str(path), 'wb') as data:\n client.download_fileobj(Bucket=bucket, Object=object, Data=data)\n except Exception as e:\n raise ObjectDownloadError(e)","def DownloadFile(self, gcs_file_name, io_base):\n bucket, bucket_path = self._ParseBucketAndPath(gcs_file_name)\n\n # Check the size of the remote file. If it's empty, we have to return early\n # because the chunked downloader will crash. There aren't any contents to\n # retrieve in that case, anyway.\n object_data = self._RunWithRetries(\n self._service.objects().get(bucket=bucket, object=bucket_path).execute,\n self._CommonErrorMatcher)\n if ('name' not in object_data or object_data['name'] != bucket_path\n or 'size' not in object_data):\n raise CloudStorageApiError('Object data for %s is malformed.' %\n GcsPath(bucket, bucket_path))\n if int(object_data['size']) == 0:\n return\n\n request = self._service.objects().get_media(bucket=bucket,\n object=bucket_path)\n downloader = gapi_http.MediaIoBaseDownload(\n io_base, request, chunksize=1024*1024)\n done = False\n while not done:\n # The first return value indicates download progress, which we won't do\n # anything fancy with for now.\n _, done = self._RunWithRetries(downloader.next_chunk,\n self._CommonErrorMatcher)","def generate_download_signed_url_v4(bucket_name, blob_name):\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(blob_name)\n\n url = blob.generate_signed_url(\n version=\"v4\",\n # This URL is valid for 15 minutes\n expiration=datetime.timedelta(minutes=30),\n # Allow GET requests using this URL.\n method=\"GET\",\n )\n\n return url","def download_from_gcs(gcs_uri, target_path):\n bucket, blob_name = gcs_uri.replace('gs://', '').split('/', 1)\n client = storage.Client(project='embdatalab')\n bucket = client.get_bucket(bucket)\n prefix = blob_name.split('*')[0]\n unzipped = open(target_path, 'w')\n cmd = \"gunzip -c -f %s >> %s\"\n for blob in bucket.list_blobs(prefix=prefix):\n with tempfile.NamedTemporaryFile(mode='rb+') as f:\n logger.info(\"Downloading %s to %s\" % (blob.path, f.name))\n blob.chunk_size = 2 ** 30\n blob.download_to_file(f)\n f.flush()\n f.seek(0)\n subprocess.check_call(\n cmd % (f.name, unzipped.name), shell=True)\n return unzipped.name","def download_file(cls, uri, fobj):\n # Breaks the URI into usable componenents.\n values = get_values_from_media_uri(uri)\n\n conn = cls._get_aws_s3_connection(values['username'],\n values['password'])\n bucket = conn.get_bucket(values['host'])\n key = bucket.get_key(values['path'])\n\n logger.debug(\"S3Backend.download_file(): \" \\\n \"Downloading: %s\" % uri)\n\n try:\n key.get_contents_to_file(fobj)\n except AttributeError:\n # Raised by ResumableDownloadHandler in boto when the given S3\n # key can't be found.\n message = \"The specified input file cannot be found.\"\n raise InfileNotFoundException(message)\n\n logger.debug(\"S3Backend.download_file(): \" \\\n \"Download of %s completed.\" % uri)\n return fobj","def get_s3_object(self, remote_s3_url):\n try:\n _file = tempfile.mkstemp()[1]\n parsed_s3_path = remote_s3_url.split(\"/\", 3) # s3://bucket-name/key\n remote_bucket = parsed_s3_path[2] # Bucket name\n remote_key = parsed_s3_path[3] # Key\n self.download_file(remote_bucket, remote_key, _file)\n return _file\n except Exception as e:\n message = {'FILE': __file__.split('/')[-1],\n 'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}\n self.logger.exception(message)\n raise","def do_part_download(args):\r\n bucket_name, key_name, fname, min_byte, max_byte, split, secure, max_tries, current_tries = args\r\n conn = boto.connect_s3(calling_format=OrdinaryCallingFormat())\r\n conn.is_secure = secure\r\n\r\n # Make the S3 request\r\n resp = conn.make_request(\"GET\", bucket=bucket_name,\r\n key=key_name, headers={'Range':\"bytes=%d-%d\" % (min_byte, max_byte)})\r\n\r\n # Open the target file, seek to byte offset\r\n fd = os.open(fname, os.O_WRONLY)\r\n logger.debug(\"Opening file descriptor %d, seeking to %d\" % (fd, min_byte))\r\n os.lseek(fd, min_byte, os.SEEK_SET)\r\n\r\n chunk_size = min((max_byte-min_byte), split*1024*1024)\r\n logger.debug(\"Reading HTTP stream in %dM chunks\" % (chunk_size/1024./1024))\r\n t1 = time.time()\r\n s = 0\r\n try:\r\n while True:\r\n data = resp.read(chunk_size)\r\n if data == \"\":\r\n break\r\n os.write(fd, data)\r\n s += len(data)\r\n t2 = time.time() - t1\r\n os.close(fd)\r\n s = s / 1024 / 1024.\r\n logger.debug(\"Downloaded %0.2fM in %0.2fs at %0.2fMBps\" % (s, t2, s/t2))\r\n except Exception, err:\r\n logger.debug(\"Retry request %d of max %d times\" % (current_tries, max_tries))\r\n if (current_tries > max_tries):\r\n logger.error(err)\r\n else:\r\n time.sleep(3)\r\n current_tries += 1\r\n do_part_download(bucket_name, key_name, fname, min_byte, max_byte, split, secure, max_tries, current_tries)","def download(\n bucket: str, key: str, file_path: str, session: Optional[boto3.Session] = None\n) -> str:\n s3_client = _get_client(session)\n\n LOGGER.info(\"downloading s3://%s/%s to %s...\", bucket, key, file_path)\n s3_client.download_file(Bucket=bucket, Key=key, Filename=file_path)\n return file_path","def download_fileobj(Fileobj=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\n pass","def get(self, key):\n key = str(urllib.unquote(key)).strip()\n logging.debug('key is %s', key)\n blob_info = blobstore.BlobInfo.get(key)\n self.send_blob(blob_info)","def load(self, bucket, key):\n\n bucket = self._build_bucket_resource(bucket)\n\n with io.BytesIO() as stream:\n bucket.download_fileobj(key, stream)\n stream.seek(0)\n\n wrapper = io.TextIOWrapper(stream, encoding='utf-8')\n # Preserve the original order\n return json.load(wrapper, object_pairs_hook=collections.OrderedDict)","def _s3_get_file(url):\n try:\n return S3().get_contents_from_url(url)\n except Exception as e:\n raise ScrBaseException(\"Could not load file from {0}: {1}\".format(url, e))","def s3_get(url, temp_file, proxies=None):\n\ts3_resource = boto3.resource (\"s3\", config=Config (proxies=proxies))\n\tbucket_name, s3_path = split_s3_path (url)\n\ts3_resource.Bucket (bucket_name).download_fileobj (s3_path, temp_file)","def list_blobs(bucket_name):\n # bucket_name = \"your-bucket-name\"\n\n storage_client = storage.Client()\n print(storage_client.current_batch)\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n # print(len([1 for blob in blobs]))\n for blob in blobs:\n print(blob.name)","def get_blob_content(container_name: str, blob_path: str) -> str:\n global BLOB_SERVICE_CLIENT\n # TODO: Should add retry policy here\n if not BLOB_SERVICE_CLIENT:\n logging.info(\n f\"{HEADER} Initialize blob service client for {DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL}\")\n BLOB_SERVICE_CLIENT = BlobServiceClient(DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL,\n credential=DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN)\n blob_client = BLOB_SERVICE_CLIENT.get_blob_client(container=container_name, blob=blob_path)\n content = blob_client.download_blob().content_as_text()\n return content","def load_pickle(self, bucket, key):\n\n with BytesIO() as obj_buffer:\n self._s3.Bucket(bucket).download_fileobj(key, obj_buffer)\n obj_buffer.seek(0)\n obj = pickle.load(obj_buffer)\n\n return obj","def copy_blob(bucket_name, blob_name, destination_bucket_name, destination_blob_name):\r\n\r\n # initialize client, get bucket, & get blob\r\n storage_client, source_bucket, source_blob = create_client(bucket_name, blob_name)\r\n\r\n # set destination bucket name\r\n destination_bucket = storage_client.bucket(destination_bucket_name)\r\n\r\n # copy blob\r\n blob_copy = source_bucket.copy_blob(\r\n source_blob, destination_bucket, destination_blob_name\r\n )\r\n\r\n print(\r\n \"blob {} in bucket {} copied to blob {} in bucket {}.\".format(\r\n source_blob.name,\r\n source_bucket.name,\r\n blob_copy.name,\r\n destination_bucket.name,\r\n )\r\n )","def get_file(object_name: str, **kwargs) -> HTTPResponse:\n data = client.get_object(DATASETS_BUCKET, object_name, **kwargs)\n return data","def get_object(self, bucket_name, key, stream=False, extra_get_args={}):\n url = self.__key_url(bucket_name, key)\n res = self.infinispan_client.get(url, headers=self.headers, auth=self.basicAuth)\n data = res.content\n return data","async def read(self, size=-1):\n # read the object using the bucket and path already determined in\n # __init__, and using the connection object\n try:\n # get the file size first\n file_size = await self._getsize()\n if size== -1:\n range_start = 0\n range_end = file_size\n range_size = file_size\n else:\n range_start = self._seek_pos\n range_end = self._seek_pos+size-1\n if range_end > file_size:\n range_end = file_size-1\n range_size = range_end-range_start+1\n\n # if multipart download is not supported\n if not self._multipart_download:\n # get the full file\n s3_object = await self._conn_obj.conn.get_object(\n Bucket = self._bucket,\n Key = self._path,\n )\n body = s3_object['Body']\n data = await body.read()\n # if the file is smaller than the MAXIMUM_PART_SIZE\n elif (range_size < self._part_size):\n # the requested range is the full file, it is fastest to\n # not specify the range\n if (range_start == 0 and range_size == file_size):\n # get the full file\n s3_object = await self._conn_obj.conn.get_object(\n Bucket = self._bucket,\n Key = self._path,\n )\n # a portion of the file is requested\n else:\n s3_object = await self._conn_obj.conn.get_object(\n Bucket = self._bucket,\n Key = self._path,\n Range = 'bytes={}-{}'.format(\n range_start, range_end\n )\n )\n body = s3_object['Body']\n data = await body.read()\n # multipart download version\n else:\n \"\"\"Use range get to split up a file into the MAXIMUM_PART_SIZE\n and download each part asynchronously.\"\"\"\n # calculate the number of necessary parts\n n_parts = int(range_size / self._part_size + 1)\n # don't go above the maximum number downloadable\n if n_parts > self._max_parts:\n n_parts = self._max_parts\n # (re)calculate the download size\n part_size = float(range_size) / n_parts\n # create the tasks and assign the return data buffer\n tasks = []\n data_buf = io.BytesIO()\n\n for p in range(0, n_parts):\n event_loop = asyncio.get_event_loop()\n task = event_loop.create_task(self._read_partial_file(\n p, part_size\n ))\n tasks.append(task)\n # wait for all the tasks to finish\n results = await asyncio.gather(*tasks)\n # read each chunk of data and write into the global buffer\n for r in results:\n data_buf.write(r)\n r = None # indicate ready for garbage collection\n data_buf.seek(0)\n data = data_buf.read()\n\n except ClientError as e:\n raise IOException(\n \"Could not read from object {} {}\".format(self._path, e)\n )\n except AttributeError as e:\n self._handle_connection_exception(e)\n return data","def new_blob(self, blob_name):\n return storage.Blob(blob_name, self.bucket)","def download_file(file_name, bucket_name, object_name=None):\n # If S3 object_name was not specified, use file_name\n if object_name is None:\n object_name = os.path.basename(file_name)\n\n try:\n # Download file from bucket\n response = s3.download_file(Bucket=bucket_name, Key=object_name, Filename=file_name)\n except ClientError as e:\n logging.error(e)\n return False\n return True","def _do_retrieve(bucket_name, key_path, number_retries=DEFAULT_S3_RETRIES):\n try:\n return conn.get_object(Bucket=bucket_name, Key=key_path, ResponseContentType='string')\n except Exception:\n if number_retries > 0:\n print(\"s3_retrieve failed with incomplete read, retrying on %s\" % key_path)\n return _do_retrieve(bucket_name, key_path, number_retries=number_retries - 1)\n raise","def get_file_s3(bucket, key):\n \n client = boto3.client('s3')\n return client.get_object(Bucket=bucket, Key=key)['Body'].read().decode('utf-8')","def download(url, bucket_id, key_prefix):\n\n baseFile = '_'.join(url.split('/')[-4:]) #os.path.basename(url)\n\n #move the file to a more uniq path\n os.umask(0002)\n temp_path = \"/tmp/\"\n file = os.path.join(temp_path,baseFile)\n bucket = conn.get_bucket(bucket_id)\n key = bucket.get_key(key_prefix + baseFile, validate=False)\n s3_exists = key.exists()\n file_exists = os.path.isfile(file)\n \n if not file_exists and s3_exists:\n sys.stderr.write(\"Downloading %s from S3\\n\"%url)\n key.get_contents_to_filename(file)\n sys.stderr.write(\"Downloaded %s from S3\\n\"%url)\n elif not file_exists and not s3_exists:\n sys.stderr.write(\"Downloading %s from the web\\n\"%url)\n try:\n req = urllib2.urlopen(url)\n total_size = int(req.info().getheader('Content-Length').strip())\n downloaded = 0\n CHUNK = 256 * 10240\n with open(file, 'wb') as fp:\n while True:\n chunk = req.read(CHUNK)\n downloaded += len(chunk)\n #print math.floor( (downloaded / total_size) * 100 )\n if not chunk: break\n fp.write(chunk)\n except urllib2.HTTPError, e:\n sys.stderr.write(\"HTTP Error: %s %s\\n\"%(e.code , url))\n return False\n except urllib2.URLError, e:\n sys.stderr.write(\"URL Error: %s %s\\n\"%(e.reason , url))\n return False\n sys.stderr.write(\"Downloaded %s from the web\\n\"%url)\n\n if not s3_exists:\n sys.stderr.write(\"Uploading %s to S3\\n\"%url)\n key.set_contents_from_filename(file)\n\n sys.stderr.write(\"File ready: %s\\n\"%url)\n return file","def delete_blob(blob_name):\n # bucket_name = \"your-bucket-name\"\n # blob_name = \"your-object-name\"\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(blob_name)\n blob.delete()\n print('Blob {} deleted.'.format(blob_name))","def list_blobs(bucket_name):\n storage_client = storage.Client()\n bucket = storage_client.get_bucket(bucket_name)\n\n blobs = bucket.list_blobs()\n list_blobs = [blob.public_url for blob in blobs]\n return list_blobs","async def _download(self) -> None:\n\n # do request\n async with aiohttp.ClientSession() as session:\n async with session.get(self.url, auth=self._auth, timeout=self._timeout) as response:\n # check response\n if response.status == 200:\n # get data and return it\n self._buffer = await response.read()\n elif response.status == 401:\n log.error(\"Wrong credentials for downloading file.\")\n raise FileNotFoundError\n else:\n log.error(\"Could not download file from filecache.\")\n raise FileNotFoundError","def _torrent_for_blob(blob, is_public):\n # Make sure the storage has a size.\n if not blob.compressed_size:\n abort(404)\n\n # Lookup the torrent information for the storage.\n torrent_info = registry_model.get_torrent_info(blob)\n if torrent_info is None:\n abort(404)\n\n # Lookup the webseed path for the storage.\n webseed = storage.get_direct_download_url(\n blob.placements, blob.storage_path, expires_in=app.config[\"BITTORRENT_WEBSEED_LIFETIME\"]\n )\n if webseed is None:\n # We cannot support webseeds for storages that cannot provide direct downloads.\n exact_abort(501, \"Storage engine does not support seeding.\")\n\n # Load the config for building torrents.\n torrent_config = TorrentConfiguration.from_app_config(instance_keys, app.config)\n\n # Build the filename for the torrent.\n if is_public:\n name = public_torrent_filename(blob.uuid)\n else:\n user = get_authenticated_user()\n if not user:\n abort(403)\n\n name = per_user_torrent_filename(torrent_config, user.uuid, blob.uuid)\n\n # Return the torrent file.\n torrent_file = make_torrent(\n torrent_config,\n name,\n webseed,\n blob.compressed_size,\n torrent_info.piece_length,\n torrent_info.pieces,\n )\n\n headers = {\n \"Content-Type\": \"application/x-bittorrent\",\n \"Content-Disposition\": \"attachment; filename={0}.torrent\".format(name),\n }\n\n return make_response(torrent_file, 200, headers)","def fetch(self, url) -> bytes:\n buffer = self.download(url)\n zfs = ZipFileSystem(buffer, \"r\")\n return zfs.open(zfs.glob(\"*\")[0]).read()","def list_blobs(bucket):\n bucket = default_bucket if bucket is None else bucket\n bucket_name = bucket if isinstance(bucket, str) else bucket.name\n blobs = gcs.list_blobs(bucket_name)\n return blobs","def download_file_from_icos(icos_obj, bucket: str, local_file_name: str, key: str) -> None:\r\n try:\r\n icos_obj.download_file(Bucket=bucket, Key=key, Filename=local_file_name)\r\n except Exception as e:\r\n print(Exception, e)\r\n else:\r\n print('File `{}` downloaded from ICOS and saved locally as `{}`.'.format(key, local_file_name))","def download(self, key, filename):\n self.resource.Bucket(self.bucket).download_file(key, filename)\n return filename","def download_chain(s3_path, local_path, bucket_name='lwr-inverse-us-east'):\n s3 = boto3.resource(\"s3\")\n lwr_AIES = s3.Bucket(bucket_name)\n try:\n lwr_AIES.download_file(Key=s3_path, Filename=local_path)\n print(\"Download successful\")\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == \"404\":\n print(\"The object does not exist.\")\n else:\n raise","def list_blobs(bucket_name):\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n return blobs","def upload_to_bucket(blob_name, file, bucket_name):\n\n # Explicitly use service account credentials by specifying the private key\n # file.\n storage_client = storage.Client.from_service_account_json('creds.json')\n\n bucket = storage_client.get_bucket(bucket_name)\n blob = bucket.blob(blob_name)\n blob.upload_from_file(file)\n\n # returns a public url\n return blob.public_url","def get_file_download(self, bucket_id, file_id):\n\n \n path = '/storage/buckets/{bucketId}/files/{fileId}/download'\n params = {}\n if bucket_id is None:\n raise AppwriteException('Missing required parameter: \"bucket_id\"')\n\n if file_id is None:\n raise AppwriteException('Missing required parameter: \"file_id\"')\n\n path = path.replace('{bucketId}', bucket_id)\n path = path.replace('{fileId}', file_id)\n\n\n return self.client.call('get', path, {\n 'content-type': 'application/json',\n }, params)","def download_from_s3(self, filename: str, filename_output: Optional[str] = None) -> str:\n if self.aws_access_key_id is None:\n raise Exception(\n 'To use `download_from_s3` you need to pass '\n '`aws_access_key_id` and '\n '`aws_secret_access_key`'\n )\n\n s3 = boto3.client('s3',\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key)\n\n # Progress bar\n meta_data = s3.head_object(Bucket=self.bucket_name, Key=filename)\n size = int(meta_data.get('ContentLength', 0))\n progress_bar = self._progress(size)\n\n # Downloading file\n s3.download_file(self.bucket_name, filename,\n filename if filename_output is None else filename_output,\n Callback=progress_bar)","def download(self, bucket_name, key_name, fname):\n dname = os.path.dirname(fname)\n if dname and not os.path.exists(dname):\n os.makedirs(dname)\n bucket = self.s3_.get_bucket(bucket_name)\n key = bucket.get_key(key_name)\n return key.get_contents_to_filename(fname)","def fetch(iid):\n if AWS_CLIENT_CONFIG and BUCKET_NAME:\n try:\n s3 = boto3.resource('s3', **AWS_CLIENT_CONFIG)\n obj = s3.Bucket(BUCKET_NAME).Object(iid).get()\n if obj:\n return obj.get('Body')\n except botocore.exceptions.ClientError as e:\n logger.error(e)\n else:\n # get locally from temp dir (tests, local development)\n return get_temp_file(iid)\n return None","def fetch_and_extract(self, filename):\n # type: (Text) -> None\n\n with io.open(filename, 'wb') as f:\n self.bucket.download_fileobj(filename, f)\n with tarfile.open(filename, \"r:gz\") as tar:\n tar.extractall(self.data_dir)","def get_object(self, container_name, object_name, download_path):\n obj = self.client.get_object(container_name, object_name)\n with open(download_path + object_name, 'w') as test_file:\n test_file.write(obj[1])\n return True","def _download(self):\n self._system.download_file(\"http://curl.haxx.se/download/\" + self._tar_name)","def download(handle):\n storage = get_storage()\n # FIXME: 404 if not found or invalid?\n return storage.route(handle)","def get_remote_bytes(file_url) -> io.BytesIO:\n result = urlfetch.fetch(file_url)\n return io.BytesIO(result.content)"],"string":"[\n \"def download_blob(bucket_name, source_blob_name):\\n\\n storage_client = storage.Client()\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n\\n return blob\",\n \"def blob_download(blob_url):\\n blob = storage.Object.from_url(blob_url)\\n blobc = blob.download()\\n return blobc\",\n \"def download_blob(bucket_name, source_blob_name):\\n storage_client = storage.Client()\\n\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n return blob.download_as_string().decode()\",\n \"def download_blob(url: str) -> io.BytesIO:\\n storage_client = storage.Client()\\n bucket_name = get_bucket_name(url)\\n source_blob_name = get_blob_name(url)\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n f = io.BytesIO(blob.download_as_bytes())\\n return f\",\n \"def download_bucket(blob_name, path_to_file):\\r\\n blob = bucket.blob(blob_name)\\r\\n blob.download_to_filename(path_to_file)\",\n \"def download_specific_blob(bucket_name, path_to_storage_file_name, download_file_name):\\r\\n\\r\\n try:\\r\\n # initialize client & get blob\\r\\n _, _, blob = create_client(bucket_name, path_to_storage_file_name)\\r\\n\\r\\n # set the path to source file\\r\\n blob.download_to_filename(download_file_name)\\r\\n \\r\\n except Exception as err:\\r\\n raise err\\r\\n sys.exit(1)\\r\\n \\r\\n else:\\r\\n print(f\\\"download blob '{path_to_storage_file_name}' succeed\\\")\\r\\n\\r\\n return None\",\n \"def get_blob(self, download_meta):\\n bucket_name, key = self._get_bucket_key(download_meta)\\n response = self.s3.get_object(Bucket=bucket_name,\\n Key=key)\\n return response['Body'].read().decode()\",\n \"def download_blob(bucket_name, source_blob_name, destination_file_name):\\n storage_client = storage.Client()\\n try:\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n \\n blob.download_to_filename(destination_file_name)\\n \\n print('Blob {} downloaded to {}.'.format(\\n source_blob_name,\\n destination_file_name)) \\n except:\\n print(\\\"User does not have access to that bucket. Trying public link:\\\")\\n gcs_url = 'https://%(bucket)s.storage.googleapis.com/%(file)s' % {'bucket':bucket_name, 'file':source_blob_name}\\n urllib.urlretrieve(gcs_url, destination_file_name)\\n print (\\\"Download complete\\\")\",\n \"def downloadBlob(self, oid, serial):\\n\\n key = s3_blob_filename(oid, serial)\\n\\n # Confirm blob cache directory is locked for writes\\n cache_filename = self.fshelper.getBlobFilename(oid, serial)\\n lock_filename = os.path.join(os.path.dirname(cache_filename), '.lock')\\n assert os.path.exists(lock_filename)\\n\\n # Download\\n self.bucket.download_file(key, cache_filename)\\n os.chmod(cache_filename, stat.S_IREAD)\\n\\n # Cache bookkeeping\\n self._blob_data_bytes_loaded += os.path.getsize(cache_filename)\\n self._check_blob_size(self._blob_data_bytes_loaded)\",\n \"def download_blob(bucket_name, source_blob_name, destination_file_name):\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n\\n blob.download_to_filename(destination_file_name)\\n\\n print('Blob {} downloaded to {}.'.format(\\n source_blob_name,\\n destination_file_name))\",\n \"def get_blob(self, blob_name):\\n return self.bucket.get_blob(blob_name)\",\n \"def download(self, bucket, object, filename=None):\\n service = self.get_conn()\\n downloaded_file_bytes = service \\\\\\n .objects() \\\\\\n .get_media(bucket=bucket, object=object) \\\\\\n .execute()\\n\\n # Write the file to local file path, if requested.\\n if filename:\\n write_argument = 'wb' if isinstance(downloaded_file_bytes, bytes) else 'w'\\n with open(filename, write_argument) as file_fd:\\n file_fd.write(downloaded_file_bytes)\\n\\n return downloaded_file_bytes\",\n \"def download_blob(bucket_name, source_blob_name, destination_file_name):\\n # bucket_name = \\\"your-bucket-name\\\"\\n # source_blob_name = \\\"storage-object-name\\\"\\n # destination_file_name = \\\"local/path/to/file\\\"\\n\\n bucket = storage_client.bucket(bucket_name)\\n blobs = storage_client.list_blobs(bucket_name)\\n for blob in blobs:\\n print(blob.name)\\n if (blob.name == source_blob_name):\\n # Construct a client side representation of a blob.\\n # Note `Bucket.blob` differs from `Bucket.get_blob` as it doesn't retrieve\\n # any content from Google Cloud Storage. As we don't need additional data,\\n # using `Bucket.blob` is preferred here.\\n blob = bucket.blob(source_blob_name)\\n blob.download_to_filename(destination_file_name)\\n \\n print(\\n \\\"Blob {} downloaded to {}.\\\".format(\\n source_blob_name, destination_file_name\\n )\\n )\",\n \"def download_blob(bucket_name, source_blob_name, destination_file_name):\\n # The ID of your GCS bucket\\n # bucket_name = \\\"your-bucket-name\\\"\\n\\n # The ID of your GCS object\\n # source_blob_name = \\\"storage-object-name\\\"\\n\\n # The path to which the file should be downloaded\\n # destination_file_name = \\\"local/path/to/file\\\"\\n\\n storage_client = storage.Client()\\n\\n bucket = storage_client.bucket(bucket_name)\\n\\n # Construct a client side representation of a blob.\\n # Note `Bucket.blob` differs from `Bucket.get_blob` as it doesn't retrieve\\n # any content from Google Cloud Storage. As we don't need additional data,\\n # using `Bucket.blob` is preferred here.\\n blob = bucket.blob(source_blob_name)\\n blob.download_to_filename(destination_file_name)\\n\\n print(\\n \\\"Downloaded storage object {} from bucket {} to local file {}.\\\".format(\\n source_blob_name, bucket_name, destination_file_name))\",\n \"def download_blob(source_blob_name, destination_file_name, bucket_name=\\\"bts-ml-data\\\"):\\n # bucket_name = \\\"your-bucket-name\\\"\\n # source_blob_name = \\\"storage-object-name\\\"\\n # destination_file_name = \\\"local/path/to/file\\\"\\n\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(source_blob_name)\\n\\n blob.download_to_filename(destination_file_name)\\n\\n print(\\n \\\"Blob {} downloaded to {}.\\\".format(\\n source_blob_name, destination_file_name\\n )\\n )\",\n \"def run(\\n self,\\n bucket: str = None,\\n blob: str = None,\\n project: str = None,\\n chunk_size: int = None,\\n credentials: dict = None,\\n encryption_key: str = None,\\n request_timeout: Union[float, Tuple[float, float]] = 60,\\n ) -> str:\\n # create client\\n client = get_storage_client(project=project, credentials=credentials)\\n\\n # retrieve bucket\\n bucket = self._retrieve_bucket(\\n client=client, bucket=bucket, create_bucket=False\\n )\\n\\n # identify blob name\\n blob = self._get_blob(\\n bucket,\\n blob,\\n chunk_size=chunk_size,\\n encryption_key=encryption_key,\\n )\\n # Support GCS < 1.31\\n return (\\n blob.download_as_bytes(timeout=request_timeout)\\n if hasattr(blob, \\\"download_as_bytes\\\")\\n else blob.download_as_string(timeout=request_timeout)\\n )\",\n \"def download(bucket, key):\\n validate_bucket_name(bucket)\\n validate_key_name(key)\\n client = get_client()\\n\\n # do a buffered download\\n bytes_io = io.BytesIO()\\n client.download_fileobj(bucket, key, bytes_io)\\n\\n # hope that stuff is not too big, and just return content\\n return bytes_io.getvalue()\",\n \"def get_blob(self, download_meta):\\n blob_id = download_meta['blob_id']\\n if isinstance(blob_id, str):\\n blob_id = uuid.UUID(blob_id)\\n session = self.DBSession()\\n blob = session.get(Blob, blob_id) # was session.query(Blob).get(blob_id), rewritten for SA2.0\\n return blob.data\",\n \"def _retrieve_blob(self, object_key):\\n return self.s3_resource.Object(self.CVE_BUCKET, object_key).get()['Body'].read()\",\n \"def download(self, bucket_name, file_name, file_path):\\n\\n self.client.download_file(bucket_name, file_name, file_path)\",\n \"def loadS3Blob(self, oid, serial):\\n\\n # Check if it's already in the cache\\n cache_filename = self.fshelper.getBlobFilename(oid, serial)\\n if os.path.exists(cache_filename):\\n return ZEO.ClientStorage._accessed(cache_filename)\\n\\n # If not, download from S3...\\n # First, we'll create the directory for this oid, if it doesn't exist.\\n self.fshelper.createPathForOID(oid)\\n\\n # OK, it's not here and we (or someone) needs to get it. We\\n # want to avoid getting it multiple times. We want to avoid\\n # getting it multiple times even accross separate client\\n # processes on the same machine. We'll use file locking.\\n\\n lock = ZEO.ClientStorage._lock_blob(cache_filename)\\n try:\\n # We got the lock, so it's our job to download it. First,\\n # we'll double check that someone didn't download it while we\\n # were getting the lock:\\n\\n if os.path.exists(cache_filename):\\n return ZEO.ClientStorage._accessed(cache_filename)\\n\\n # Actually download the blob. When this function\\n # returns, it will have been sent. (The receiving will\\n # have been handled by the asyncore thread.)\\n self.downloadBlob(oid, serial)\\n\\n if os.path.exists(cache_filename):\\n return ZEO.ClientStorage._accessed(cache_filename)\\n\\n raise ZODB.POSException.POSKeyError(\\\"No blob file\\\", oid, serial)\\n finally:\\n lock.close()\",\n \"def download_file(self, bucket_name, object_name, file_name):\\n self._client.download_file(bucket_name, object_name, file_name)\",\n \"def download_blob(blob, file_group, destination, blob_service, progress_callback):\\n\\n def _wrap_callback(curr, total):\\n if progress_callback:\\n progress_callback(curr, total, destination)\\n\\n blob_service.get_blob_to_path(\\n get_container_name(file_group), blob, destination,\\n progress_callback=_wrap_callback)\",\n \"def __retrieve_from_bucket(fname):\\n blob = BUCKET.blob(fname)\\n json_data = json.loads(blob.download_as_string())\\n return json_data\",\n \"def urlgrab(self, url, filename=None, **kwargs):\\n blob_location = \\\"%s/%s\\\" % (self.base_path, url)\\n self.verbose_logger.info(\\\"downloading gs://%s/%s to %s\\\" % (self.bucket.name, blob_location, filename))\\n url = url.lstrip('/')\\n if not filename:\\n filename = url\\n\\n blob = storage.blob.Blob(name=blob_location,bucket = self.bucket)\\n blob.download_to_filename(filename)\\n return filename\",\n \"def get_raw(key: str, bucket: google.cloud.storage.bucket.Bucket) -> bytes:\\n blob = google.cloud.storage.blob.Blob(name=key, bucket=bucket)\\n return blob.download_as_string()\",\n \"def get_blob_url(self, download_meta):\\n bucket_name, key = self._get_bucket_key(download_meta)\\n location = self.s3.generate_presigned_url(\\n ClientMethod='get_object',\\n ExpiresIn=36*60*60,\\n Params={'Bucket': bucket_name, 'Key': key})\\n return location\",\n \"def _download_from_bucket(self, ext_filename, local_filename, force=False):\\n if os.path.exists(local_filename) and not force:\\n logging.info('File {} already exists. Not overwriting...'.format(local_filename))\\n return\\n if os.path.exists(local_filename) and force:\\n logging.info('File {} already exists. Overwriting...'.format(local_filename))\\n else:\\n logging.info('File {} does not exist. Downloading...'.format(local_filename))\\n\\n Path(os.path.dirname(local_filename)).mkdir(parents=True, exist_ok=True)\\n\\n if self.s3:\\n self.bucket.download_file(ext_filename, local_filename)\\n logging.info('Downloaded {} to {}'.format(ext_filename, local_filename))\\n if self.gs:\\n try:\\n client = storage.Client()\\n bucket = client.get_bucket(self.bucket_name)\\n blob = storage.Blob(ext_filename, bucket)\\n blob.download_to_filename(local_filename)\\n logging.info('Downloaded {} to {}'.format(ext_filename, local_filename))\\n except:\\n logging.warning('Downloading failed')\\n\\n i += 1\",\n \"def download_file(bucket_name: str, object_name: str, file_path: str):\\n # pylint: disable=import-outside-toplevel\\n from sotaque_brasileiro.utils import safe_getenv\\n\\n minio_client = Minio(\\n safe_getenv(constants.MINIO_ENDPOINT.value),\\n access_key=safe_getenv(constants.MINIO_ACCESS_KEY.value),\\n secret_key=safe_getenv(constants.MINIO_SECRET_KEY.value),\\n )\\n minio_client.fget_object(bucket_name, object_name, file_path)\",\n \"def delete_blob(bucket_name, blob_name):\\r\\n\\r\\n # initialize client, get bucket, & get blob\\r\\n _, _, blob = create_client(bucket_name, blob_name)\\r\\n\\r\\n # delete blob\\r\\n blob.delete()\\r\\n\\r\\n print(\\\"blob {} deleted\\\".format(blob_name))\",\n \"def download_file(bucket, key, filename):\\n validate_bucket_name(bucket)\\n validate_key_name(key)\\n client = get_client()\\n\\n client.download_file(bucket, key, filename)\",\n \"def get_image(filename):\\n\\n client.download_file(S3_BUCKET, filename, 'uploads/{}'.format(filename))\",\n \"def download_file(Bucket=None, Key=None, Filename=None, ExtraArgs=None, Callback=None, Config=None):\\n pass\",\n \"def get(self, bucket: str, object_name: str) -> bytes:\\n raise NotImplementedError()\",\n \"def OpenBlob(self, blob_key):\\n return StringIO.StringIO(\\n self._blobs[blobstore.BlobKey(unicode(blob_key))])\",\n \"def download_object(self, bucket, key, dest_path) -> None:\\n self.resource.Bucket(bucket).download_file(key, dest_path)\",\n \"def get_file(self, file_name: str) -> BytesIO:\\n fl = BytesIO()\\n self.client.download_fileobj(self.bucket, file_name, fl)\\n fl.seek(0)\\n return fl\",\n \"def download_file(bucket,file_name):\\n with open(file_name, 'wb') as f:\\n s3.download_fileobj(bucket, file_name,f)\\n print(file_name, \\\": is downloaded\\\")\",\n \"def fetch_and_extract(self, filename):\\n # type: (Text) -> None\\n\\n blob = self.bucket.blob(filename)\\n blob.download_to_filename(filename)\\n\\n with tarfile.open(filename, \\\"r:gz\\\") as tar:\\n tar.extractall(self.data_dir)\",\n \"def download_from_blob():\\n block_blob_service = BlockBlobService(account_name='project3twitter',\\n account_key='<YOUR_ACCOUNT_KEY>')\\n container_name = 'project3'\\n block_blob_service.set_container_acl(container_name, public_access=PublicAccess.Container)\\n\\n # actual download\\n block_blob_service.get_blob_to_path(container_name, 'word-count.txt', 'resource/word-count.txt')\",\n \"def delete_blob(bucket_name, blob_name):\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(blob_name)\\n\\n blob.delete()\\n\\n print('Blob {} deleted.'.format(blob_name))\",\n \"def download_file_from_s3_public_bucket(bucket, object, output_file):\\n botocore_config = Config(signature_version=UNSIGNED)\\n s3_client = boto3.client(\\\"s3\\\", config=botocore_config)\\n s3_client.download_file(bucket, object, output_file)\",\n \"def _download_file(bucket: str, key: str) -> str:\\n tmp_file_name = f\\\"/tmp/logs\\\"\\n\\n try:\\n with open(tmp_file_name, \\\"wb\\\") as data:\\n s3cl.download_fileobj(bucket, key, data)\\n except Exception as e:\\n print(type(e).__name__, e)\\n f = open(tmp_file_name, \\\"w\\\")\\n f.write(\\\"\\\")\\n f.close()\\n try:\\n with gzip.open(tmp_file_name, mode=\\\"rt\\\") as f:\\n x = f.read()\\n return x\\n except Exception as e:\\n print(type(e).__name__, e, key)\\n return \\\"\\\"\",\n \"def loadBlob(self, oid, serial):\\n start = time.time()\\n try:\\n blob_filename = self.storage.loadBlob(oid, serial)\\n logger.debug('Fetched blob from ZEO in %ss' % (time.time() - start))\\n except ZODB.POSException.POSKeyError:\\n blob_filename = self.loadS3Blob(oid, serial)\\n logger.debug('Fetched blob from S3 in %ss' % (time.time() - start))\\n return blob_filename\",\n \"def s3_download(path):\\n with s3_read(path):\\n # Reading the file will cache the file locally.\\n pass\",\n \"def download(self, bucket_name=None,\\n object_key=None,\\n dest=None):\\n\\n if bucket_name == None or \\\\\\n object_key == None or \\\\\\n dest == None:\\n u_print(\\\" Error - argument is missing\\\")\\n\\n u_print_d('S3.download() - bucket=[{}] key=[{}] dest=[{}]'.format(bucket_name,\\n object_key,\\n dest))\\n return self.s3.Object(bucket_name, object_key).download_file(dest)\",\n \"def get_bytes(bucket: str, key: str) -> bytes:\\n logger.debug(f'Reading from s3://{bucket}/{key}')\\n response = client().get_object(Bucket=bucket, Key=key)\\n return response['Body'].read()\",\n \"def s3_get(url, temp_file):\\n s3_resource = boto3.resource(\\\"s3\\\")\\n bucket_name, s3_path = split_s3_path(url)\\n s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)\",\n \"def s3_get(url, temp_file):\\n s3_resource = boto3.resource(\\\"s3\\\")\\n bucket_name, s3_path = split_s3_path(url)\\n s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)\",\n \"async def fetch_file(self, download_url: str) -> bytes:\\n log.debug(f\\\"Fetching file from branding repository: '{download_url}'.\\\")\\n\\n async with self.bot.http_session.get(download_url, params=PARAMS, headers=HEADERS) as response:\\n if response.status != 200:\\n raise RuntimeError(f\\\"Failed to fetch file due to status: {response.status}\\\")\\n\\n log.debug(\\\"Fetch successful, reading payload.\\\")\\n return await response.read()\",\n \"def download(self, file_url):\\n url = self.base_url + \\\"/storage-service/cloud-storage/s3/file/download?url={0}\\\".format(file_url)\\n\\n headers = {\\\"ApiKey\\\": self.api_key}\\n response = requests.get(url=url, headers=headers)\\n return response\",\n \"def get_blob(uuid, path=''):\\n check_bundles_have_read_permission(local.model, request.user, [uuid])\\n bundle = local.model.get_bundle(uuid)\\n\\n target_info = local.download_manager.get_target_info(uuid, path, 0)\\n if target_info is None:\\n abort(httplib.NOT_FOUND, 'Not found.')\\n\\n # Figure out the file name.\\n if not path and bundle.metadata.name:\\n filename = bundle.metadata.name\\n else:\\n filename = target_info['name']\\n\\n if target_info['type'] == 'directory':\\n # Always tar and gzip directories.\\n filename = filename + '.tar.gz'\\n fileobj = local.download_manager.stream_tarred_gzipped_directory(uuid, path)\\n elif target_info['type'] == 'file':\\n if not zip_util.path_is_archive(filename) and request_accepts_gzip_encoding():\\n # Let's gzip to save bandwidth. The browser will transparently decode\\n # the file.\\n filename = filename + '.gz'\\n fileobj = local.download_manager.stream_file(uuid, path, gzipped=True)\\n else:\\n fileobj = local.download_manager.stream_file(uuid, path, gzipped=False)\\n else:\\n # Symlinks.\\n abort(httplib.FORBIDDEN, 'Cannot download files of this type.')\\n \\n # Set headers.\\n mimetype, _ = mimetypes.guess_type(filename, strict=False)\\n response.set_header('Content-Type', mimetype or 'text/plain')\\n if zip_util.get_archive_ext(filename) == '.gz' and request_accepts_gzip_encoding():\\n filename = zip_util.strip_archive_ext(filename)\\n response.set_header('Content-Encoding', 'gzip')\\n else:\\n response.set_header('Content-Encoding', 'identity')\\n response.set_header('Content-Disposition', 'filename=\\\"%s\\\"' % filename)\\n\\n return fileobj\",\n \"def get_s3_object(bucket, key_name, local_file):\\n\\n tracer.put_metadata('object', f's3://{bucket}/{key_name}')\\n\\n try:\\n s3_resource.Bucket(bucket).download_file(key_name, local_file)\\n result = 'ok'\\n tracer.put_annotation('OBJECT_DOWNLOAD', 'SUCCESS')\\n except botocore.exceptions.ClientError as e:\\n tracer.put_annotation('OBJECT_DOWNLOAD', 'FAILURE')\\n if e.response['Error']['Code'] == '404':\\n result = f'Error: s3://{bucket}/{key_name} does not exist'\\n else:\\n result = f'Error: {str(e)}'\\n\\n return(result)\",\n \"def _progress_blob(self, bucket, blob_name, progress_callback):\\n return _ProgressBlob(\\n name=blob_name,\\n bucket=bucket,\\n progress_callback=progress_callback\\n )\",\n \"def download_from_s3(s3_resource, photo):\\n try:\\n bucket, key = photo.replace(\\\"s3://\\\", \\\"\\\").split(\\\"/\\\", 1)\\n local_file = os.path.basename(photo)\\n except ValueError as err:\\n logger.exception(\\\"Couldn't get S3 info for %s: %s\\\", photo)\\n raise\\n\\n try:\\n logger.info(\\\"Downloading %s\\\", photo)\\n s3_resource.Bucket(bucket).download_file(key, local_file)\\n except ClientError:\\n logger.exception(\\\"Couldn't download %s from S3.\\\", photo)\\n raise\\n\\n return local_file\",\n \"def downloadObject(bucket:str, object:str, region:str, path:Path) -> None:\\n client = boto3.client('s3', region_name=region)\\n try:\\n with open(str(path), 'wb') as data:\\n client.download_fileobj(Bucket=bucket, Object=object, Data=data)\\n except Exception as e:\\n raise ObjectDownloadError(e)\",\n \"def DownloadFile(self, gcs_file_name, io_base):\\n bucket, bucket_path = self._ParseBucketAndPath(gcs_file_name)\\n\\n # Check the size of the remote file. If it's empty, we have to return early\\n # because the chunked downloader will crash. There aren't any contents to\\n # retrieve in that case, anyway.\\n object_data = self._RunWithRetries(\\n self._service.objects().get(bucket=bucket, object=bucket_path).execute,\\n self._CommonErrorMatcher)\\n if ('name' not in object_data or object_data['name'] != bucket_path\\n or 'size' not in object_data):\\n raise CloudStorageApiError('Object data for %s is malformed.' %\\n GcsPath(bucket, bucket_path))\\n if int(object_data['size']) == 0:\\n return\\n\\n request = self._service.objects().get_media(bucket=bucket,\\n object=bucket_path)\\n downloader = gapi_http.MediaIoBaseDownload(\\n io_base, request, chunksize=1024*1024)\\n done = False\\n while not done:\\n # The first return value indicates download progress, which we won't do\\n # anything fancy with for now.\\n _, done = self._RunWithRetries(downloader.next_chunk,\\n self._CommonErrorMatcher)\",\n \"def generate_download_signed_url_v4(bucket_name, blob_name):\\n bucket = storage_client.bucket(bucket_name)\\n blob = bucket.blob(blob_name)\\n\\n url = blob.generate_signed_url(\\n version=\\\"v4\\\",\\n # This URL is valid for 15 minutes\\n expiration=datetime.timedelta(minutes=30),\\n # Allow GET requests using this URL.\\n method=\\\"GET\\\",\\n )\\n\\n return url\",\n \"def download_from_gcs(gcs_uri, target_path):\\n bucket, blob_name = gcs_uri.replace('gs://', '').split('/', 1)\\n client = storage.Client(project='embdatalab')\\n bucket = client.get_bucket(bucket)\\n prefix = blob_name.split('*')[0]\\n unzipped = open(target_path, 'w')\\n cmd = \\\"gunzip -c -f %s >> %s\\\"\\n for blob in bucket.list_blobs(prefix=prefix):\\n with tempfile.NamedTemporaryFile(mode='rb+') as f:\\n logger.info(\\\"Downloading %s to %s\\\" % (blob.path, f.name))\\n blob.chunk_size = 2 ** 30\\n blob.download_to_file(f)\\n f.flush()\\n f.seek(0)\\n subprocess.check_call(\\n cmd % (f.name, unzipped.name), shell=True)\\n return unzipped.name\",\n \"def download_file(cls, uri, fobj):\\n # Breaks the URI into usable componenents.\\n values = get_values_from_media_uri(uri)\\n\\n conn = cls._get_aws_s3_connection(values['username'],\\n values['password'])\\n bucket = conn.get_bucket(values['host'])\\n key = bucket.get_key(values['path'])\\n\\n logger.debug(\\\"S3Backend.download_file(): \\\" \\\\\\n \\\"Downloading: %s\\\" % uri)\\n\\n try:\\n key.get_contents_to_file(fobj)\\n except AttributeError:\\n # Raised by ResumableDownloadHandler in boto when the given S3\\n # key can't be found.\\n message = \\\"The specified input file cannot be found.\\\"\\n raise InfileNotFoundException(message)\\n\\n logger.debug(\\\"S3Backend.download_file(): \\\" \\\\\\n \\\"Download of %s completed.\\\" % uri)\\n return fobj\",\n \"def get_s3_object(self, remote_s3_url):\\n try:\\n _file = tempfile.mkstemp()[1]\\n parsed_s3_path = remote_s3_url.split(\\\"/\\\", 3) # s3://bucket-name/key\\n remote_bucket = parsed_s3_path[2] # Bucket name\\n remote_key = parsed_s3_path[3] # Key\\n self.download_file(remote_bucket, remote_key, _file)\\n return _file\\n except Exception as e:\\n message = {'FILE': __file__.split('/')[-1],\\n 'METHOD': inspect.stack()[0][3], 'EXCEPTION': str(e)}\\n self.logger.exception(message)\\n raise\",\n \"def do_part_download(args):\\r\\n bucket_name, key_name, fname, min_byte, max_byte, split, secure, max_tries, current_tries = args\\r\\n conn = boto.connect_s3(calling_format=OrdinaryCallingFormat())\\r\\n conn.is_secure = secure\\r\\n\\r\\n # Make the S3 request\\r\\n resp = conn.make_request(\\\"GET\\\", bucket=bucket_name,\\r\\n key=key_name, headers={'Range':\\\"bytes=%d-%d\\\" % (min_byte, max_byte)})\\r\\n\\r\\n # Open the target file, seek to byte offset\\r\\n fd = os.open(fname, os.O_WRONLY)\\r\\n logger.debug(\\\"Opening file descriptor %d, seeking to %d\\\" % (fd, min_byte))\\r\\n os.lseek(fd, min_byte, os.SEEK_SET)\\r\\n\\r\\n chunk_size = min((max_byte-min_byte), split*1024*1024)\\r\\n logger.debug(\\\"Reading HTTP stream in %dM chunks\\\" % (chunk_size/1024./1024))\\r\\n t1 = time.time()\\r\\n s = 0\\r\\n try:\\r\\n while True:\\r\\n data = resp.read(chunk_size)\\r\\n if data == \\\"\\\":\\r\\n break\\r\\n os.write(fd, data)\\r\\n s += len(data)\\r\\n t2 = time.time() - t1\\r\\n os.close(fd)\\r\\n s = s / 1024 / 1024.\\r\\n logger.debug(\\\"Downloaded %0.2fM in %0.2fs at %0.2fMBps\\\" % (s, t2, s/t2))\\r\\n except Exception, err:\\r\\n logger.debug(\\\"Retry request %d of max %d times\\\" % (current_tries, max_tries))\\r\\n if (current_tries > max_tries):\\r\\n logger.error(err)\\r\\n else:\\r\\n time.sleep(3)\\r\\n current_tries += 1\\r\\n do_part_download(bucket_name, key_name, fname, min_byte, max_byte, split, secure, max_tries, current_tries)\",\n \"def download(\\n bucket: str, key: str, file_path: str, session: Optional[boto3.Session] = None\\n) -> str:\\n s3_client = _get_client(session)\\n\\n LOGGER.info(\\\"downloading s3://%s/%s to %s...\\\", bucket, key, file_path)\\n s3_client.download_file(Bucket=bucket, Key=key, Filename=file_path)\\n return file_path\",\n \"def download_fileobj(Fileobj=None, Bucket=None, Key=None, ExtraArgs=None, Callback=None, Config=None):\\n pass\",\n \"def get(self, key):\\n key = str(urllib.unquote(key)).strip()\\n logging.debug('key is %s', key)\\n blob_info = blobstore.BlobInfo.get(key)\\n self.send_blob(blob_info)\",\n \"def load(self, bucket, key):\\n\\n bucket = self._build_bucket_resource(bucket)\\n\\n with io.BytesIO() as stream:\\n bucket.download_fileobj(key, stream)\\n stream.seek(0)\\n\\n wrapper = io.TextIOWrapper(stream, encoding='utf-8')\\n # Preserve the original order\\n return json.load(wrapper, object_pairs_hook=collections.OrderedDict)\",\n \"def _s3_get_file(url):\\n try:\\n return S3().get_contents_from_url(url)\\n except Exception as e:\\n raise ScrBaseException(\\\"Could not load file from {0}: {1}\\\".format(url, e))\",\n \"def s3_get(url, temp_file, proxies=None):\\n\\ts3_resource = boto3.resource (\\\"s3\\\", config=Config (proxies=proxies))\\n\\tbucket_name, s3_path = split_s3_path (url)\\n\\ts3_resource.Bucket (bucket_name).download_fileobj (s3_path, temp_file)\",\n \"def list_blobs(bucket_name):\\n # bucket_name = \\\"your-bucket-name\\\"\\n\\n storage_client = storage.Client()\\n print(storage_client.current_batch)\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n # print(len([1 for blob in blobs]))\\n for blob in blobs:\\n print(blob.name)\",\n \"def get_blob_content(container_name: str, blob_path: str) -> str:\\n global BLOB_SERVICE_CLIENT\\n # TODO: Should add retry policy here\\n if not BLOB_SERVICE_CLIENT:\\n logging.info(\\n f\\\"{HEADER} Initialize blob service client for {DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL}\\\")\\n BLOB_SERVICE_CLIENT = BlobServiceClient(DATABRICKS_OUTPUT_STORAGE_ACCOUNT_URL,\\n credential=DATABRICKS_OUTPUT_STORAGE_SAS_TOKEN)\\n blob_client = BLOB_SERVICE_CLIENT.get_blob_client(container=container_name, blob=blob_path)\\n content = blob_client.download_blob().content_as_text()\\n return content\",\n \"def load_pickle(self, bucket, key):\\n\\n with BytesIO() as obj_buffer:\\n self._s3.Bucket(bucket).download_fileobj(key, obj_buffer)\\n obj_buffer.seek(0)\\n obj = pickle.load(obj_buffer)\\n\\n return obj\",\n \"def copy_blob(bucket_name, blob_name, destination_bucket_name, destination_blob_name):\\r\\n\\r\\n # initialize client, get bucket, & get blob\\r\\n storage_client, source_bucket, source_blob = create_client(bucket_name, blob_name)\\r\\n\\r\\n # set destination bucket name\\r\\n destination_bucket = storage_client.bucket(destination_bucket_name)\\r\\n\\r\\n # copy blob\\r\\n blob_copy = source_bucket.copy_blob(\\r\\n source_blob, destination_bucket, destination_blob_name\\r\\n )\\r\\n\\r\\n print(\\r\\n \\\"blob {} in bucket {} copied to blob {} in bucket {}.\\\".format(\\r\\n source_blob.name,\\r\\n source_bucket.name,\\r\\n blob_copy.name,\\r\\n destination_bucket.name,\\r\\n )\\r\\n )\",\n \"def get_file(object_name: str, **kwargs) -> HTTPResponse:\\n data = client.get_object(DATASETS_BUCKET, object_name, **kwargs)\\n return data\",\n \"def get_object(self, bucket_name, key, stream=False, extra_get_args={}):\\n url = self.__key_url(bucket_name, key)\\n res = self.infinispan_client.get(url, headers=self.headers, auth=self.basicAuth)\\n data = res.content\\n return data\",\n \"async def read(self, size=-1):\\n # read the object using the bucket and path already determined in\\n # __init__, and using the connection object\\n try:\\n # get the file size first\\n file_size = await self._getsize()\\n if size== -1:\\n range_start = 0\\n range_end = file_size\\n range_size = file_size\\n else:\\n range_start = self._seek_pos\\n range_end = self._seek_pos+size-1\\n if range_end > file_size:\\n range_end = file_size-1\\n range_size = range_end-range_start+1\\n\\n # if multipart download is not supported\\n if not self._multipart_download:\\n # get the full file\\n s3_object = await self._conn_obj.conn.get_object(\\n Bucket = self._bucket,\\n Key = self._path,\\n )\\n body = s3_object['Body']\\n data = await body.read()\\n # if the file is smaller than the MAXIMUM_PART_SIZE\\n elif (range_size < self._part_size):\\n # the requested range is the full file, it is fastest to\\n # not specify the range\\n if (range_start == 0 and range_size == file_size):\\n # get the full file\\n s3_object = await self._conn_obj.conn.get_object(\\n Bucket = self._bucket,\\n Key = self._path,\\n )\\n # a portion of the file is requested\\n else:\\n s3_object = await self._conn_obj.conn.get_object(\\n Bucket = self._bucket,\\n Key = self._path,\\n Range = 'bytes={}-{}'.format(\\n range_start, range_end\\n )\\n )\\n body = s3_object['Body']\\n data = await body.read()\\n # multipart download version\\n else:\\n \\\"\\\"\\\"Use range get to split up a file into the MAXIMUM_PART_SIZE\\n and download each part asynchronously.\\\"\\\"\\\"\\n # calculate the number of necessary parts\\n n_parts = int(range_size / self._part_size + 1)\\n # don't go above the maximum number downloadable\\n if n_parts > self._max_parts:\\n n_parts = self._max_parts\\n # (re)calculate the download size\\n part_size = float(range_size) / n_parts\\n # create the tasks and assign the return data buffer\\n tasks = []\\n data_buf = io.BytesIO()\\n\\n for p in range(0, n_parts):\\n event_loop = asyncio.get_event_loop()\\n task = event_loop.create_task(self._read_partial_file(\\n p, part_size\\n ))\\n tasks.append(task)\\n # wait for all the tasks to finish\\n results = await asyncio.gather(*tasks)\\n # read each chunk of data and write into the global buffer\\n for r in results:\\n data_buf.write(r)\\n r = None # indicate ready for garbage collection\\n data_buf.seek(0)\\n data = data_buf.read()\\n\\n except ClientError as e:\\n raise IOException(\\n \\\"Could not read from object {} {}\\\".format(self._path, e)\\n )\\n except AttributeError as e:\\n self._handle_connection_exception(e)\\n return data\",\n \"def new_blob(self, blob_name):\\n return storage.Blob(blob_name, self.bucket)\",\n \"def download_file(file_name, bucket_name, object_name=None):\\n # If S3 object_name was not specified, use file_name\\n if object_name is None:\\n object_name = os.path.basename(file_name)\\n\\n try:\\n # Download file from bucket\\n response = s3.download_file(Bucket=bucket_name, Key=object_name, Filename=file_name)\\n except ClientError as e:\\n logging.error(e)\\n return False\\n return True\",\n \"def _do_retrieve(bucket_name, key_path, number_retries=DEFAULT_S3_RETRIES):\\n try:\\n return conn.get_object(Bucket=bucket_name, Key=key_path, ResponseContentType='string')\\n except Exception:\\n if number_retries > 0:\\n print(\\\"s3_retrieve failed with incomplete read, retrying on %s\\\" % key_path)\\n return _do_retrieve(bucket_name, key_path, number_retries=number_retries - 1)\\n raise\",\n \"def get_file_s3(bucket, key):\\n \\n client = boto3.client('s3')\\n return client.get_object(Bucket=bucket, Key=key)['Body'].read().decode('utf-8')\",\n \"def download(url, bucket_id, key_prefix):\\n\\n baseFile = '_'.join(url.split('/')[-4:]) #os.path.basename(url)\\n\\n #move the file to a more uniq path\\n os.umask(0002)\\n temp_path = \\\"/tmp/\\\"\\n file = os.path.join(temp_path,baseFile)\\n bucket = conn.get_bucket(bucket_id)\\n key = bucket.get_key(key_prefix + baseFile, validate=False)\\n s3_exists = key.exists()\\n file_exists = os.path.isfile(file)\\n \\n if not file_exists and s3_exists:\\n sys.stderr.write(\\\"Downloading %s from S3\\\\n\\\"%url)\\n key.get_contents_to_filename(file)\\n sys.stderr.write(\\\"Downloaded %s from S3\\\\n\\\"%url)\\n elif not file_exists and not s3_exists:\\n sys.stderr.write(\\\"Downloading %s from the web\\\\n\\\"%url)\\n try:\\n req = urllib2.urlopen(url)\\n total_size = int(req.info().getheader('Content-Length').strip())\\n downloaded = 0\\n CHUNK = 256 * 10240\\n with open(file, 'wb') as fp:\\n while True:\\n chunk = req.read(CHUNK)\\n downloaded += len(chunk)\\n #print math.floor( (downloaded / total_size) * 100 )\\n if not chunk: break\\n fp.write(chunk)\\n except urllib2.HTTPError, e:\\n sys.stderr.write(\\\"HTTP Error: %s %s\\\\n\\\"%(e.code , url))\\n return False\\n except urllib2.URLError, e:\\n sys.stderr.write(\\\"URL Error: %s %s\\\\n\\\"%(e.reason , url))\\n return False\\n sys.stderr.write(\\\"Downloaded %s from the web\\\\n\\\"%url)\\n\\n if not s3_exists:\\n sys.stderr.write(\\\"Uploading %s to S3\\\\n\\\"%url)\\n key.set_contents_from_filename(file)\\n\\n sys.stderr.write(\\\"File ready: %s\\\\n\\\"%url)\\n return file\",\n \"def delete_blob(blob_name):\\n # bucket_name = \\\"your-bucket-name\\\"\\n # blob_name = \\\"your-object-name\\\"\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(blob_name)\\n blob.delete()\\n print('Blob {} deleted.'.format(blob_name))\",\n \"def list_blobs(bucket_name):\\n storage_client = storage.Client()\\n bucket = storage_client.get_bucket(bucket_name)\\n\\n blobs = bucket.list_blobs()\\n list_blobs = [blob.public_url for blob in blobs]\\n return list_blobs\",\n \"async def _download(self) -> None:\\n\\n # do request\\n async with aiohttp.ClientSession() as session:\\n async with session.get(self.url, auth=self._auth, timeout=self._timeout) as response:\\n # check response\\n if response.status == 200:\\n # get data and return it\\n self._buffer = await response.read()\\n elif response.status == 401:\\n log.error(\\\"Wrong credentials for downloading file.\\\")\\n raise FileNotFoundError\\n else:\\n log.error(\\\"Could not download file from filecache.\\\")\\n raise FileNotFoundError\",\n \"def _torrent_for_blob(blob, is_public):\\n # Make sure the storage has a size.\\n if not blob.compressed_size:\\n abort(404)\\n\\n # Lookup the torrent information for the storage.\\n torrent_info = registry_model.get_torrent_info(blob)\\n if torrent_info is None:\\n abort(404)\\n\\n # Lookup the webseed path for the storage.\\n webseed = storage.get_direct_download_url(\\n blob.placements, blob.storage_path, expires_in=app.config[\\\"BITTORRENT_WEBSEED_LIFETIME\\\"]\\n )\\n if webseed is None:\\n # We cannot support webseeds for storages that cannot provide direct downloads.\\n exact_abort(501, \\\"Storage engine does not support seeding.\\\")\\n\\n # Load the config for building torrents.\\n torrent_config = TorrentConfiguration.from_app_config(instance_keys, app.config)\\n\\n # Build the filename for the torrent.\\n if is_public:\\n name = public_torrent_filename(blob.uuid)\\n else:\\n user = get_authenticated_user()\\n if not user:\\n abort(403)\\n\\n name = per_user_torrent_filename(torrent_config, user.uuid, blob.uuid)\\n\\n # Return the torrent file.\\n torrent_file = make_torrent(\\n torrent_config,\\n name,\\n webseed,\\n blob.compressed_size,\\n torrent_info.piece_length,\\n torrent_info.pieces,\\n )\\n\\n headers = {\\n \\\"Content-Type\\\": \\\"application/x-bittorrent\\\",\\n \\\"Content-Disposition\\\": \\\"attachment; filename={0}.torrent\\\".format(name),\\n }\\n\\n return make_response(torrent_file, 200, headers)\",\n \"def fetch(self, url) -> bytes:\\n buffer = self.download(url)\\n zfs = ZipFileSystem(buffer, \\\"r\\\")\\n return zfs.open(zfs.glob(\\\"*\\\")[0]).read()\",\n \"def list_blobs(bucket):\\n bucket = default_bucket if bucket is None else bucket\\n bucket_name = bucket if isinstance(bucket, str) else bucket.name\\n blobs = gcs.list_blobs(bucket_name)\\n return blobs\",\n \"def download_file_from_icos(icos_obj, bucket: str, local_file_name: str, key: str) -> None:\\r\\n try:\\r\\n icos_obj.download_file(Bucket=bucket, Key=key, Filename=local_file_name)\\r\\n except Exception as e:\\r\\n print(Exception, e)\\r\\n else:\\r\\n print('File `{}` downloaded from ICOS and saved locally as `{}`.'.format(key, local_file_name))\",\n \"def download(self, key, filename):\\n self.resource.Bucket(self.bucket).download_file(key, filename)\\n return filename\",\n \"def download_chain(s3_path, local_path, bucket_name='lwr-inverse-us-east'):\\n s3 = boto3.resource(\\\"s3\\\")\\n lwr_AIES = s3.Bucket(bucket_name)\\n try:\\n lwr_AIES.download_file(Key=s3_path, Filename=local_path)\\n print(\\\"Download successful\\\")\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == \\\"404\\\":\\n print(\\\"The object does not exist.\\\")\\n else:\\n raise\",\n \"def list_blobs(bucket_name):\\n storage_client = storage.Client()\\n\\n # Note: Client.list_blobs requires at least package version 1.17.0.\\n blobs = storage_client.list_blobs(bucket_name)\\n\\n return blobs\",\n \"def upload_to_bucket(blob_name, file, bucket_name):\\n\\n # Explicitly use service account credentials by specifying the private key\\n # file.\\n storage_client = storage.Client.from_service_account_json('creds.json')\\n\\n bucket = storage_client.get_bucket(bucket_name)\\n blob = bucket.blob(blob_name)\\n blob.upload_from_file(file)\\n\\n # returns a public url\\n return blob.public_url\",\n \"def get_file_download(self, bucket_id, file_id):\\n\\n \\n path = '/storage/buckets/{bucketId}/files/{fileId}/download'\\n params = {}\\n if bucket_id is None:\\n raise AppwriteException('Missing required parameter: \\\"bucket_id\\\"')\\n\\n if file_id is None:\\n raise AppwriteException('Missing required parameter: \\\"file_id\\\"')\\n\\n path = path.replace('{bucketId}', bucket_id)\\n path = path.replace('{fileId}', file_id)\\n\\n\\n return self.client.call('get', path, {\\n 'content-type': 'application/json',\\n }, params)\",\n \"def download_from_s3(self, filename: str, filename_output: Optional[str] = None) -> str:\\n if self.aws_access_key_id is None:\\n raise Exception(\\n 'To use `download_from_s3` you need to pass '\\n '`aws_access_key_id` and '\\n '`aws_secret_access_key`'\\n )\\n\\n s3 = boto3.client('s3',\\n aws_access_key_id=self.aws_access_key_id,\\n aws_secret_access_key=self.aws_secret_access_key)\\n\\n # Progress bar\\n meta_data = s3.head_object(Bucket=self.bucket_name, Key=filename)\\n size = int(meta_data.get('ContentLength', 0))\\n progress_bar = self._progress(size)\\n\\n # Downloading file\\n s3.download_file(self.bucket_name, filename,\\n filename if filename_output is None else filename_output,\\n Callback=progress_bar)\",\n \"def download(self, bucket_name, key_name, fname):\\n dname = os.path.dirname(fname)\\n if dname and not os.path.exists(dname):\\n os.makedirs(dname)\\n bucket = self.s3_.get_bucket(bucket_name)\\n key = bucket.get_key(key_name)\\n return key.get_contents_to_filename(fname)\",\n \"def fetch(iid):\\n if AWS_CLIENT_CONFIG and BUCKET_NAME:\\n try:\\n s3 = boto3.resource('s3', **AWS_CLIENT_CONFIG)\\n obj = s3.Bucket(BUCKET_NAME).Object(iid).get()\\n if obj:\\n return obj.get('Body')\\n except botocore.exceptions.ClientError as e:\\n logger.error(e)\\n else:\\n # get locally from temp dir (tests, local development)\\n return get_temp_file(iid)\\n return None\",\n \"def fetch_and_extract(self, filename):\\n # type: (Text) -> None\\n\\n with io.open(filename, 'wb') as f:\\n self.bucket.download_fileobj(filename, f)\\n with tarfile.open(filename, \\\"r:gz\\\") as tar:\\n tar.extractall(self.data_dir)\",\n \"def get_object(self, container_name, object_name, download_path):\\n obj = self.client.get_object(container_name, object_name)\\n with open(download_path + object_name, 'w') as test_file:\\n test_file.write(obj[1])\\n return True\",\n \"def _download(self):\\n self._system.download_file(\\\"http://curl.haxx.se/download/\\\" + self._tar_name)\",\n \"def download(handle):\\n storage = get_storage()\\n # FIXME: 404 if not found or invalid?\\n return storage.route(handle)\",\n \"def get_remote_bytes(file_url) -> io.BytesIO:\\n result = urlfetch.fetch(file_url)\\n return io.BytesIO(result.content)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8135551","0.8126195","0.7890022","0.78339165","0.7721649","0.77189684","0.7649907","0.7573001","0.7502306","0.74736196","0.74572617","0.7345375","0.73441243","0.72784907","0.7213689","0.7128273","0.7065729","0.6937288","0.6928252","0.6894445","0.68285346","0.6771118","0.6566794","0.650527","0.6438687","0.64302903","0.6416173","0.6409029","0.6397378","0.63945675","0.638665","0.63407785","0.63147837","0.62715214","0.6262548","0.624161","0.6205678","0.61963224","0.6155657","0.61530966","0.61245006","0.611263","0.60940665","0.6090566","0.6088601","0.6071537","0.60596937","0.6040488","0.6040488","0.6002172","0.5999373","0.59909165","0.59675324","0.5923368","0.59098816","0.5894061","0.5890488","0.58899784","0.58667856","0.5866474","0.58513284","0.5847841","0.582473","0.5798656","0.57645994","0.5751948","0.5749541","0.5737763","0.5696356","0.5694621","0.56945485","0.56900066","0.5687712","0.5679901","0.5671524","0.5670936","0.56703305","0.5665057","0.56610024","0.56574565","0.5651831","0.56483865","0.5643131","0.5636791","0.56332004","0.5617774","0.56170154","0.5614051","0.56115985","0.56067723","0.56050444","0.5587371","0.55863833","0.5547073","0.55434656","0.5540437","0.55376863","0.5533472","0.55327046","0.552546"],"string":"[\n \"0.8135551\",\n \"0.8126195\",\n \"0.7890022\",\n \"0.78339165\",\n \"0.7721649\",\n \"0.77189684\",\n \"0.7649907\",\n \"0.7573001\",\n \"0.7502306\",\n \"0.74736196\",\n \"0.74572617\",\n \"0.7345375\",\n \"0.73441243\",\n \"0.72784907\",\n \"0.7213689\",\n \"0.7128273\",\n \"0.7065729\",\n \"0.6937288\",\n \"0.6928252\",\n \"0.6894445\",\n \"0.68285346\",\n \"0.6771118\",\n \"0.6566794\",\n \"0.650527\",\n \"0.6438687\",\n \"0.64302903\",\n \"0.6416173\",\n \"0.6409029\",\n \"0.6397378\",\n \"0.63945675\",\n \"0.638665\",\n \"0.63407785\",\n \"0.63147837\",\n \"0.62715214\",\n \"0.6262548\",\n \"0.624161\",\n \"0.6205678\",\n \"0.61963224\",\n \"0.6155657\",\n \"0.61530966\",\n \"0.61245006\",\n \"0.611263\",\n \"0.60940665\",\n \"0.6090566\",\n \"0.6088601\",\n \"0.6071537\",\n \"0.60596937\",\n \"0.6040488\",\n \"0.6040488\",\n \"0.6002172\",\n \"0.5999373\",\n \"0.59909165\",\n \"0.59675324\",\n \"0.5923368\",\n \"0.59098816\",\n \"0.5894061\",\n \"0.5890488\",\n \"0.58899784\",\n \"0.58667856\",\n \"0.5866474\",\n \"0.58513284\",\n \"0.5847841\",\n \"0.582473\",\n \"0.5798656\",\n \"0.57645994\",\n \"0.5751948\",\n \"0.5749541\",\n \"0.5737763\",\n \"0.5696356\",\n \"0.5694621\",\n \"0.56945485\",\n \"0.56900066\",\n \"0.5687712\",\n \"0.5679901\",\n \"0.5671524\",\n \"0.5670936\",\n \"0.56703305\",\n \"0.5665057\",\n \"0.56610024\",\n \"0.56574565\",\n \"0.5651831\",\n \"0.56483865\",\n \"0.5643131\",\n \"0.5636791\",\n \"0.56332004\",\n \"0.5617774\",\n \"0.56170154\",\n \"0.5614051\",\n \"0.56115985\",\n \"0.56067723\",\n \"0.56050444\",\n \"0.5587371\",\n \"0.55863833\",\n \"0.5547073\",\n \"0.55434656\",\n \"0.5540437\",\n \"0.55376863\",\n \"0.5533472\",\n \"0.55327046\",\n \"0.552546\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70821893"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":94597,"cells":{"query":{"kind":"string","value":"Current user will place order"},"document":{"kind":"string","value":"def post(self, request, format=None):\n inventory_id = request.data.get('inventory_id', None)\n\n user_id = None\n if hasattr(request, 'user'):\n user_id = request.user.id\n\n if inventory_id is not None and user_id is not None:\n try:\n inventory_item = self.nv_models.Inventory.objects.get(\n pk=inventory_id)\n except self.nv_models.Inventory.DoesNotExist:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n try:\n user = self.nv_models.ExtendedUser.objects.get(pk=user_id)\n except self.nv_models.ExtendedUser.DoesNotExist:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n\n try:\n new_data = {}\n new_data['inventory'] = inventory_item\n new_data['customer'] = user\n new_order = self.nv_models.InventoryOrder(**new_data)\n new_order.save()\n\n return Response(self.InventoryOrderSerializer(new_order).data)\n except Exception as e:\n return Response(status=status.HTTP_400_BAD_REQUEST)\n\n return Response(status=status.HTTP_400_BAD_REQUEST)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def place_order(self):\n\n order_total = self.get_order_total()\n\n if self.person.can_afford(order_total):\n print 'This person is stinkin rich!'\n else:\n print \"No soup for you!\"","def test_order_by_user(self):\n self.fill_session_cart()\n self.client.post(self.CHECKOUT_URL, self.build_checkout_form())\n self.assertEqual(OrderInfo.objects.get().user,\n USER_MODEL.objects.get())","def place_buy_order(self):\n price = request.form[\"price\"]\n stocks = request.form[\"stocks\"]\n trader_id = request.form[\"trader_id\"]\n self.market.place_buy_order(trader_id, price, stocks)\n return \"\"","def place(self):\n\n return super().place(self._order_params)","def test_process_order_adds_coins_to_users_account_if_coins_in_order(self):\n self.client.force_login(User.objects.get_or_create(username=\"coinsuser\", email=\"coinsuser@email.com\")[0])\n\n current_user = User.objects.get(username=\"coinsuser\")\n\n request = HttpRequest()\n engine = import_module(settings.SESSION_ENGINE)\n session_key = None\n request.session = engine.SessionStore(session_key)\n\n # below two lines of code from https://stackoverflow.com/questions/11938164/why-dont-my-django-unittests-know-that-messagemiddleware-is-installed\n # fixes bug where test fails because unitest\n messages = FallbackStorage(request)\n setattr(request, '_messages', messages)\n\n random_coins_store_item = choice(StoreItem.objects.filter(is_coins=True))\n cart_add(request, random_coins_store_item.id)\n\n process_order(request, current_user)\n\n current_users_coins = UserCoins.objects.get(user=current_user).coins\n self.assertEqual(current_users_coins, random_coins_store_item.coins_amount)","def slot_userorder(self, gox, (price, volume, typ, oid, status)):\r\n pass","def place_order(self,name,address,quantity,contact):\n self.name = name\n self.address = address\n self.quantity = quantity\n self.contact = contact\n self.event = \"user_place_order\"\n self.query_1 = \"SELECT meal_price FROM menu WHERE meal_name=%s\"\n self.input_1 = (self.name,)\n self.query_2 = \"\"\"INSERT INTO public.orders (order_price,order_delivery_address,order_quantity,\n order_contact,order_status,user_id, meal_name) VALUES (%s,%s,%s,%s,%s,%s,%s)\"\"\"\n self.message = \"Order placed successfully.\"\n self.error = \"Unable to place order. The meal is not available\"","def on_order(self, order: OrderData):\n # print(\"on_order\")\n # print(order)\n pass","def place_order(self, **kwargs):\r\n create_options = self._generate_create_dict(**kwargs)\r\n return self.client['Product_Order'].placeOrder(create_options)","def place_order(self, market: Market, order) -> bool:\n runner_context = self.get_runner_context(*order.lookup)\n if self.validate_order(runner_context, order):\n runner_context.place()\n market.place_order(order)\n return True\n else:\n return False","def on_order(self, order: OrderData):\n pass","def on_order(self, order: OrderData):\n pass","def on_order(self, order: OrderData):\n pass","def get_one_user_orders(self,user_id):\n\n sql = \"SELECT * FROM parcel_orders WHERE user_id='{}'\".format(user_id)\n self.db_object.cursor.execute(sql)\n placed_orders = self.db_object.cursor.fetchall()\n return placed_orders","def test_order_from_user(self):\n new_user = self.create_test_user()\n new_order = OrderInfo.objects.create(user=new_user)\n self.assertEqual(new_order.user, new_user)","def PlaceOrder(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def test_place_one_order(self):\n self.orders_list.place_order(self.order)\n self.assertEqual(len(self.orders_list.orders_list), 1)\n self.assertEqual(self.orders_list.orders_list[0].order_id, 0)","def user_open_orders(request):\n qset = Order.objects.filter(\n receiver=request.user\n ).order_by(\n '-date_placed'\n )\n for order in qset:\n min_bid = get_min_bid(order)\n if not min_bid:\n order.min_bid = \"No current bids\"\n else:\n order.min_bid = min_bid\n\n data_dict = {}\n data_dict.update({ k : v.value\n for (k,v)\n in OrderAction.Action._member_map_.items()\n })\n data_dict['orders'] = qset\n\n return render(request, 'friendship/user_open_orders.html', data_dict)","def create_order():","def order(self, request):\n is_auth = request.session.get(\"is_auth\", False)\n if not is_auth:\n return HttpResponseRedirect('/crisis')\n\n uid = request.session['uid']\n context = RUNNING_INFO.get(uid, {})\n context.update({\"is_auth\": is_auth,\n \"is_daily\": request.session.get(\"is_daily\", False),\n \"is_leader\": request.session.get(\"is_leader\", False),\n \"entity_list\": ENTITY,\n \"parts_list\": PARTS,\n \"detail_info_list\": DETAIL_INFO})\n\n if \"priority\" not in context:\n priority = {}\n for item in ARMY:\n priority.update({item: 1})\n context.update({\"priority\": priority})\n\n if context.get(\"is_run\", False):\n context.update({\"left_time\": self.utils.get_remaining_time(uid),\n \"order\": self.utils.get_current_unit_order(uid)})\n \"\"\" Context Example\n context = {\"username\": self.utils.get_user_name(uid),\n \"is_run\": False,\n \"is_auth\": is_auth,\n \"resource\": {\"money\": 100, \"food\": 200, \"fuel\": 300},\n \"entity\": {\"armor_composite\": 1, \"armor_plate\": 2, \"control_block\": 3,\n \"gun_receiver\": 4, \"kevlar_fiber\": 5, \"laser_aimer\": 6,\n \"powder_charge\": 7, \"rare_item\": 8, \"tnt_charge\": 9},\n \"parts\": {\"artillery_armor\": 1, \"artillery_chassis\": 2, \"artillery_shell\": 3, \"detonator\": 4,\n \"gunner_armor\": 5, \"gunner_gun\": 6, \"jeep_armor\": 7, \"jeep_gun\": 8, \"sniper_armor\": 9,\n \"sniper_gun\": 10, \"soldier_gun\": 11, \"tank_chassis\": 12, \"thrower_armor\": 13,\n \"thrower_gun\": 14, \"wave_emitter\": 15},\n 'order': {'soldier': 1, 'thrower': 4, 'artillery': 8, 'gunner': 2, 'base_artillery': 7, 'jeep': 6, 'artillery_emp': 9, 'base_tank': 5, 'artillery_cassete': 0, 'sniper': 3}\n }\n \"\"\"\n\n if request.method == \"POST\":\n if \"start\" in request.POST:\n order, priority = {}, {}\n data = dict(request.POST)\n\n for item in ARMY:\n try:\n count = int(data.get(item, [''])[0])\n except:\n count = 0\n try:\n prior = int(data.get(\"%s_priority\" % item, [''])[0])\n except:\n prior = 1\n order.update({item: count})\n priority.update({item: prior})\n\n context.update({\"is_run\": True,\n \"order\": order,\n \"priority\": priority,\n \"left_time\": self.utils.get_remaining_time(uid)})\n\n RUNNING_INFO.update({uid: context})\n self.utils.start_gather(uid, context)\n elif \"stop\" in request.POST:\n uid = request.session['uid']\n context = RUNNING_INFO.get(uid, {})\n context.update({\"is_run\": False, \"left_time\": \"00:00:00\"})\n RUNNING_INFO.update({uid: context})\n self.utils.stop_gather(uid)\n\n return render_to_response(\"crisis/order.html\",\n context,\n context_instance=RequestContext(request))","def test_make_order_by_authorised_user(self):\n\n\t\tres = self.login_user()\n\t\taccess_token = json.loads(res.data.decode())['access_token']\n\n\t\tresponse = self.client().post(\n\t\t\t'/api/v2/orders',\n\t\t\theaders={\"x-access-token\": access_token},\n\t\t\tdata = json.dumps(\n\t\t\t\tself.order_data) , content_type = 'application/json')\n\t\tresult = json.loads(response.data)\n\t\tself.assertEqual(result[\"message\"], \"Order succesfully posted\")\n\t\tself.assertEqual(response.status_code, 201)","def update_order():","def update_order():","def save(self, *args, **kwargs):\n self.order_total = self.membership.price\n if not self.order_number:\n self.order_number = self._generate_order_number()\n super().save(*args, **kwargs)","def cook_order(request):\n\torder_id = request.GET.get('order_id', 0)\n\tcs , status = CookStatus.objects.get_or_create(cook_name=request.user)\n\n\tif cs.current_order is None:\n\t\tcs.current_order = Order.objects.get(id=order_id)\n\t\tcs.current_order.status = 'cooking'\n\t\tcs.current_order.tikchen = request.user.username\n\t\tcs.current_order.save()\n\t\tcs.save()\n\n\treturn HttpResponseRedirect(\"/staff/cook_order_list/\")","def create_order(request, order, transaction_id):\n\n\n order.transaction_id = transaction_id\n print transaction_id\n #order.ip_address = request.META.get('REMOTE_ADDR')\n order.user = None\n #if request.user.is_authenticated():\n # order.user = request.user\n order.status = Order.SUBMITTED\n\n DBSession.add(order)\n\n\n if order:\n \"\"\" if the order save succeeded \"\"\"\n cart_items = cart.get_cart_items(request).all()\n print \"The items in the cart are: \", len(cart_items)\n\n for ci in cart_items:\n \"\"\" create order item for each cart item \"\"\"\n\n print \"The product is \", ci.product\n oi = OrderItem()\n oi.order_id = order.id\n oi.order = order\n oi.quantity = ci.quantity\n print \"The product id is \", ci.product.id\n oi.product_id = ci.product.id\n oi.product = ci.product\n\n oi.price = ci.price # now using @property\n DBSession.add(oi)\n\n # all set, clear the cart\n cart.empty_cart(request)\n\n ## save profile info for future orders\n #if request.user.is_authenticated():\n # from ecomstore.accounts import profile\n #\n # profile.set(request)\n\n return order","def place_order(self, custom_id=None, **params):\n self.conn.send('newOrder', custom_id=custom_id, **params)","def order(self, typ, price, volume):\r\n self.count_submitted += 1\r\n self.client.send_order_add(typ, price, volume)","def make_order(self) -> Order:\n return Order.objects.create(self.user, customer=self)","def place(self, order_params):\n\n # Prevent multiple invocations with the same OID.\n if self.oid() is not None:\n return self.oid()\n\n # Common params across all orders\n # https://docs.gdax.com/?python#orders\n data = {\n 'side': self.__side,\n 'type': self.__order_type,\n 'product_id': self.__product,\n }\n data.update(order_params)\n\n log.info('placing ORDER')\n self.__resp = httpapi.post(\n common.api_url + 'orders',\n data=json.dumps(data),\n auth=common.auth,\n )\n\n return self.oid(), self.__resp","def place_order(creator, **kwargs):\n if \"shipping_method\" not in kwargs:\n kwargs[\"shipping_method\"] = Free()\n\n shipping_charge = kwargs[\"shipping_method\"].calculate(kwargs[\"basket\"])\n\n kwargs[\"total\"] = calculators.OrderTotalCalculator().calculate(\n basket=kwargs[\"basket\"],\n shipping_charge=shipping_charge,\n surcharges=kwargs[\"surcharges\"],\n )\n kwargs[\"shipping_charge\"] = shipping_charge\n\n return creator.place_order(**kwargs)","async def place_order(request: web.Request, body) -> web.Response:\n body = Order.from_dict(body)\n return web.Response(status=200)","def create(self):\n return self.account.create_order(self)","def request_orders(self):\r\n if self.use_http():\r\n self.enqueue_http_request(\"money/orders\", {}, \"orders\")\r\n else:\r\n self.send_signed_call(\"private/orders\", {}, \"orders\")","def get_order(self):\n #store the orders for the current cycle inside the class\n self.orders = self.firebase.get_data(\"orders\")","def _on_op_private_user_order(self, msg):\r\n order = msg[\"user_order\"]\r\n oid = order[\"oid\"]\r\n\r\n # there exist 3 fundamentally different types of user_order messages,\r\n # they differ in the presence or absence of certain parts of the message\r\n\r\n if \"status\" in order:\r\n # these are limit orders or market orders (new or updated).\r\n #\r\n # we also need to check whether they belong to our own gox instance,\r\n # since they contain currency this is easy, we compare the currency\r\n # and simply ignore mesages for all unrelated currencies.\r\n if order[\"currency\"] == self.curr_quote and order[\"item\"] == self.curr_base:\r\n volume = int(order[\"amount\"][\"value_int\"])\r\n typ = order[\"type\"]\r\n status = order[\"status\"]\r\n if \"price\" in order:\r\n # these are limit orders (new or updated)\r\n price = int(order[\"price\"][\"value_int\"])\r\n else:\r\n # these are market orders (new or updated)\r\n price = 0\r\n self.signal_userorder(self, (price, volume, typ, oid, status))\r\n\r\n else:\r\n # these are remove messages (cancel or fill)\r\n # here it is a bit more expensive to check whether they belong to\r\n # this gox instance, they don't carry any other useful data besides\r\n # the order id and the remove reason but since a remove message can\r\n # only affect us if the oid is in the owns list already we just\r\n # ask the orderbook instance whether it knows about this order\r\n # and ignore all the ones that have unknown oid\r\n if self.orderbook.have_own_oid(oid):\r\n # they don't contain a status field either, so we make up\r\n # our own status string to make it more useful. It will\r\n # be \"removed:\" followed by the reason. Possible reasons are:\r\n # \"requested\", \"completed_passive\", \"completed_active\"\r\n # so for example a cancel would be \"removed:requested\"\r\n # and a limit order fill would be \"removed:completed_passive\".\r\n status = \"removed:\" + order[\"reason\"]\r\n self.signal_userorder(self, (0, 0, \"\", oid, status))","def order(self, order):\n self._order = order","def subscribe_user_orders(self, update_handler):\n pass","def on_order(self, order: OrderData):\n\n if order.vt_orderid not in (self.short_orders + self.long_orders):\n return\n\n self.pos_calculator.update_position(order)\n\n self.current_pos = self.pos_calculator.pos\n self.avg_price = self.pos_calculator.avg_price\n\n if order.status == Status.ALLTRADED:\n\n if order.vt_orderid in self.long_orders:\n self.long_orders.remove(order.vt_orderid)\n self.trade_count += 1\n\n short_price = order.price + self.step_price\n if short_price <= self.high_price:\n orders = self.short(short_price, self.order_volume)\n self.short_orders.extend(orders)\n\n if len(self.long_orders) < self.max_open_orders:\n long_price = order.price - self.step_price * self.max_open_orders\n if long_price >= self.low_price:\n orders = self.buy(long_price, self.order_volume)\n self.long_orders.extend(orders)\n\n if order.vt_orderid in self.short_orders:\n self.short_orders.remove(order.vt_orderid)\n self.trade_count += 1\n long_price = order.price - self.step_price\n if long_price >= self.low_price:\n orders = self.buy(long_price, self.order_volume)\n self.long_orders.extend(orders)\n\n if len(self.short_orders) < self.max_open_orders:\n short_price = order.price + self.step_price * self.max_open_orders\n if short_price <= self.high_price:\n orders = self.short(short_price, self.order_volume)\n self.short_orders.extend(orders)\n\n if not order.is_active():\n if order.vt_orderid in self.long_orders:\n self.long_orders.remove(order.vt_orderid)\n\n elif order.vt_orderid in self.short_orders:\n self.short_orders.remove(order.vt_orderid)\n\n self.put_event()","def place_order(id):\n data = services.validate_order(id, request.json, current_user)\n if \"error\" in data:\n return jsonify({\"message\": data[\"message\"]}), HTTPStatus.BAD_REQUEST\n order_key = f\"PENDING_ORDER#{current_user.id}#{data['event'].id}\"\n session[order_key] = [\n {\n \"package_id\": package.id,\n \"event_id\": data[\"event\"].id,\n \"sponsor_id\": current_user.id,\n \"price\": float(package.price),\n \"name\": package.name\n }\n for package in data[\"packages\"]\n ]\n session[\"user\"] = \"Facebook\"\n return jsonify({\"url\": url_for(\"payments.checkout\", event_id=data[\"event\"].id)})","def test_order_view_permissions(client, user):\n random_user = UserFactory.create(is_staff=False, is_superuser=False)\n order = OrderFactory.create(user=user)\n client.force_login(random_user)\n resp = client.get(reverse(\"order-api\", kwargs={\"pk\": order.id}))\n assert resp.status_code == statuses.HTTP_403_FORBIDDEN\n order.user = random_user\n order.save()\n resp = client.get(reverse(\"order-api\", kwargs={\"pk\": order.id}))\n assert resp.status_code == statuses.HTTP_200_OK","def set_momo_order_checkout(request, payment_mean, *args, **kwargs):\n service = get_service_instance()\n config = service.config\n if getattr(settings, 'DEBUG', False):\n order = parse_order_info(request)\n else:\n try:\n order = parse_order_info(request)\n except:\n return HttpResponseRedirect(reverse('shopping:checkout'))\n order.retailer = service\n order.payment_mean = payment_mean\n order.save() # Save first to generate the Order id\n order = Order.objects.get(pk=order.id) # Grab the newly created object to avoid create another one in subsequent save()\n\n member = request.user\n if member.is_authenticated():\n order.member = member\n else:\n order.aotc = generate_tx_code(order.id, order.anonymous_buyer.auto_inc)\n\n order.rcc = generate_tx_code(order.id, config.rel_id)\n order.save()\n request.session['object_id'] = order.id\n request.session['amount'] = order.total_cost","def place_order(\n self,\n basket,\n total,\n shipping_method,\n shipping_charge,\n user=None,\n shipping_address=None,\n billing_address=None,\n order_number=None,\n status=None,\n request=None,\n **kwargs\n ):\n # Make sure basket isn't empty\n if basket.is_empty:\n # Translators: User facing error message in checkout\n raise ValueError(_(\"Empty baskets cannot be submitted\"))\n\n # Allocate an order number\n if not order_number:\n OrderNumberGenerator = get_class(\"order.utils\", \"OrderNumberGenerator\")\n generator = OrderNumberGenerator()\n order_number = generator.order_number(basket)\n\n # Figure out what status the new order should be\n if not status and hasattr(settings, \"OSCAR_INITIAL_ORDER_STATUS\"):\n status = getattr(settings, \"OSCAR_INITIAL_ORDER_STATUS\")\n\n # Make sure there isn't already an order with this order number\n if Order._default_manager.filter(number=order_number).exists():\n # Translators: User facing error message in checkout\n raise ValueError(\n _(\"There is already an order with number %(order_number)s\")\n % dict(order_number=order_number)\n )\n\n # Open a transaction so that order creation is atomic.\n with transaction.atomic():\n # Create the actual order.Order and order.Line models\n order = self.create_order_model(\n user,\n basket,\n shipping_address,\n shipping_method,\n shipping_charge,\n billing_address,\n total,\n order_number,\n status,\n request=request,\n **kwargs\n )\n for line in basket.all_lines():\n self.create_line_models(order, line)\n self.update_stock_records(line)\n\n # Make sure all the vouchers in the order are active and can actually be used by the order placing user.\n voucher_user = request.user if request and request.user else user\n for voucher in basket.vouchers.select_for_update():\n available_to_user, msg = voucher.is_available_to_user(user=voucher_user)\n if not voucher.is_active() or not available_to_user:\n raise ValueError(msg)\n\n # Record any discounts associated with this order\n for application in basket.offer_applications:\n # Trigger any deferred benefits from offers and capture the resulting message\n application[\"message\"] = application[\"offer\"].apply_deferred_benefit(\n basket, order, application\n )\n # Record offer application results\n if application[\"result\"].affects_shipping:\n # Skip zero shipping discounts\n shipping_discount = shipping_method.discount(basket)\n if shipping_discount <= Decimal(\"0.00\"):\n continue\n # If a shipping offer, we need to grab the actual discount off\n # the shipping method instance, which should be wrapped in an\n # OfferDiscount instance.\n application[\"discount\"] = shipping_discount\n self.create_discount_model(order, application)\n self.record_discount(application)\n\n # Record voucher usage for this order\n for voucher in basket.vouchers.all():\n self.record_voucher_usage(order, voucher, user)\n\n # Send signal for analytics to pick up\n order_placed.send(sender=self, order=order, user=user)\n\n # Done! Return the order.Order model\n return order","def order(self, order):\n\n self._order = order","def order(self, order):\n\n self._order = order","def order(self, order):\n\n self._order = order","def add_order(self, order):\n if order.is_bid:\n if order.price in self.buy_levels:\n limit = self.buy_levels[order.price]\n if limit.size == 0:\n self.buy_tree.size += 1\n limit.add(order)\n self.buy_map[order.uid] = order\n order.parent_limit = limit\n else:\n limit = Limit(order.price)\n limit.add(order)\n self.buy_map[order.uid] = order\n self.buy_tree.insert(limit)\n self.buy_tree.size += 1\n self.buy_levels[order.price] = limit\n order.parent_limit = self.buy_levels[order.price]\n if self.highest_buy is None or order.price > self.highest_buy:\n self.highest_buy = order.price\n else:\n if order.price in self.sell_levels:\n limit = self.sell_levels[order.price]\n if limit.size == 0:\n self.sell_tree.size += 1\n limit.add(order)\n self.sell_map[order.uid] = order\n order.parent_limit = self.sell_levels[order.price]\n else:\n limit = Limit(order.price)\n limit.add(order)\n self.sell_map[order.uid] = order\n self.sell_tree.insert(limit)\n self.sell_tree.size += 1\n self.sell_levels[order.price] = limit\n order.parent_limit = self.sell_levels[order.price]\n if self.lowest_sell is None or order.price < self.lowest_sell:\n self.lowest_sell = order.price\n self.update_book()","def submit_order(request, orderid):\n if request.user.is_staff:\n order = WorkingOrder.objects.get(pk=orderid)\n else:\n order = request.user.workingorder_set.get(id=orderid) \n\n if order.status != BaseOrder.Const.DEALER_EDIT:\n return HttpResponseServerError()\n \n # always submit orders in the context of proper account\n account = order.owner.get_profile().account\n \n if request.method == 'GET': \n form = SubmitForm(instance=order)\n else:\n form = SubmitForm(request.POST, instance=order)\n if form.is_valid():\n order = form.save(commit=False)\n cost = order.cost or decimal.Decimal() \n if cost > account.credit_balance:\n ## users account doesn't have enough juice.. send then to the ecom engine \n ## to pay, then get them back here ...\n order = form.save()\n products = [form.cleaned_data['design_product']]\n option = form.cleaned_data.get('processing_option', None)\n if option:\n products.append(option) \n new_cart(request, products)\n request.method = 'GET' \n return paypal_checkout(request, success_url=reverse('submit-order', args=[orderid]))\n else: \n register_design_order(order.owner, order.owner.get_profile().account, order, cost)\n order = form.save(commit=False)\n order.status = BaseOrder.Const.SUBMITTED\n order.submitted = datetime.now()\n order.save()\n # return HttpResponseRedirect('completed_order_summary', args=[orderid]) # TODO\n return HttpResponseRedirect(reverse('submit-order-completed', args=[order.id])) \n return dict(order=order, form=form)","def on_order(self, order, lvl, bp):\n\t\tS = order.symbol\n\n\t\tneed_bp = order.quantity * self.ticks[S].close\n\t\tif need_bp <= bp: # have enough buying power to place order\n\t\t\tused_bp = need_bp\n\n\t\t\tif lvl == 'hard_stop':\n\t\t\t\tself.on_hard_stop(S)\n\t\t\telif lvl == 'rebalance':\n\t\t\t\tself.on_rebalance(S)\n\n\t\t\tself.pos[order.symbol].confirm_order(order)\n\t\t\tlogger.info(\n\t\t\t\t'Publish Order={} for Strategy={}'\n\t\t\t\t.format(order, self.id)\n\t\t\t)\n\t\t\tself.basic_publish('order', sender=self.id, order=order)\n\t\telse:\n\t\t\tused_bp = 0\n\t\treturn used_bp","def show_completed(self, user):\n\n orders = CartProduct.show_completed(user)\n CartProductsView.show_completed(orders)","def customer_session_started_handler(self, data: dict, **kwargs) -> None:\n restaurant = self.env.manager.restaurant.get(data['restaurant_id'])\n if restaurant.available():\n self.env.publish('order_placed', data={\n 'customer_id': self.id,\n 'restaurant_id': data['restaurant_id']})","def slot_user_order(self, dummy_sender, data):\r\n (price, volume, typ, oid, status) = data\r\n found = False\r\n removed = False # was the order removed?\r\n opened = False # did the order change from 'post-pending' to 'open'\"?\r\n voldiff = 0 # did the order volume change (full or partial fill)\r\n if \"executing\" in status:\r\n # don't need this status at all\r\n return\r\n if \"post-pending\" in status:\r\n # don't need this status at all\r\n return\r\n if \"removed\" in status:\r\n for i in range(len(self.owns)):\r\n if self.owns[i].oid == oid:\r\n order = self.owns[i]\r\n\r\n # work around MtGox strangeness:\r\n # for some reason it will send a \"completed_passive\"\r\n # immediately followed by a \"completed_active\" when a\r\n # market order is filled and removed. Since \"completed_passive\"\r\n # is meant for limit orders only we will just completely\r\n # IGNORE all \"completed_passive\" if it affects a market order,\r\n # there WILL follow a \"completed_active\" immediately after.\r\n if order.price == 0:\r\n if \"passive\" in status:\r\n # ignore it, the correct one with\r\n # \"active\" will follow soon\r\n return\r\n\r\n self.debug(\r\n \"### removing order %s \" % oid,\r\n \"price:\", self.gox.quote2str(order.price),\r\n \"type:\", order.typ)\r\n\r\n # remove it from owns...\r\n self.owns.pop(i)\r\n\r\n # ...and update own volume cache in the bids or asks\r\n self._update_level_own_volume(\r\n order.typ,\r\n order.price,\r\n self.get_own_volume_at(order.price, order.typ)\r\n )\r\n removed = True\r\n break\r\n else:\r\n for order in self.owns:\r\n if order.oid == oid:\r\n found = True\r\n self.debug(\r\n \"### updating order %s \" % oid,\r\n \"volume:\", self.gox.base2str(volume),\r\n \"status:\", status)\r\n voldiff = volume - order.volume\r\n opened = (order.status != \"open\" and status == \"open\")\r\n order.volume = volume\r\n order.status = status\r\n break\r\n\r\n if not found:\r\n # This can happen if we added the order with a different\r\n # application or the gox server sent the user_order message\r\n # before the reply to \"order/add\" (this can happen because\r\n # actually there is no guarantee which one arrives first).\r\n # We will treat this like a reply to \"order/add\"\r\n self.add_own(Order(price, volume, typ, oid, status))\r\n\r\n # The add_own() method has handled everything that was needed\r\n # for new orders and also emitted all signals already, we\r\n # can immediately return here because the job is done.\r\n return\r\n\r\n # update level own volume cache\r\n self._update_level_own_volume(\r\n typ, price, self.get_own_volume_at(price, typ))\r\n\r\n # We try to help the strategy with tracking the orders as good\r\n # as we can by sending different signals for different events.\r\n if removed:\r\n reason = self.gox.msg[\"user_order\"][\"reason\"]\r\n self.signal_own_removed(self, (order, reason))\r\n if opened:\r\n self.signal_own_opened(self, (order))\r\n if voldiff:\r\n self.signal_own_volume(self, (order, voldiff))\r\n self.signal_changed(self, None)\r\n self.signal_owns_changed(self, None)","def click_placeorder(self):\n time.sleep(0.4)\n placeorder = self.driver.find_element_by_css_selector(self.CSS_PLACEORDER)\n action = TouchActions(self.driver)\n action.tap(placeorder).perform()\n WebDriverWait(self.driver, 20).until(EC.visibility_of_element_located((By.CSS_SELECTOR, '#order-no')))\n order_id=self.driver.find_element_by_css_selector('#order-no').text\n return order_id","def test_get_one_order(self):\n self.orders_list.place_order(self.order)\n self.orders_list.place_order(self.order)\n self.assertEqual(self.orders_list.get_order(1), self.order)","def m_ts_OrderAdded(self, sender, e):\r\n print(\"Order was added with price of {0}.\".format(e.Order.LimitPrice))","def select_user_and_add_transaction(self):\n def add_transaction(to_user):\n print(\"Amount of transaction:\")\n amount = input()\n new_transaction = transaction.Transaction(amount)\n to_user.add_transaction(new_transaction)\n\n try:\n selected_user = self.prompt_user_selection()\n add_transaction(selected_user)\n except ValueError:\n print(\"No changes made.\")","def update_orders(comp, order, user_correct, payment_id):\n users_orders = []\n for item in order.items.all():\n users_orders.append(item.id)\n item.is_paid = True\n item.save()\n order.related_competition = comp\n order.payment_id = payment_id\n order.order_date = timezone.now()\n order.answer_correct = user_correct\n order.ordered = True\n order.save()\n return order","def set_order(self, order):\n self.order = order","def set_order(self, order):\n self.order = order","def place(self, order):\n assert isinstance(order, Order)\n order_id = self.__order_count\n self.__pending_orders[order_id] = order\n self.__order_count = order_id + 1\n return order_id","def order(self, stock, amount):\n self.orders[stock] = amount","def get_one_order():","def test_process_order(self):\n checkout_form = self.get_checkout_form()\n checkout_form.submit()\n\n sleep(0.5)\n result = self.browser.find_element_by_id('result')\n self.assertIn(\n \"Your order was placed.\",\n result.text\n )\n self.assertTrue(len(self.client.session['cart']) == 0)\n OrderInfo.objects.get()","def order_ready(request):\n\tcs , status = CookStatus.objects.get_or_create(cook_name=request.user)\n\tif cs.current_order is not None:\n\t\tcs.current_order.status = 'ready-to-serve'\n\t\tcs.current_order.save()\n\t\tcs.current_order = None\n\t\tcs.save()\n\n\treturn HttpResponseRedirect(\"/staff/cook_order_list/\")","def prepare_order(request):\n distributer_id = request.GET.get('distributer')\n billing_address_id = request.GET.get('bill_addr')\n pickup_method = 2 # this is left in place if we ever decide to have door-to-door deliveries - otherwise it should be deleted\n cart = Cart.objects.get_or_create(user=request.user, processed_to_order=False)[0]\n user_bill_addr = UserBillingAddress.objects.get_or_create(pk=billing_address_id, user=request.user)[0]\n distributer = Distributer.objects.get(pk=distributer_id)\n\n # Create order\n order = Order()\n order.user = request.user\n order.distributer = distributer\n order.subtotal = cart.subtotal\n order.tax_total = cart.tax_total\n order.total = cart.total\n order.discount_for_returned_package = 0 #TODO implement returned packaging\n order.to_pay = 0 #TODO implement returned packaging\n order.delivery_method = pickup_method\n order.save()\n\n # create all order items\n for item in cart.cartitem_set.all():\n order_item = OrderItem()\n order_item.order = order\n order_item.item_name = str(item.item)\n order_item.item_price = item.item.price\n order_item.item_quantity = item.quantity\n order_item.item_decimal_quantity = 0 #TODO implement decimal quantity\n order_item.item_unit_of_measure = \"kom\" #TODO implement decimal quantity\n order_item.item_tax_bracket = item.item.tax_bracket\n order_item.item_subtotal = item.line_subtotal\n order_item.item_tax_total = item.line_tax_total\n order_item.item_total = item.line_tax_total\n if item.item.package_type == None:\n order_item.item_package = None\n order_item.item_package_price = 0\n else:\n order_item.item_package = item.item.package_type.type\n order_item.item_package_price = item.item.package_type.price\n order_item.item_package_subtotal = item.line_package_subtotal\n order_item.item_package_tax_total = item.line_package_tax_total\n order_item.item_package_total = item.line_package_total\n order_item.save()\n\n billing_address = OrderBillingAddress()\n billing_address.order = order\n billing_address.name = user_bill_addr.name\n billing_address.surname = user_bill_addr.surname\n billing_address.street_name = user_bill_addr.street_name\n billing_address.street_nr = user_bill_addr.street_nr\n billing_address.zip_code = user_bill_addr.zip_code\n billing_address.city = user_bill_addr.city\n billing_address.country = user_bill_addr.country\n billing_address.vat_nr = user_bill_addr.vat_nr\n billing_address.vat_taxpayer = user_bill_addr.vat_taxpayer\n billing_address.save()\n\n return redirect(reverse('orders_overview', kwargs={'pk': str(order.pk)}))","def __orderAddItem(self, order, item):\n cursor = self.__db.cursor()\n iID = self.__id.getID(\"orderitem\")\n cursor.execute(\"INSERT INTO `orderItems` (`insertionID`, `orderID`, `itemID`) VALUES (%s, %s, %s);\",\n (iID, order, item))\n return iID","def place_order(self, order_event):\n self._check_day_data(order_event.order_time)\n if order_event.order_type == 'MARKET':\n self._fill_market_order(order_event)\n elif order_event.order_type == 'LIMIT':\n if self._check_limit_order(order_event, order_event.order_time):\n pass\n self.resting_orders.append(order_event)","def set_order_done():\n data = select_data_source()\n user = data['user']\n order_id = data['id']\n \n if check_user_permission(user) : return permission_denied_return\n \n db = database.getdb()\n \n ### Check if is valid.\n \n cmd = 'select passed from orders where id==\"{0}\"'.format(order_id)\n order_valid = db.execute(cmd).fetchall()[0][0]\n if order_valid == 0 :\n return finish_invalid_return\n \n ### Check if is done.\n cmd = 'select done from orders where id==\"{0}\"'.format(order_id)\n order_done = db.execute(cmd).fetchall()[0][0]\n if order_done != 0 :\n return finish_done_return\n \n ### All check done.\n ### Set it to done.\n cmd = 'update orders set done=1 where id==\"{0}\"'.format(order_id)\n db.execute(cmd)\n db.commit()\n print('user sets order {0} to be done.'.format(user))\n \n return finish_complete_return","def order_success(self, request):\n order = self.order_from_request(request)\n\n if not order:\n return self.order_new(request)\n\n if not order.balance_remaining:\n self.set_order_on_request(request, order=None)\n\n\n order_data = OrderData.objects.get(order=order)\n o_data = simplejson.loads(order_data.data)\n\n paymentData = {}\n paymentData['delivery_address2'] = o_data['delivery_address2']\n paymentData['billing_address2'] = o_data['billing_address2']\n paymentData['delivery_date'] = o_data['delivery_date']\n paymentData['delivery_state'] = o_data['delivery_state']\n paymentData['billing_state'] = o_data['billing_state']\n paymentData['salutation'] = o_data['salutation']\n paymentData['contact_number'] = o_data['billing_contact_number']\n\n #try:\n oPayment = OrderPayment.objects.get(order=order)\n oPayment.payment_method = o_data['order_payment_method']\n oPayment.data = simplejson.dumps(paymentData)\n oPayment.save()\n #except:\n # pass\n\n \"\"\"\n order update note\n \"\"\"\n notes = o_data['order_notes']\n order.notes = notes\n order.save()\n\n # st_save_helper(request, order)\n\n \"\"\"\n sbid = None\n\n if 'customer_styleboard' in request.session:\n sbid = request.session.get('customer_styleboard').id\n\n if 'personalize_id' in request.session:\n print \"There's a personalize_id\"\n \"\"\"\n\n current_user = User.objects.get(id=int(request.user.id))\n\n if 'ipn_emailed' in o_data and o_data['ipn_emailed']:\n\n pass\n \n else:\n\n emailed = send_email_order(order, current_user, notes, paymentData['contact_number'], self)\n\n logr.info('emailed order confirmation to : %s from order success' % current_user.email)\n\n\n order_data.delete() # not needed after saving to order payment\\\n \n clear_styleboard_session(request)\n\n try:\n del request.session['customer_styleboard']\n del request.session['personalize_id']\n except:\n pass\n\n return self.render(request, 'plata/shop_order_success.html',\n self.get_context(request, {\n 'order': order,\n 'progress': 'success',\n }))","def test_update_order_by_user(client):\n resp = put_json(client, '/v1/orders/1/', {\n \"status\": \"Complete\", },\n headers={'Authorization': 'Bearer ' + user_two(client)})\n assert resp.status_code == 401\n assert b'Not authorized' in resp.data","def order(request):\n if request.method == 'GET':\n try:\n order = Order.objects.filter()\n serializer = OrderSerializer(order, many=True)\n except Order.DoesNotExist:\n message = 'An order does not exist in this ID({})!'.format(order)\n data = {'error': message}\n return Response(data, status=status.HTTP_403_FORBIDDEN)\n\n return Response(serializer.data, status=status.HTTP_200_OK)\n\n if request.method == 'POST':\n user = request.data['user']\n products = request.data['products']\n\n try:\n user_qry = User.objects.get(username=user)\n except User.DoesNotExist:\n message = 'An user does not exist in this name({})!'.format(user)\n data = {'error': message}\n return Response(data, status=status.HTTP_403_FORBIDDEN)\n\n try:\n total_amount = 0\n for prd in products:\n prd_qry = Product.objects.get(id=prd)\n total_amount += prd_qry.price\n except Product.DoesNotExist:\n message = 'An product does not exist in this ID({})!'.format(prd)\n data = {'error': message}\n return Response(data, status=status.HTTP_403_FORBIDDEN)\n\n ordr = Order()\n ordr.user = user_qry\n ordr.total_amount = total_amount\n ordr.save()\n ordr.product.set(products)\n\n data = {'Success': 'Success'}\n return Response(data, status=status.HTTP_201_CREATED)","def send_exchange_request(self, user):\n self.current['user'] = user\n self.send_request(user, self.KM_REQUEST_KEY)","def mark_ready_for_review(self, user: User) -> None:\n from .exceptions import OperationForbiddenError, OrderEmptyError\n\n # If order is not in the \"CREATED\" state, raise an\n # OperationForbiddenError\n if not self.is_created:\n raise OperationForbiddenError(\n self.STATE_CHANGE_FORBIDDEN_ERROR_MSG % {\n 'current_state': Order.OrderState.get_choice_display(\n self.state\n ),\n 'new_state': Order.OrderState.PENDING.choice_display\n }\n )\n\n # If the order's item list is empty, raise an OrderEmptyError\n if not self.orderitem_set.exists():\n raise OrderEmptyError(\n self,\n 'An order should contain at least one Order item before it '\n 'can be marked as \"PENDING\".'\n )\n\n # Update the order to \"PENDING\" state\n self.update(user, state=Order.OrderState.PENDING.choice_value)","def _send_to_market(self, order, is_mine):\n \n \n ord_type = order[self.col_idx['ordtype']]\n if ord_type == \"new\":\n self.mkt.send(is_buy=order[self.col_idx['is_buy']],\n qty=order[self.col_idx['qty']],\n price=order[self.col_idx['price']],\n uid=order[self.col_idx['uid']],\n is_mine=is_mine,\n timestamp=order[self.col_idx['timestamp']])\n elif ord_type == \"cancel\":\n self.mkt.cancel(uid=order[self.col_idx['uid']])\n elif ord_type == \"modif\":\n self.mkt.modif(uid=order[self.col_idx['uid']], \n new_qty=order[self.col_idx['qty']])\n else:\n raise ValueError(f'Unexpected ordtype: {ord_type}')","def sender_order(self, sender_order):\n\n self._sender_order = sender_order","def add_item(self, order_item):\n self.order_items.append(order_item)","def store_order(self, store_name, store_orders,\\\n order_id, order_details):\n #store the details of the order in dictionary\n if order_details[\"ready\"] == True:\n print(\"order number {} is ready\".format(order_id))\n #save the information of all customers\n customers_data = self.firebase.get_data(\"customers\")\n message_information = {\n #store the current information\n 'order_id' : order_id,\n 'store_name' : store_name,\n 'store_orders' : store_orders,\n 'order_details' : order_details,\n #get the order details\n 'food' : order_details['food'],\n 'persons_id' : order_details['id'],\n }\n\n #add the persons details\n customer = customers_data[message_information['persons_id']]\n #print(customer)\n message_information['service'] = customer['service']\n message_information['mobile'] = customer['mobile']\n\n #If there is additional information, append it. Else, just put NA\n try:\n message_information['addit_info'] = order_details['addit_info']\n except:\n message_information['addit_info'] = 'NA'\n\n #Create the message\n self.create_message(message_information)\n\n else:\n # Update time_waited\n order_time = order_details[\"order_time\"]\n if order_time != \"None\":\n if order_details[\"ready\"]!=\"true\" or order_details[\"ready\"]!=\"waiting for collection\":\n time_elapsed = int(time.time()) - order_details[\"order_time\"]\n self.firebase.update([\"orders\", STORE_NAME, order_id], {\"time_waited\": time_elapsed})","def send_admin_about_order(order):\n send_email('Flask ecommerce, New order', sender=current_app.config['ADMINS'][0],\n recipients=current_app.config['ADMINS'],\n text_body=render_template('email/new_order.txt', order=order),\n html_body=render_template('email/new_order.html', order=order))","def test_buy_order(self):\n\n user = self.set_auth_token_header()\n\n # set account buying power\n account = user.account\n account.available_bp = 1000\n account.save()\n\n data = {\n 'stock': 'GOOG',\n 'quantity': 15,\n 'price': 1.25,\n 'order_type': 'BUY'\n }\n\n # Order create API\n url = reverse('orders-list')\n response = self.client.post(url, data=data)\n # order created\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n # account balance should've been updated\n acc = Account.objects.get(user=user)\n self.assertEqual(acc.available_bp, 981.25)\n\n # stock shares should've been updated\n shares = StockShare.objects.get(account=acc)\n self.assertEqual(shares.quantity, 15.0)\n self.assertEqual(shares.total_value, 18.75)\n self.assertEqual(shares.stock.code, 'GOOG')","def place_order(self, instrument, lower, upper, units=1, side_arg='buy'):\n if (side_arg == 'buy'):\n return self.oanda.create_order(self.account_id, instrument=instrument,\n units=units, side=side_arg,\n stopLoss=lower, takeProfit=upper,\n type='market')\n elif (side_arg == 'sell'):\n return self.oanda.create_order(self.account_id, instrument=instrument,\n units=units, side=side_arg,\n stopLoss=upper, takeProfit=lower,\n type='market')","def orders(self, orders):\n\n self._orders = orders","def orders(self, orders):\n\n self._orders = orders","def resolve_order(info, id):\n order = get_node(info, id, only_type=Order)\n user = info.context.user\n if (order.user == user or user.get_all_permissions() & {\n 'order.view_order', 'order.edit_order'}):\n return order","def click_place_order(self):\n WebDriverWait(self.driver, 20).until(\n EC.presence_of_element_located((By.CSS_SELECTOR, self.CSS_PLACEORDER)))\n placeorder = self.driver.find_element_by_css_selector(self.CSS_PLACEORDER)\n action = TouchActions(self.driver)\n action.tap(placeorder).perform()","def generate_initial_order(self):\n self.ensure_one()\n # Add only active lines\n agreement_lines = self.mapped('agreement_line').filtered('active_chk')\n order = self.create_order(self.start_date, agreement_lines)\n self.write({'state': 'first'})\n order.signal_workflow('order_confirm')\n # Return view with order created\n return {\n 'domain': \"[('id', '=', %s)]\" % order.id,\n 'view_type': 'form',\n 'view_mode': 'form',\n 'res_model': 'sale.order',\n 'context': self.env.context,\n 'res_id': order.id,\n 'view_id': [self.env.ref('sale.view_order_form').id],\n 'type': 'ir.actions.act_window',\n 'nodestroy': True\n }","def buy(self, irc, msg, args, optlist, amount, thing, price, otherthing, notes):\n self.db.deleteExpired(self.registryValue('orderExpiry'))\n gpgauth = self._checkGPGAuth(irc, msg.prefix)\n if gpgauth is None:\n irc.error(\"For identification purposes, you must be identified via GPG \"\n \"to use the order book.\")\n return\n results = self.db.getByNick(gpgauth['nick'])\n if len(results) >= self.registryValue('maxUserOpenOrders'):\n irc.error(\"You may not have more than %s outstanding open orders.\" % \\\n self.registryValue('maxUserOpenOrders'))\n return\n extratime = 0\n if dict(optlist).has_key('long'):\n extratime = self.registryValue('longOrderDuration')\n trust = self._getTrust(irc, 'nanotube', gpgauth['nick'])\n sumtrust = sum([t for t,n in trust])\n if sumtrust < self.registryValue('minTrustForLongOrders'):\n irc.error(\"You must have a minimum of %s cumulative trust at \"\n \"level 1 and level 2 from nanotube to \"\n \"to place long orders.\" % (self.registryValue('minTrustForLongOrders'),))\n return\n orderid = self.db.buy(gpgauth['nick'], msg.host, amount, thing, price, otherthing, notes, extratime)\n irc.reply(\"Order id %s created.\" % (orderid,))\n if not world.testing:\n irc.queueMsg(ircmsgs.privmsg(\"#bitcoin-otc-ticker\",\n \"#%s || %s || BUY %s %s @ %s %s || %s\" % (orderid,\n gpgauth['nick'],\n amount,\n thing,\n self._getIndexedValue(price),\n otherthing,\n notes,)))","def test_place_multiple_orders(self):\n self.orders_list.place_order(self.order)\n self.orders_list.place_order(self.order)\n self.orders_list.place_order(self.order)\n self.assertEqual(len(self.orders_list.orders_list), 3)\n self.assertEqual(self.orders_list.orders_list[2].order_id, 2)","def PlaceOutgoingOrder(self, state):\r\n amountToOrder , policy_action = self.policy.calculate_order( state )\r\n\r\n self.outgoingOrdersQueue.PushEnvelope(amountToOrder)\r\n self.lastOrderQuantity = amountToOrder\r\n \r\n return policy_action","def __order_status(self):\n log.debug(\"Displaying __order_status\")\n # Find the latest orders\n orders = self.session.query(db.Order) \\\n .filter(db.Order.user == self.user) \\\n .order_by(db.Order.creation_date.desc()) \\\n .limit(20) \\\n .all()\n # Ensure there is at least one order to display\n if len(orders) == 0:\n self.bot.send_message(self.chat.id, self.loc.get(\"error_no_orders\"))\n # Display the order status to the user\n for order in orders:\n self.bot.send_message(self.chat.id, order.text(w=self, session=self.session, user=True))\n # TODO: maybe add a page displayer instead of showing the latest 5 orders","def email_order(request, order, user_correct):\n users_entries = get_entries(order)\n\n html_email = loader.render_to_string(\n 'email_templates/order_complete.html',\n {\n 'order': order,\n 'user': request.user.first_name,\n 'user_correct': user_correct,\n 'users_entries': users_entries\n }\n )\n message = strip_tags(html_email)\n send_mail(\n 'Your Order for Project Parts',\n message=message,\n from_email='noreply@projectparts.com',\n fail_silently=False,\n connection=None,\n recipient_list=[str(request.user.email)],\n html_message=html_email\n )","def on_order(self, order: OrderData):\n pass\n\n # self.write_log(f\"on_order: status:{order.status}, orderid: {order.vt_orderid}, offset:{order.offset}, price:{order.price}, volume:{order.volume}, traded: {order.traded}\")\n # self.put_event()","def orderInfo(self, orderInfo):\r\n\r\n self._orderInfo = orderInfo","def place_order(self, order: Order) -> None:\n\n if order.id in [order.id for order in self.orders]:\n raise OrderAlreadyCreatedError(order)\n\n if not order.symbol.is_enabled:\n raise SymbolIsNotEnabledError(order.symbol)\n\n t = Thread(target=self.__place_order, args=(order,))\n t.start()\n\n self.__sort_orders_by_price()","def place_order(cls, order: 'Order') -> 'Order':\n counter_order_type = OrderType.SELL.value if order.type == OrderType.BUY.value else OrderType.BUY.value\n counter_orders = None\n with transaction.atomic():\n if counter_order_type == OrderType.SELL.value:\n counter_orders = cls.objects.select_for_update().filter(\n type=counter_order_type,\n instrument=order.instrument,\n price__lte=order.price).order_by('price', 'created_at_dt')\n elif counter_order_type == OrderType.BUY.value:\n counter_orders = cls.objects.select_for_update().filter(\n type=counter_order_type,\n instrument=order.instrument,\n price__gte=order.price).order_by('-price', 'created_at_dt')\n if not counter_orders:\n # place order into the order book\n order.save()\n return order\n for counter_order in counter_orders:\n order, counter_order, *balances = cls._trade_orders(\n order, counter_order)\n order.save()\n counter_order.save()\n for balance in balances:\n balance.save()\n if order.status == OrderStatus.COMPLETED:\n return order\n return order","def postOrder(self,Account=None,side='buy',price=0,amount=0,order=None):\n\t\tif order is not None:\n\t\t\tif order.Posted is True:\n\t\t\t\tprint(\"No order posted-order object might have been used\")\n\t\t\t\treturn -1\n\t\telse:\n\t\t\tif Account is None:\n\t\t\t\tprint(\"No order is posted-no account was provided\")\n\t\t\t\treturn -1\n\t\t\telse:\n\t\t\t\tprice=Decimal(price)\n\t\t\t\tamount=Decimal(amount)\n\t\t\t\torder=Order.Order(Account,side,price,amount)\n\t\tcharge=False\n\t\tif side is 'buy':\n\t\t\t charge=Account.withdraw(side,price*amount) \n\t\telse:\n\t\t\tcharge=Account.withdraw(side,amount)\n\t\tif charge is False:\n\t\t\treturn -1\n\t\telse:\n\t\t\tif self.register(order) is False:\n\t\t\t\treturn -1\n\t\t\telse:\n\t\t\t\torder.Posted=True\n\t\t\t\tprint(\"\\n\"+order.Side.upper()+\" order \"+str(order.ID)+\" from account \"+str(order.Account.ID)+\" is posted\\n\")\n\t\t\t\treturn order.ID","def placeOrder(self, dish):\n # Get a random waiter\n waiter = choice(self.waiters)\n # Place the order, returning the result\n food = waiter.takeOrder(dish, self.chefs)\n return food","def place_sell_order(self):\n price = request.form[\"price\"]\n stocks = request.form[\"stocks\"]\n trader_id = request.form[\"trader_id\"]\n self.market.place_sell_order(trader_id, price, stocks)\n return \"\"","def process_order(self, order_event : event.EventOrder) :\n pass","def orderWatch(self, order):\r\n\t\tself.orders.append(order)","def checkout(self, request, order):\n if not request.user.is_authenticated():\n if request.method == 'POST' and '_login' in request.POST:\n loginform = AuthenticationForm(data=request.POST, prefix='login')\n\n if loginform.is_valid():\n user = loginform.get_user()\n auth.login(request, user)\n\n order.user = user\n order.save()\n return HttpResponseRedirect('.')\n else:\n loginform = AuthenticationForm(prefix='login')\n else:\n loginform = None\n\n if order.status < order.CHECKOUT:\n order.update_status(order.CHECKOUT, 'Checkout process started')\n\n OrderForm = self.checkout_form(request, order)\n\n\n\n orderform_kwargs = {\n 'prefix': 'order',\n 'instance': order,\n 'request': request,\n 'shop': self,\n }\n\n if request.method == 'POST' and '_checkout' in request.POST:\n orderform = OrderForm(request.POST, **orderform_kwargs)\n\n if orderform.is_valid():\n notes = request.POST.get('order-notes')\n same_as_billing = request.POST.get('order-shipping_same_as_billing')\n delivery_address = request.POST.get('order-shipping_address')\n billing_address = request.POST.get('order-billing_address')\n delivery_address2 = request.POST.get('order-shipping_address2')\n billing_address2 = request.POST.get('order-billing_address2')\n delivery_date = request.POST.get('order-shipping_date')\n delivery_state = request.POST.get('order-shipping_state')\n billing_state = request.POST.get('order-billing_state')\n delivery_city = request.POST.get('order-shipping_city')\n billing_city = request.POST.get('order-billing_city')\n delivery_zip_code = request.POST.get('order-shipping_zip_code')\n billing_zip_code = request.POST.get('order-billing_zip_code')\n salutation = request.POST.get('order-billing_salutation')\n billing_country = request.POST.get('order-billing_country')\n delivery_country = request.POST.get('order-shipping_country')\n billing_first_name = request.POST.get('order-billing_first_name')\n billing_last_name = request.POST.get('order-billing_last_name')\n email = request.POST.get('order-email')\n billing_contact_number = request.POST.get('order-billing_contact_number')\n\n if same_as_billing:\n billing_address = delivery_address\n billing_address2 = delivery_address2\n billing_state = delivery_state\n billing_city = delivery_city\n billing_zip_code = delivery_zip_code\n billing_country = delivery_country\n\n \n # request.session['order-payment_method'] = request.POST.get('order-payment_method','')\n\n custom_data = {}\n custom_data['order_payment_method'] = request.POST.get('order-payment_method','')\n custom_data['order_notes'] = request.POST.get('order-notes','')\n custom_data['delivery_address2'] = delivery_address2\n custom_data['billing_address2'] = billing_address2\n custom_data['delivery_date'] = delivery_date\n custom_data['delivery_state'] = delivery_state\n custom_data['billing_state'] = billing_state\n custom_data['salutation'] = salutation\n custom_data['billing_country'] = billing_country\n custom_data['shipping_country'] = delivery_country\n custom_data['billing_contact_number'] = billing_contact_number\n\n try:\n order_data = OrderData.objects.get(order=order)\n except:\n order_data = OrderData()\n\n order_data.order = order\n order_data.data = simplejson.dumps(custom_data)\n order_data.save()\n \n \n orderform.save(\n notes=notes, \n billing_salutation=salutation,\n same_as_billing=True if same_as_billing else False,\n shipping_address=delivery_address,\n billing_address=billing_address,\n shipping_address2=delivery_address2,\n billing_address2=billing_address2,\n shipping_state=delivery_state,\n billing_state=billing_state,\n shipping_city=delivery_city,\n billing_city=billing_city,\n shipping_zip_code=delivery_zip_code,\n billing_zip_code=billing_zip_code,\n billing_country=billing_country,\n shipping_country=delivery_country,\n billing_first_name=billing_first_name,\n billing_last_name=billing_last_name,\n email=email\n )\n\n \"\"\"\n added notes\n \"\"\"\n return redirect('plata_shop_discounts')\n else:\n\n orderform = OrderForm(**orderform_kwargs)\n \n\n return self.render_checkout(request, {\n 'order': order,\n 'loginform': loginform,\n 'orderform': orderform,\n 'progress': 'checkout'\n })","def handle_add(self, controller):\n \n try:\n pizza = controller.customer.pizza ## get a reference to pizza object of the customer\n \n except Exception:\n showinfo(title='Pop-up', message=\"No Pizza Created Yet.\")\n return\n \n else:\n # create an order if not exist, and add pizza to order\n c = controller.customer\n self.onPress(c) ## update requested data\n if not c.my_order:\n c.my_order = Order(c.name, c.address, c.id)\n \n c.AddToOrder()\n controller.show_frame(PageTwo) ## go to my order page"],"string":"[\n \"def place_order(self):\\n\\n order_total = self.get_order_total()\\n\\n if self.person.can_afford(order_total):\\n print 'This person is stinkin rich!'\\n else:\\n print \\\"No soup for you!\\\"\",\n \"def test_order_by_user(self):\\n self.fill_session_cart()\\n self.client.post(self.CHECKOUT_URL, self.build_checkout_form())\\n self.assertEqual(OrderInfo.objects.get().user,\\n USER_MODEL.objects.get())\",\n \"def place_buy_order(self):\\n price = request.form[\\\"price\\\"]\\n stocks = request.form[\\\"stocks\\\"]\\n trader_id = request.form[\\\"trader_id\\\"]\\n self.market.place_buy_order(trader_id, price, stocks)\\n return \\\"\\\"\",\n \"def place(self):\\n\\n return super().place(self._order_params)\",\n \"def test_process_order_adds_coins_to_users_account_if_coins_in_order(self):\\n self.client.force_login(User.objects.get_or_create(username=\\\"coinsuser\\\", email=\\\"coinsuser@email.com\\\")[0])\\n\\n current_user = User.objects.get(username=\\\"coinsuser\\\")\\n\\n request = HttpRequest()\\n engine = import_module(settings.SESSION_ENGINE)\\n session_key = None\\n request.session = engine.SessionStore(session_key)\\n\\n # below two lines of code from https://stackoverflow.com/questions/11938164/why-dont-my-django-unittests-know-that-messagemiddleware-is-installed\\n # fixes bug where test fails because unitest\\n messages = FallbackStorage(request)\\n setattr(request, '_messages', messages)\\n\\n random_coins_store_item = choice(StoreItem.objects.filter(is_coins=True))\\n cart_add(request, random_coins_store_item.id)\\n\\n process_order(request, current_user)\\n\\n current_users_coins = UserCoins.objects.get(user=current_user).coins\\n self.assertEqual(current_users_coins, random_coins_store_item.coins_amount)\",\n \"def slot_userorder(self, gox, (price, volume, typ, oid, status)):\\r\\n pass\",\n \"def place_order(self,name,address,quantity,contact):\\n self.name = name\\n self.address = address\\n self.quantity = quantity\\n self.contact = contact\\n self.event = \\\"user_place_order\\\"\\n self.query_1 = \\\"SELECT meal_price FROM menu WHERE meal_name=%s\\\"\\n self.input_1 = (self.name,)\\n self.query_2 = \\\"\\\"\\\"INSERT INTO public.orders (order_price,order_delivery_address,order_quantity,\\n order_contact,order_status,user_id, meal_name) VALUES (%s,%s,%s,%s,%s,%s,%s)\\\"\\\"\\\"\\n self.message = \\\"Order placed successfully.\\\"\\n self.error = \\\"Unable to place order. The meal is not available\\\"\",\n \"def on_order(self, order: OrderData):\\n # print(\\\"on_order\\\")\\n # print(order)\\n pass\",\n \"def place_order(self, **kwargs):\\r\\n create_options = self._generate_create_dict(**kwargs)\\r\\n return self.client['Product_Order'].placeOrder(create_options)\",\n \"def place_order(self, market: Market, order) -> bool:\\n runner_context = self.get_runner_context(*order.lookup)\\n if self.validate_order(runner_context, order):\\n runner_context.place()\\n market.place_order(order)\\n return True\\n else:\\n return False\",\n \"def on_order(self, order: OrderData):\\n pass\",\n \"def on_order(self, order: OrderData):\\n pass\",\n \"def on_order(self, order: OrderData):\\n pass\",\n \"def get_one_user_orders(self,user_id):\\n\\n sql = \\\"SELECT * FROM parcel_orders WHERE user_id='{}'\\\".format(user_id)\\n self.db_object.cursor.execute(sql)\\n placed_orders = self.db_object.cursor.fetchall()\\n return placed_orders\",\n \"def test_order_from_user(self):\\n new_user = self.create_test_user()\\n new_order = OrderInfo.objects.create(user=new_user)\\n self.assertEqual(new_order.user, new_user)\",\n \"def PlaceOrder(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def test_place_one_order(self):\\n self.orders_list.place_order(self.order)\\n self.assertEqual(len(self.orders_list.orders_list), 1)\\n self.assertEqual(self.orders_list.orders_list[0].order_id, 0)\",\n \"def user_open_orders(request):\\n qset = Order.objects.filter(\\n receiver=request.user\\n ).order_by(\\n '-date_placed'\\n )\\n for order in qset:\\n min_bid = get_min_bid(order)\\n if not min_bid:\\n order.min_bid = \\\"No current bids\\\"\\n else:\\n order.min_bid = min_bid\\n\\n data_dict = {}\\n data_dict.update({ k : v.value\\n for (k,v)\\n in OrderAction.Action._member_map_.items()\\n })\\n data_dict['orders'] = qset\\n\\n return render(request, 'friendship/user_open_orders.html', data_dict)\",\n \"def create_order():\",\n \"def order(self, request):\\n is_auth = request.session.get(\\\"is_auth\\\", False)\\n if not is_auth:\\n return HttpResponseRedirect('/crisis')\\n\\n uid = request.session['uid']\\n context = RUNNING_INFO.get(uid, {})\\n context.update({\\\"is_auth\\\": is_auth,\\n \\\"is_daily\\\": request.session.get(\\\"is_daily\\\", False),\\n \\\"is_leader\\\": request.session.get(\\\"is_leader\\\", False),\\n \\\"entity_list\\\": ENTITY,\\n \\\"parts_list\\\": PARTS,\\n \\\"detail_info_list\\\": DETAIL_INFO})\\n\\n if \\\"priority\\\" not in context:\\n priority = {}\\n for item in ARMY:\\n priority.update({item: 1})\\n context.update({\\\"priority\\\": priority})\\n\\n if context.get(\\\"is_run\\\", False):\\n context.update({\\\"left_time\\\": self.utils.get_remaining_time(uid),\\n \\\"order\\\": self.utils.get_current_unit_order(uid)})\\n \\\"\\\"\\\" Context Example\\n context = {\\\"username\\\": self.utils.get_user_name(uid),\\n \\\"is_run\\\": False,\\n \\\"is_auth\\\": is_auth,\\n \\\"resource\\\": {\\\"money\\\": 100, \\\"food\\\": 200, \\\"fuel\\\": 300},\\n \\\"entity\\\": {\\\"armor_composite\\\": 1, \\\"armor_plate\\\": 2, \\\"control_block\\\": 3,\\n \\\"gun_receiver\\\": 4, \\\"kevlar_fiber\\\": 5, \\\"laser_aimer\\\": 6,\\n \\\"powder_charge\\\": 7, \\\"rare_item\\\": 8, \\\"tnt_charge\\\": 9},\\n \\\"parts\\\": {\\\"artillery_armor\\\": 1, \\\"artillery_chassis\\\": 2, \\\"artillery_shell\\\": 3, \\\"detonator\\\": 4,\\n \\\"gunner_armor\\\": 5, \\\"gunner_gun\\\": 6, \\\"jeep_armor\\\": 7, \\\"jeep_gun\\\": 8, \\\"sniper_armor\\\": 9,\\n \\\"sniper_gun\\\": 10, \\\"soldier_gun\\\": 11, \\\"tank_chassis\\\": 12, \\\"thrower_armor\\\": 13,\\n \\\"thrower_gun\\\": 14, \\\"wave_emitter\\\": 15},\\n 'order': {'soldier': 1, 'thrower': 4, 'artillery': 8, 'gunner': 2, 'base_artillery': 7, 'jeep': 6, 'artillery_emp': 9, 'base_tank': 5, 'artillery_cassete': 0, 'sniper': 3}\\n }\\n \\\"\\\"\\\"\\n\\n if request.method == \\\"POST\\\":\\n if \\\"start\\\" in request.POST:\\n order, priority = {}, {}\\n data = dict(request.POST)\\n\\n for item in ARMY:\\n try:\\n count = int(data.get(item, [''])[0])\\n except:\\n count = 0\\n try:\\n prior = int(data.get(\\\"%s_priority\\\" % item, [''])[0])\\n except:\\n prior = 1\\n order.update({item: count})\\n priority.update({item: prior})\\n\\n context.update({\\\"is_run\\\": True,\\n \\\"order\\\": order,\\n \\\"priority\\\": priority,\\n \\\"left_time\\\": self.utils.get_remaining_time(uid)})\\n\\n RUNNING_INFO.update({uid: context})\\n self.utils.start_gather(uid, context)\\n elif \\\"stop\\\" in request.POST:\\n uid = request.session['uid']\\n context = RUNNING_INFO.get(uid, {})\\n context.update({\\\"is_run\\\": False, \\\"left_time\\\": \\\"00:00:00\\\"})\\n RUNNING_INFO.update({uid: context})\\n self.utils.stop_gather(uid)\\n\\n return render_to_response(\\\"crisis/order.html\\\",\\n context,\\n context_instance=RequestContext(request))\",\n \"def test_make_order_by_authorised_user(self):\\n\\n\\t\\tres = self.login_user()\\n\\t\\taccess_token = json.loads(res.data.decode())['access_token']\\n\\n\\t\\tresponse = self.client().post(\\n\\t\\t\\t'/api/v2/orders',\\n\\t\\t\\theaders={\\\"x-access-token\\\": access_token},\\n\\t\\t\\tdata = json.dumps(\\n\\t\\t\\t\\tself.order_data) , content_type = 'application/json')\\n\\t\\tresult = json.loads(response.data)\\n\\t\\tself.assertEqual(result[\\\"message\\\"], \\\"Order succesfully posted\\\")\\n\\t\\tself.assertEqual(response.status_code, 201)\",\n \"def update_order():\",\n \"def update_order():\",\n \"def save(self, *args, **kwargs):\\n self.order_total = self.membership.price\\n if not self.order_number:\\n self.order_number = self._generate_order_number()\\n super().save(*args, **kwargs)\",\n \"def cook_order(request):\\n\\torder_id = request.GET.get('order_id', 0)\\n\\tcs , status = CookStatus.objects.get_or_create(cook_name=request.user)\\n\\n\\tif cs.current_order is None:\\n\\t\\tcs.current_order = Order.objects.get(id=order_id)\\n\\t\\tcs.current_order.status = 'cooking'\\n\\t\\tcs.current_order.tikchen = request.user.username\\n\\t\\tcs.current_order.save()\\n\\t\\tcs.save()\\n\\n\\treturn HttpResponseRedirect(\\\"/staff/cook_order_list/\\\")\",\n \"def create_order(request, order, transaction_id):\\n\\n\\n order.transaction_id = transaction_id\\n print transaction_id\\n #order.ip_address = request.META.get('REMOTE_ADDR')\\n order.user = None\\n #if request.user.is_authenticated():\\n # order.user = request.user\\n order.status = Order.SUBMITTED\\n\\n DBSession.add(order)\\n\\n\\n if order:\\n \\\"\\\"\\\" if the order save succeeded \\\"\\\"\\\"\\n cart_items = cart.get_cart_items(request).all()\\n print \\\"The items in the cart are: \\\", len(cart_items)\\n\\n for ci in cart_items:\\n \\\"\\\"\\\" create order item for each cart item \\\"\\\"\\\"\\n\\n print \\\"The product is \\\", ci.product\\n oi = OrderItem()\\n oi.order_id = order.id\\n oi.order = order\\n oi.quantity = ci.quantity\\n print \\\"The product id is \\\", ci.product.id\\n oi.product_id = ci.product.id\\n oi.product = ci.product\\n\\n oi.price = ci.price # now using @property\\n DBSession.add(oi)\\n\\n # all set, clear the cart\\n cart.empty_cart(request)\\n\\n ## save profile info for future orders\\n #if request.user.is_authenticated():\\n # from ecomstore.accounts import profile\\n #\\n # profile.set(request)\\n\\n return order\",\n \"def place_order(self, custom_id=None, **params):\\n self.conn.send('newOrder', custom_id=custom_id, **params)\",\n \"def order(self, typ, price, volume):\\r\\n self.count_submitted += 1\\r\\n self.client.send_order_add(typ, price, volume)\",\n \"def make_order(self) -> Order:\\n return Order.objects.create(self.user, customer=self)\",\n \"def place(self, order_params):\\n\\n # Prevent multiple invocations with the same OID.\\n if self.oid() is not None:\\n return self.oid()\\n\\n # Common params across all orders\\n # https://docs.gdax.com/?python#orders\\n data = {\\n 'side': self.__side,\\n 'type': self.__order_type,\\n 'product_id': self.__product,\\n }\\n data.update(order_params)\\n\\n log.info('placing ORDER')\\n self.__resp = httpapi.post(\\n common.api_url + 'orders',\\n data=json.dumps(data),\\n auth=common.auth,\\n )\\n\\n return self.oid(), self.__resp\",\n \"def place_order(creator, **kwargs):\\n if \\\"shipping_method\\\" not in kwargs:\\n kwargs[\\\"shipping_method\\\"] = Free()\\n\\n shipping_charge = kwargs[\\\"shipping_method\\\"].calculate(kwargs[\\\"basket\\\"])\\n\\n kwargs[\\\"total\\\"] = calculators.OrderTotalCalculator().calculate(\\n basket=kwargs[\\\"basket\\\"],\\n shipping_charge=shipping_charge,\\n surcharges=kwargs[\\\"surcharges\\\"],\\n )\\n kwargs[\\\"shipping_charge\\\"] = shipping_charge\\n\\n return creator.place_order(**kwargs)\",\n \"async def place_order(request: web.Request, body) -> web.Response:\\n body = Order.from_dict(body)\\n return web.Response(status=200)\",\n \"def create(self):\\n return self.account.create_order(self)\",\n \"def request_orders(self):\\r\\n if self.use_http():\\r\\n self.enqueue_http_request(\\\"money/orders\\\", {}, \\\"orders\\\")\\r\\n else:\\r\\n self.send_signed_call(\\\"private/orders\\\", {}, \\\"orders\\\")\",\n \"def get_order(self):\\n #store the orders for the current cycle inside the class\\n self.orders = self.firebase.get_data(\\\"orders\\\")\",\n \"def _on_op_private_user_order(self, msg):\\r\\n order = msg[\\\"user_order\\\"]\\r\\n oid = order[\\\"oid\\\"]\\r\\n\\r\\n # there exist 3 fundamentally different types of user_order messages,\\r\\n # they differ in the presence or absence of certain parts of the message\\r\\n\\r\\n if \\\"status\\\" in order:\\r\\n # these are limit orders or market orders (new or updated).\\r\\n #\\r\\n # we also need to check whether they belong to our own gox instance,\\r\\n # since they contain currency this is easy, we compare the currency\\r\\n # and simply ignore mesages for all unrelated currencies.\\r\\n if order[\\\"currency\\\"] == self.curr_quote and order[\\\"item\\\"] == self.curr_base:\\r\\n volume = int(order[\\\"amount\\\"][\\\"value_int\\\"])\\r\\n typ = order[\\\"type\\\"]\\r\\n status = order[\\\"status\\\"]\\r\\n if \\\"price\\\" in order:\\r\\n # these are limit orders (new or updated)\\r\\n price = int(order[\\\"price\\\"][\\\"value_int\\\"])\\r\\n else:\\r\\n # these are market orders (new or updated)\\r\\n price = 0\\r\\n self.signal_userorder(self, (price, volume, typ, oid, status))\\r\\n\\r\\n else:\\r\\n # these are remove messages (cancel or fill)\\r\\n # here it is a bit more expensive to check whether they belong to\\r\\n # this gox instance, they don't carry any other useful data besides\\r\\n # the order id and the remove reason but since a remove message can\\r\\n # only affect us if the oid is in the owns list already we just\\r\\n # ask the orderbook instance whether it knows about this order\\r\\n # and ignore all the ones that have unknown oid\\r\\n if self.orderbook.have_own_oid(oid):\\r\\n # they don't contain a status field either, so we make up\\r\\n # our own status string to make it more useful. It will\\r\\n # be \\\"removed:\\\" followed by the reason. Possible reasons are:\\r\\n # \\\"requested\\\", \\\"completed_passive\\\", \\\"completed_active\\\"\\r\\n # so for example a cancel would be \\\"removed:requested\\\"\\r\\n # and a limit order fill would be \\\"removed:completed_passive\\\".\\r\\n status = \\\"removed:\\\" + order[\\\"reason\\\"]\\r\\n self.signal_userorder(self, (0, 0, \\\"\\\", oid, status))\",\n \"def order(self, order):\\n self._order = order\",\n \"def subscribe_user_orders(self, update_handler):\\n pass\",\n \"def on_order(self, order: OrderData):\\n\\n if order.vt_orderid not in (self.short_orders + self.long_orders):\\n return\\n\\n self.pos_calculator.update_position(order)\\n\\n self.current_pos = self.pos_calculator.pos\\n self.avg_price = self.pos_calculator.avg_price\\n\\n if order.status == Status.ALLTRADED:\\n\\n if order.vt_orderid in self.long_orders:\\n self.long_orders.remove(order.vt_orderid)\\n self.trade_count += 1\\n\\n short_price = order.price + self.step_price\\n if short_price <= self.high_price:\\n orders = self.short(short_price, self.order_volume)\\n self.short_orders.extend(orders)\\n\\n if len(self.long_orders) < self.max_open_orders:\\n long_price = order.price - self.step_price * self.max_open_orders\\n if long_price >= self.low_price:\\n orders = self.buy(long_price, self.order_volume)\\n self.long_orders.extend(orders)\\n\\n if order.vt_orderid in self.short_orders:\\n self.short_orders.remove(order.vt_orderid)\\n self.trade_count += 1\\n long_price = order.price - self.step_price\\n if long_price >= self.low_price:\\n orders = self.buy(long_price, self.order_volume)\\n self.long_orders.extend(orders)\\n\\n if len(self.short_orders) < self.max_open_orders:\\n short_price = order.price + self.step_price * self.max_open_orders\\n if short_price <= self.high_price:\\n orders = self.short(short_price, self.order_volume)\\n self.short_orders.extend(orders)\\n\\n if not order.is_active():\\n if order.vt_orderid in self.long_orders:\\n self.long_orders.remove(order.vt_orderid)\\n\\n elif order.vt_orderid in self.short_orders:\\n self.short_orders.remove(order.vt_orderid)\\n\\n self.put_event()\",\n \"def place_order(id):\\n data = services.validate_order(id, request.json, current_user)\\n if \\\"error\\\" in data:\\n return jsonify({\\\"message\\\": data[\\\"message\\\"]}), HTTPStatus.BAD_REQUEST\\n order_key = f\\\"PENDING_ORDER#{current_user.id}#{data['event'].id}\\\"\\n session[order_key] = [\\n {\\n \\\"package_id\\\": package.id,\\n \\\"event_id\\\": data[\\\"event\\\"].id,\\n \\\"sponsor_id\\\": current_user.id,\\n \\\"price\\\": float(package.price),\\n \\\"name\\\": package.name\\n }\\n for package in data[\\\"packages\\\"]\\n ]\\n session[\\\"user\\\"] = \\\"Facebook\\\"\\n return jsonify({\\\"url\\\": url_for(\\\"payments.checkout\\\", event_id=data[\\\"event\\\"].id)})\",\n \"def test_order_view_permissions(client, user):\\n random_user = UserFactory.create(is_staff=False, is_superuser=False)\\n order = OrderFactory.create(user=user)\\n client.force_login(random_user)\\n resp = client.get(reverse(\\\"order-api\\\", kwargs={\\\"pk\\\": order.id}))\\n assert resp.status_code == statuses.HTTP_403_FORBIDDEN\\n order.user = random_user\\n order.save()\\n resp = client.get(reverse(\\\"order-api\\\", kwargs={\\\"pk\\\": order.id}))\\n assert resp.status_code == statuses.HTTP_200_OK\",\n \"def set_momo_order_checkout(request, payment_mean, *args, **kwargs):\\n service = get_service_instance()\\n config = service.config\\n if getattr(settings, 'DEBUG', False):\\n order = parse_order_info(request)\\n else:\\n try:\\n order = parse_order_info(request)\\n except:\\n return HttpResponseRedirect(reverse('shopping:checkout'))\\n order.retailer = service\\n order.payment_mean = payment_mean\\n order.save() # Save first to generate the Order id\\n order = Order.objects.get(pk=order.id) # Grab the newly created object to avoid create another one in subsequent save()\\n\\n member = request.user\\n if member.is_authenticated():\\n order.member = member\\n else:\\n order.aotc = generate_tx_code(order.id, order.anonymous_buyer.auto_inc)\\n\\n order.rcc = generate_tx_code(order.id, config.rel_id)\\n order.save()\\n request.session['object_id'] = order.id\\n request.session['amount'] = order.total_cost\",\n \"def place_order(\\n self,\\n basket,\\n total,\\n shipping_method,\\n shipping_charge,\\n user=None,\\n shipping_address=None,\\n billing_address=None,\\n order_number=None,\\n status=None,\\n request=None,\\n **kwargs\\n ):\\n # Make sure basket isn't empty\\n if basket.is_empty:\\n # Translators: User facing error message in checkout\\n raise ValueError(_(\\\"Empty baskets cannot be submitted\\\"))\\n\\n # Allocate an order number\\n if not order_number:\\n OrderNumberGenerator = get_class(\\\"order.utils\\\", \\\"OrderNumberGenerator\\\")\\n generator = OrderNumberGenerator()\\n order_number = generator.order_number(basket)\\n\\n # Figure out what status the new order should be\\n if not status and hasattr(settings, \\\"OSCAR_INITIAL_ORDER_STATUS\\\"):\\n status = getattr(settings, \\\"OSCAR_INITIAL_ORDER_STATUS\\\")\\n\\n # Make sure there isn't already an order with this order number\\n if Order._default_manager.filter(number=order_number).exists():\\n # Translators: User facing error message in checkout\\n raise ValueError(\\n _(\\\"There is already an order with number %(order_number)s\\\")\\n % dict(order_number=order_number)\\n )\\n\\n # Open a transaction so that order creation is atomic.\\n with transaction.atomic():\\n # Create the actual order.Order and order.Line models\\n order = self.create_order_model(\\n user,\\n basket,\\n shipping_address,\\n shipping_method,\\n shipping_charge,\\n billing_address,\\n total,\\n order_number,\\n status,\\n request=request,\\n **kwargs\\n )\\n for line in basket.all_lines():\\n self.create_line_models(order, line)\\n self.update_stock_records(line)\\n\\n # Make sure all the vouchers in the order are active and can actually be used by the order placing user.\\n voucher_user = request.user if request and request.user else user\\n for voucher in basket.vouchers.select_for_update():\\n available_to_user, msg = voucher.is_available_to_user(user=voucher_user)\\n if not voucher.is_active() or not available_to_user:\\n raise ValueError(msg)\\n\\n # Record any discounts associated with this order\\n for application in basket.offer_applications:\\n # Trigger any deferred benefits from offers and capture the resulting message\\n application[\\\"message\\\"] = application[\\\"offer\\\"].apply_deferred_benefit(\\n basket, order, application\\n )\\n # Record offer application results\\n if application[\\\"result\\\"].affects_shipping:\\n # Skip zero shipping discounts\\n shipping_discount = shipping_method.discount(basket)\\n if shipping_discount <= Decimal(\\\"0.00\\\"):\\n continue\\n # If a shipping offer, we need to grab the actual discount off\\n # the shipping method instance, which should be wrapped in an\\n # OfferDiscount instance.\\n application[\\\"discount\\\"] = shipping_discount\\n self.create_discount_model(order, application)\\n self.record_discount(application)\\n\\n # Record voucher usage for this order\\n for voucher in basket.vouchers.all():\\n self.record_voucher_usage(order, voucher, user)\\n\\n # Send signal for analytics to pick up\\n order_placed.send(sender=self, order=order, user=user)\\n\\n # Done! Return the order.Order model\\n return order\",\n \"def order(self, order):\\n\\n self._order = order\",\n \"def order(self, order):\\n\\n self._order = order\",\n \"def order(self, order):\\n\\n self._order = order\",\n \"def add_order(self, order):\\n if order.is_bid:\\n if order.price in self.buy_levels:\\n limit = self.buy_levels[order.price]\\n if limit.size == 0:\\n self.buy_tree.size += 1\\n limit.add(order)\\n self.buy_map[order.uid] = order\\n order.parent_limit = limit\\n else:\\n limit = Limit(order.price)\\n limit.add(order)\\n self.buy_map[order.uid] = order\\n self.buy_tree.insert(limit)\\n self.buy_tree.size += 1\\n self.buy_levels[order.price] = limit\\n order.parent_limit = self.buy_levels[order.price]\\n if self.highest_buy is None or order.price > self.highest_buy:\\n self.highest_buy = order.price\\n else:\\n if order.price in self.sell_levels:\\n limit = self.sell_levels[order.price]\\n if limit.size == 0:\\n self.sell_tree.size += 1\\n limit.add(order)\\n self.sell_map[order.uid] = order\\n order.parent_limit = self.sell_levels[order.price]\\n else:\\n limit = Limit(order.price)\\n limit.add(order)\\n self.sell_map[order.uid] = order\\n self.sell_tree.insert(limit)\\n self.sell_tree.size += 1\\n self.sell_levels[order.price] = limit\\n order.parent_limit = self.sell_levels[order.price]\\n if self.lowest_sell is None or order.price < self.lowest_sell:\\n self.lowest_sell = order.price\\n self.update_book()\",\n \"def submit_order(request, orderid):\\n if request.user.is_staff:\\n order = WorkingOrder.objects.get(pk=orderid)\\n else:\\n order = request.user.workingorder_set.get(id=orderid) \\n\\n if order.status != BaseOrder.Const.DEALER_EDIT:\\n return HttpResponseServerError()\\n \\n # always submit orders in the context of proper account\\n account = order.owner.get_profile().account\\n \\n if request.method == 'GET': \\n form = SubmitForm(instance=order)\\n else:\\n form = SubmitForm(request.POST, instance=order)\\n if form.is_valid():\\n order = form.save(commit=False)\\n cost = order.cost or decimal.Decimal() \\n if cost > account.credit_balance:\\n ## users account doesn't have enough juice.. send then to the ecom engine \\n ## to pay, then get them back here ...\\n order = form.save()\\n products = [form.cleaned_data['design_product']]\\n option = form.cleaned_data.get('processing_option', None)\\n if option:\\n products.append(option) \\n new_cart(request, products)\\n request.method = 'GET' \\n return paypal_checkout(request, success_url=reverse('submit-order', args=[orderid]))\\n else: \\n register_design_order(order.owner, order.owner.get_profile().account, order, cost)\\n order = form.save(commit=False)\\n order.status = BaseOrder.Const.SUBMITTED\\n order.submitted = datetime.now()\\n order.save()\\n # return HttpResponseRedirect('completed_order_summary', args=[orderid]) # TODO\\n return HttpResponseRedirect(reverse('submit-order-completed', args=[order.id])) \\n return dict(order=order, form=form)\",\n \"def on_order(self, order, lvl, bp):\\n\\t\\tS = order.symbol\\n\\n\\t\\tneed_bp = order.quantity * self.ticks[S].close\\n\\t\\tif need_bp <= bp: # have enough buying power to place order\\n\\t\\t\\tused_bp = need_bp\\n\\n\\t\\t\\tif lvl == 'hard_stop':\\n\\t\\t\\t\\tself.on_hard_stop(S)\\n\\t\\t\\telif lvl == 'rebalance':\\n\\t\\t\\t\\tself.on_rebalance(S)\\n\\n\\t\\t\\tself.pos[order.symbol].confirm_order(order)\\n\\t\\t\\tlogger.info(\\n\\t\\t\\t\\t'Publish Order={} for Strategy={}'\\n\\t\\t\\t\\t.format(order, self.id)\\n\\t\\t\\t)\\n\\t\\t\\tself.basic_publish('order', sender=self.id, order=order)\\n\\t\\telse:\\n\\t\\t\\tused_bp = 0\\n\\t\\treturn used_bp\",\n \"def show_completed(self, user):\\n\\n orders = CartProduct.show_completed(user)\\n CartProductsView.show_completed(orders)\",\n \"def customer_session_started_handler(self, data: dict, **kwargs) -> None:\\n restaurant = self.env.manager.restaurant.get(data['restaurant_id'])\\n if restaurant.available():\\n self.env.publish('order_placed', data={\\n 'customer_id': self.id,\\n 'restaurant_id': data['restaurant_id']})\",\n \"def slot_user_order(self, dummy_sender, data):\\r\\n (price, volume, typ, oid, status) = data\\r\\n found = False\\r\\n removed = False # was the order removed?\\r\\n opened = False # did the order change from 'post-pending' to 'open'\\\"?\\r\\n voldiff = 0 # did the order volume change (full or partial fill)\\r\\n if \\\"executing\\\" in status:\\r\\n # don't need this status at all\\r\\n return\\r\\n if \\\"post-pending\\\" in status:\\r\\n # don't need this status at all\\r\\n return\\r\\n if \\\"removed\\\" in status:\\r\\n for i in range(len(self.owns)):\\r\\n if self.owns[i].oid == oid:\\r\\n order = self.owns[i]\\r\\n\\r\\n # work around MtGox strangeness:\\r\\n # for some reason it will send a \\\"completed_passive\\\"\\r\\n # immediately followed by a \\\"completed_active\\\" when a\\r\\n # market order is filled and removed. Since \\\"completed_passive\\\"\\r\\n # is meant for limit orders only we will just completely\\r\\n # IGNORE all \\\"completed_passive\\\" if it affects a market order,\\r\\n # there WILL follow a \\\"completed_active\\\" immediately after.\\r\\n if order.price == 0:\\r\\n if \\\"passive\\\" in status:\\r\\n # ignore it, the correct one with\\r\\n # \\\"active\\\" will follow soon\\r\\n return\\r\\n\\r\\n self.debug(\\r\\n \\\"### removing order %s \\\" % oid,\\r\\n \\\"price:\\\", self.gox.quote2str(order.price),\\r\\n \\\"type:\\\", order.typ)\\r\\n\\r\\n # remove it from owns...\\r\\n self.owns.pop(i)\\r\\n\\r\\n # ...and update own volume cache in the bids or asks\\r\\n self._update_level_own_volume(\\r\\n order.typ,\\r\\n order.price,\\r\\n self.get_own_volume_at(order.price, order.typ)\\r\\n )\\r\\n removed = True\\r\\n break\\r\\n else:\\r\\n for order in self.owns:\\r\\n if order.oid == oid:\\r\\n found = True\\r\\n self.debug(\\r\\n \\\"### updating order %s \\\" % oid,\\r\\n \\\"volume:\\\", self.gox.base2str(volume),\\r\\n \\\"status:\\\", status)\\r\\n voldiff = volume - order.volume\\r\\n opened = (order.status != \\\"open\\\" and status == \\\"open\\\")\\r\\n order.volume = volume\\r\\n order.status = status\\r\\n break\\r\\n\\r\\n if not found:\\r\\n # This can happen if we added the order with a different\\r\\n # application or the gox server sent the user_order message\\r\\n # before the reply to \\\"order/add\\\" (this can happen because\\r\\n # actually there is no guarantee which one arrives first).\\r\\n # We will treat this like a reply to \\\"order/add\\\"\\r\\n self.add_own(Order(price, volume, typ, oid, status))\\r\\n\\r\\n # The add_own() method has handled everything that was needed\\r\\n # for new orders and also emitted all signals already, we\\r\\n # can immediately return here because the job is done.\\r\\n return\\r\\n\\r\\n # update level own volume cache\\r\\n self._update_level_own_volume(\\r\\n typ, price, self.get_own_volume_at(price, typ))\\r\\n\\r\\n # We try to help the strategy with tracking the orders as good\\r\\n # as we can by sending different signals for different events.\\r\\n if removed:\\r\\n reason = self.gox.msg[\\\"user_order\\\"][\\\"reason\\\"]\\r\\n self.signal_own_removed(self, (order, reason))\\r\\n if opened:\\r\\n self.signal_own_opened(self, (order))\\r\\n if voldiff:\\r\\n self.signal_own_volume(self, (order, voldiff))\\r\\n self.signal_changed(self, None)\\r\\n self.signal_owns_changed(self, None)\",\n \"def click_placeorder(self):\\n time.sleep(0.4)\\n placeorder = self.driver.find_element_by_css_selector(self.CSS_PLACEORDER)\\n action = TouchActions(self.driver)\\n action.tap(placeorder).perform()\\n WebDriverWait(self.driver, 20).until(EC.visibility_of_element_located((By.CSS_SELECTOR, '#order-no')))\\n order_id=self.driver.find_element_by_css_selector('#order-no').text\\n return order_id\",\n \"def test_get_one_order(self):\\n self.orders_list.place_order(self.order)\\n self.orders_list.place_order(self.order)\\n self.assertEqual(self.orders_list.get_order(1), self.order)\",\n \"def m_ts_OrderAdded(self, sender, e):\\r\\n print(\\\"Order was added with price of {0}.\\\".format(e.Order.LimitPrice))\",\n \"def select_user_and_add_transaction(self):\\n def add_transaction(to_user):\\n print(\\\"Amount of transaction:\\\")\\n amount = input()\\n new_transaction = transaction.Transaction(amount)\\n to_user.add_transaction(new_transaction)\\n\\n try:\\n selected_user = self.prompt_user_selection()\\n add_transaction(selected_user)\\n except ValueError:\\n print(\\\"No changes made.\\\")\",\n \"def update_orders(comp, order, user_correct, payment_id):\\n users_orders = []\\n for item in order.items.all():\\n users_orders.append(item.id)\\n item.is_paid = True\\n item.save()\\n order.related_competition = comp\\n order.payment_id = payment_id\\n order.order_date = timezone.now()\\n order.answer_correct = user_correct\\n order.ordered = True\\n order.save()\\n return order\",\n \"def set_order(self, order):\\n self.order = order\",\n \"def set_order(self, order):\\n self.order = order\",\n \"def place(self, order):\\n assert isinstance(order, Order)\\n order_id = self.__order_count\\n self.__pending_orders[order_id] = order\\n self.__order_count = order_id + 1\\n return order_id\",\n \"def order(self, stock, amount):\\n self.orders[stock] = amount\",\n \"def get_one_order():\",\n \"def test_process_order(self):\\n checkout_form = self.get_checkout_form()\\n checkout_form.submit()\\n\\n sleep(0.5)\\n result = self.browser.find_element_by_id('result')\\n self.assertIn(\\n \\\"Your order was placed.\\\",\\n result.text\\n )\\n self.assertTrue(len(self.client.session['cart']) == 0)\\n OrderInfo.objects.get()\",\n \"def order_ready(request):\\n\\tcs , status = CookStatus.objects.get_or_create(cook_name=request.user)\\n\\tif cs.current_order is not None:\\n\\t\\tcs.current_order.status = 'ready-to-serve'\\n\\t\\tcs.current_order.save()\\n\\t\\tcs.current_order = None\\n\\t\\tcs.save()\\n\\n\\treturn HttpResponseRedirect(\\\"/staff/cook_order_list/\\\")\",\n \"def prepare_order(request):\\n distributer_id = request.GET.get('distributer')\\n billing_address_id = request.GET.get('bill_addr')\\n pickup_method = 2 # this is left in place if we ever decide to have door-to-door deliveries - otherwise it should be deleted\\n cart = Cart.objects.get_or_create(user=request.user, processed_to_order=False)[0]\\n user_bill_addr = UserBillingAddress.objects.get_or_create(pk=billing_address_id, user=request.user)[0]\\n distributer = Distributer.objects.get(pk=distributer_id)\\n\\n # Create order\\n order = Order()\\n order.user = request.user\\n order.distributer = distributer\\n order.subtotal = cart.subtotal\\n order.tax_total = cart.tax_total\\n order.total = cart.total\\n order.discount_for_returned_package = 0 #TODO implement returned packaging\\n order.to_pay = 0 #TODO implement returned packaging\\n order.delivery_method = pickup_method\\n order.save()\\n\\n # create all order items\\n for item in cart.cartitem_set.all():\\n order_item = OrderItem()\\n order_item.order = order\\n order_item.item_name = str(item.item)\\n order_item.item_price = item.item.price\\n order_item.item_quantity = item.quantity\\n order_item.item_decimal_quantity = 0 #TODO implement decimal quantity\\n order_item.item_unit_of_measure = \\\"kom\\\" #TODO implement decimal quantity\\n order_item.item_tax_bracket = item.item.tax_bracket\\n order_item.item_subtotal = item.line_subtotal\\n order_item.item_tax_total = item.line_tax_total\\n order_item.item_total = item.line_tax_total\\n if item.item.package_type == None:\\n order_item.item_package = None\\n order_item.item_package_price = 0\\n else:\\n order_item.item_package = item.item.package_type.type\\n order_item.item_package_price = item.item.package_type.price\\n order_item.item_package_subtotal = item.line_package_subtotal\\n order_item.item_package_tax_total = item.line_package_tax_total\\n order_item.item_package_total = item.line_package_total\\n order_item.save()\\n\\n billing_address = OrderBillingAddress()\\n billing_address.order = order\\n billing_address.name = user_bill_addr.name\\n billing_address.surname = user_bill_addr.surname\\n billing_address.street_name = user_bill_addr.street_name\\n billing_address.street_nr = user_bill_addr.street_nr\\n billing_address.zip_code = user_bill_addr.zip_code\\n billing_address.city = user_bill_addr.city\\n billing_address.country = user_bill_addr.country\\n billing_address.vat_nr = user_bill_addr.vat_nr\\n billing_address.vat_taxpayer = user_bill_addr.vat_taxpayer\\n billing_address.save()\\n\\n return redirect(reverse('orders_overview', kwargs={'pk': str(order.pk)}))\",\n \"def __orderAddItem(self, order, item):\\n cursor = self.__db.cursor()\\n iID = self.__id.getID(\\\"orderitem\\\")\\n cursor.execute(\\\"INSERT INTO `orderItems` (`insertionID`, `orderID`, `itemID`) VALUES (%s, %s, %s);\\\",\\n (iID, order, item))\\n return iID\",\n \"def place_order(self, order_event):\\n self._check_day_data(order_event.order_time)\\n if order_event.order_type == 'MARKET':\\n self._fill_market_order(order_event)\\n elif order_event.order_type == 'LIMIT':\\n if self._check_limit_order(order_event, order_event.order_time):\\n pass\\n self.resting_orders.append(order_event)\",\n \"def set_order_done():\\n data = select_data_source()\\n user = data['user']\\n order_id = data['id']\\n \\n if check_user_permission(user) : return permission_denied_return\\n \\n db = database.getdb()\\n \\n ### Check if is valid.\\n \\n cmd = 'select passed from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_valid = db.execute(cmd).fetchall()[0][0]\\n if order_valid == 0 :\\n return finish_invalid_return\\n \\n ### Check if is done.\\n cmd = 'select done from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_done = db.execute(cmd).fetchall()[0][0]\\n if order_done != 0 :\\n return finish_done_return\\n \\n ### All check done.\\n ### Set it to done.\\n cmd = 'update orders set done=1 where id==\\\"{0}\\\"'.format(order_id)\\n db.execute(cmd)\\n db.commit()\\n print('user sets order {0} to be done.'.format(user))\\n \\n return finish_complete_return\",\n \"def order_success(self, request):\\n order = self.order_from_request(request)\\n\\n if not order:\\n return self.order_new(request)\\n\\n if not order.balance_remaining:\\n self.set_order_on_request(request, order=None)\\n\\n\\n order_data = OrderData.objects.get(order=order)\\n o_data = simplejson.loads(order_data.data)\\n\\n paymentData = {}\\n paymentData['delivery_address2'] = o_data['delivery_address2']\\n paymentData['billing_address2'] = o_data['billing_address2']\\n paymentData['delivery_date'] = o_data['delivery_date']\\n paymentData['delivery_state'] = o_data['delivery_state']\\n paymentData['billing_state'] = o_data['billing_state']\\n paymentData['salutation'] = o_data['salutation']\\n paymentData['contact_number'] = o_data['billing_contact_number']\\n\\n #try:\\n oPayment = OrderPayment.objects.get(order=order)\\n oPayment.payment_method = o_data['order_payment_method']\\n oPayment.data = simplejson.dumps(paymentData)\\n oPayment.save()\\n #except:\\n # pass\\n\\n \\\"\\\"\\\"\\n order update note\\n \\\"\\\"\\\"\\n notes = o_data['order_notes']\\n order.notes = notes\\n order.save()\\n\\n # st_save_helper(request, order)\\n\\n \\\"\\\"\\\"\\n sbid = None\\n\\n if 'customer_styleboard' in request.session:\\n sbid = request.session.get('customer_styleboard').id\\n\\n if 'personalize_id' in request.session:\\n print \\\"There's a personalize_id\\\"\\n \\\"\\\"\\\"\\n\\n current_user = User.objects.get(id=int(request.user.id))\\n\\n if 'ipn_emailed' in o_data and o_data['ipn_emailed']:\\n\\n pass\\n \\n else:\\n\\n emailed = send_email_order(order, current_user, notes, paymentData['contact_number'], self)\\n\\n logr.info('emailed order confirmation to : %s from order success' % current_user.email)\\n\\n\\n order_data.delete() # not needed after saving to order payment\\\\\\n \\n clear_styleboard_session(request)\\n\\n try:\\n del request.session['customer_styleboard']\\n del request.session['personalize_id']\\n except:\\n pass\\n\\n return self.render(request, 'plata/shop_order_success.html',\\n self.get_context(request, {\\n 'order': order,\\n 'progress': 'success',\\n }))\",\n \"def test_update_order_by_user(client):\\n resp = put_json(client, '/v1/orders/1/', {\\n \\\"status\\\": \\\"Complete\\\", },\\n headers={'Authorization': 'Bearer ' + user_two(client)})\\n assert resp.status_code == 401\\n assert b'Not authorized' in resp.data\",\n \"def order(request):\\n if request.method == 'GET':\\n try:\\n order = Order.objects.filter()\\n serializer = OrderSerializer(order, many=True)\\n except Order.DoesNotExist:\\n message = 'An order does not exist in this ID({})!'.format(order)\\n data = {'error': message}\\n return Response(data, status=status.HTTP_403_FORBIDDEN)\\n\\n return Response(serializer.data, status=status.HTTP_200_OK)\\n\\n if request.method == 'POST':\\n user = request.data['user']\\n products = request.data['products']\\n\\n try:\\n user_qry = User.objects.get(username=user)\\n except User.DoesNotExist:\\n message = 'An user does not exist in this name({})!'.format(user)\\n data = {'error': message}\\n return Response(data, status=status.HTTP_403_FORBIDDEN)\\n\\n try:\\n total_amount = 0\\n for prd in products:\\n prd_qry = Product.objects.get(id=prd)\\n total_amount += prd_qry.price\\n except Product.DoesNotExist:\\n message = 'An product does not exist in this ID({})!'.format(prd)\\n data = {'error': message}\\n return Response(data, status=status.HTTP_403_FORBIDDEN)\\n\\n ordr = Order()\\n ordr.user = user_qry\\n ordr.total_amount = total_amount\\n ordr.save()\\n ordr.product.set(products)\\n\\n data = {'Success': 'Success'}\\n return Response(data, status=status.HTTP_201_CREATED)\",\n \"def send_exchange_request(self, user):\\n self.current['user'] = user\\n self.send_request(user, self.KM_REQUEST_KEY)\",\n \"def mark_ready_for_review(self, user: User) -> None:\\n from .exceptions import OperationForbiddenError, OrderEmptyError\\n\\n # If order is not in the \\\"CREATED\\\" state, raise an\\n # OperationForbiddenError\\n if not self.is_created:\\n raise OperationForbiddenError(\\n self.STATE_CHANGE_FORBIDDEN_ERROR_MSG % {\\n 'current_state': Order.OrderState.get_choice_display(\\n self.state\\n ),\\n 'new_state': Order.OrderState.PENDING.choice_display\\n }\\n )\\n\\n # If the order's item list is empty, raise an OrderEmptyError\\n if not self.orderitem_set.exists():\\n raise OrderEmptyError(\\n self,\\n 'An order should contain at least one Order item before it '\\n 'can be marked as \\\"PENDING\\\".'\\n )\\n\\n # Update the order to \\\"PENDING\\\" state\\n self.update(user, state=Order.OrderState.PENDING.choice_value)\",\n \"def _send_to_market(self, order, is_mine):\\n \\n \\n ord_type = order[self.col_idx['ordtype']]\\n if ord_type == \\\"new\\\":\\n self.mkt.send(is_buy=order[self.col_idx['is_buy']],\\n qty=order[self.col_idx['qty']],\\n price=order[self.col_idx['price']],\\n uid=order[self.col_idx['uid']],\\n is_mine=is_mine,\\n timestamp=order[self.col_idx['timestamp']])\\n elif ord_type == \\\"cancel\\\":\\n self.mkt.cancel(uid=order[self.col_idx['uid']])\\n elif ord_type == \\\"modif\\\":\\n self.mkt.modif(uid=order[self.col_idx['uid']], \\n new_qty=order[self.col_idx['qty']])\\n else:\\n raise ValueError(f'Unexpected ordtype: {ord_type}')\",\n \"def sender_order(self, sender_order):\\n\\n self._sender_order = sender_order\",\n \"def add_item(self, order_item):\\n self.order_items.append(order_item)\",\n \"def store_order(self, store_name, store_orders,\\\\\\n order_id, order_details):\\n #store the details of the order in dictionary\\n if order_details[\\\"ready\\\"] == True:\\n print(\\\"order number {} is ready\\\".format(order_id))\\n #save the information of all customers\\n customers_data = self.firebase.get_data(\\\"customers\\\")\\n message_information = {\\n #store the current information\\n 'order_id' : order_id,\\n 'store_name' : store_name,\\n 'store_orders' : store_orders,\\n 'order_details' : order_details,\\n #get the order details\\n 'food' : order_details['food'],\\n 'persons_id' : order_details['id'],\\n }\\n\\n #add the persons details\\n customer = customers_data[message_information['persons_id']]\\n #print(customer)\\n message_information['service'] = customer['service']\\n message_information['mobile'] = customer['mobile']\\n\\n #If there is additional information, append it. Else, just put NA\\n try:\\n message_information['addit_info'] = order_details['addit_info']\\n except:\\n message_information['addit_info'] = 'NA'\\n\\n #Create the message\\n self.create_message(message_information)\\n\\n else:\\n # Update time_waited\\n order_time = order_details[\\\"order_time\\\"]\\n if order_time != \\\"None\\\":\\n if order_details[\\\"ready\\\"]!=\\\"true\\\" or order_details[\\\"ready\\\"]!=\\\"waiting for collection\\\":\\n time_elapsed = int(time.time()) - order_details[\\\"order_time\\\"]\\n self.firebase.update([\\\"orders\\\", STORE_NAME, order_id], {\\\"time_waited\\\": time_elapsed})\",\n \"def send_admin_about_order(order):\\n send_email('Flask ecommerce, New order', sender=current_app.config['ADMINS'][0],\\n recipients=current_app.config['ADMINS'],\\n text_body=render_template('email/new_order.txt', order=order),\\n html_body=render_template('email/new_order.html', order=order))\",\n \"def test_buy_order(self):\\n\\n user = self.set_auth_token_header()\\n\\n # set account buying power\\n account = user.account\\n account.available_bp = 1000\\n account.save()\\n\\n data = {\\n 'stock': 'GOOG',\\n 'quantity': 15,\\n 'price': 1.25,\\n 'order_type': 'BUY'\\n }\\n\\n # Order create API\\n url = reverse('orders-list')\\n response = self.client.post(url, data=data)\\n # order created\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\\n\\n # account balance should've been updated\\n acc = Account.objects.get(user=user)\\n self.assertEqual(acc.available_bp, 981.25)\\n\\n # stock shares should've been updated\\n shares = StockShare.objects.get(account=acc)\\n self.assertEqual(shares.quantity, 15.0)\\n self.assertEqual(shares.total_value, 18.75)\\n self.assertEqual(shares.stock.code, 'GOOG')\",\n \"def place_order(self, instrument, lower, upper, units=1, side_arg='buy'):\\n if (side_arg == 'buy'):\\n return self.oanda.create_order(self.account_id, instrument=instrument,\\n units=units, side=side_arg,\\n stopLoss=lower, takeProfit=upper,\\n type='market')\\n elif (side_arg == 'sell'):\\n return self.oanda.create_order(self.account_id, instrument=instrument,\\n units=units, side=side_arg,\\n stopLoss=upper, takeProfit=lower,\\n type='market')\",\n \"def orders(self, orders):\\n\\n self._orders = orders\",\n \"def orders(self, orders):\\n\\n self._orders = orders\",\n \"def resolve_order(info, id):\\n order = get_node(info, id, only_type=Order)\\n user = info.context.user\\n if (order.user == user or user.get_all_permissions() & {\\n 'order.view_order', 'order.edit_order'}):\\n return order\",\n \"def click_place_order(self):\\n WebDriverWait(self.driver, 20).until(\\n EC.presence_of_element_located((By.CSS_SELECTOR, self.CSS_PLACEORDER)))\\n placeorder = self.driver.find_element_by_css_selector(self.CSS_PLACEORDER)\\n action = TouchActions(self.driver)\\n action.tap(placeorder).perform()\",\n \"def generate_initial_order(self):\\n self.ensure_one()\\n # Add only active lines\\n agreement_lines = self.mapped('agreement_line').filtered('active_chk')\\n order = self.create_order(self.start_date, agreement_lines)\\n self.write({'state': 'first'})\\n order.signal_workflow('order_confirm')\\n # Return view with order created\\n return {\\n 'domain': \\\"[('id', '=', %s)]\\\" % order.id,\\n 'view_type': 'form',\\n 'view_mode': 'form',\\n 'res_model': 'sale.order',\\n 'context': self.env.context,\\n 'res_id': order.id,\\n 'view_id': [self.env.ref('sale.view_order_form').id],\\n 'type': 'ir.actions.act_window',\\n 'nodestroy': True\\n }\",\n \"def buy(self, irc, msg, args, optlist, amount, thing, price, otherthing, notes):\\n self.db.deleteExpired(self.registryValue('orderExpiry'))\\n gpgauth = self._checkGPGAuth(irc, msg.prefix)\\n if gpgauth is None:\\n irc.error(\\\"For identification purposes, you must be identified via GPG \\\"\\n \\\"to use the order book.\\\")\\n return\\n results = self.db.getByNick(gpgauth['nick'])\\n if len(results) >= self.registryValue('maxUserOpenOrders'):\\n irc.error(\\\"You may not have more than %s outstanding open orders.\\\" % \\\\\\n self.registryValue('maxUserOpenOrders'))\\n return\\n extratime = 0\\n if dict(optlist).has_key('long'):\\n extratime = self.registryValue('longOrderDuration')\\n trust = self._getTrust(irc, 'nanotube', gpgauth['nick'])\\n sumtrust = sum([t for t,n in trust])\\n if sumtrust < self.registryValue('minTrustForLongOrders'):\\n irc.error(\\\"You must have a minimum of %s cumulative trust at \\\"\\n \\\"level 1 and level 2 from nanotube to \\\"\\n \\\"to place long orders.\\\" % (self.registryValue('minTrustForLongOrders'),))\\n return\\n orderid = self.db.buy(gpgauth['nick'], msg.host, amount, thing, price, otherthing, notes, extratime)\\n irc.reply(\\\"Order id %s created.\\\" % (orderid,))\\n if not world.testing:\\n irc.queueMsg(ircmsgs.privmsg(\\\"#bitcoin-otc-ticker\\\",\\n \\\"#%s || %s || BUY %s %s @ %s %s || %s\\\" % (orderid,\\n gpgauth['nick'],\\n amount,\\n thing,\\n self._getIndexedValue(price),\\n otherthing,\\n notes,)))\",\n \"def test_place_multiple_orders(self):\\n self.orders_list.place_order(self.order)\\n self.orders_list.place_order(self.order)\\n self.orders_list.place_order(self.order)\\n self.assertEqual(len(self.orders_list.orders_list), 3)\\n self.assertEqual(self.orders_list.orders_list[2].order_id, 2)\",\n \"def PlaceOutgoingOrder(self, state):\\r\\n amountToOrder , policy_action = self.policy.calculate_order( state )\\r\\n\\r\\n self.outgoingOrdersQueue.PushEnvelope(amountToOrder)\\r\\n self.lastOrderQuantity = amountToOrder\\r\\n \\r\\n return policy_action\",\n \"def __order_status(self):\\n log.debug(\\\"Displaying __order_status\\\")\\n # Find the latest orders\\n orders = self.session.query(db.Order) \\\\\\n .filter(db.Order.user == self.user) \\\\\\n .order_by(db.Order.creation_date.desc()) \\\\\\n .limit(20) \\\\\\n .all()\\n # Ensure there is at least one order to display\\n if len(orders) == 0:\\n self.bot.send_message(self.chat.id, self.loc.get(\\\"error_no_orders\\\"))\\n # Display the order status to the user\\n for order in orders:\\n self.bot.send_message(self.chat.id, order.text(w=self, session=self.session, user=True))\\n # TODO: maybe add a page displayer instead of showing the latest 5 orders\",\n \"def email_order(request, order, user_correct):\\n users_entries = get_entries(order)\\n\\n html_email = loader.render_to_string(\\n 'email_templates/order_complete.html',\\n {\\n 'order': order,\\n 'user': request.user.first_name,\\n 'user_correct': user_correct,\\n 'users_entries': users_entries\\n }\\n )\\n message = strip_tags(html_email)\\n send_mail(\\n 'Your Order for Project Parts',\\n message=message,\\n from_email='noreply@projectparts.com',\\n fail_silently=False,\\n connection=None,\\n recipient_list=[str(request.user.email)],\\n html_message=html_email\\n )\",\n \"def on_order(self, order: OrderData):\\n pass\\n\\n # self.write_log(f\\\"on_order: status:{order.status}, orderid: {order.vt_orderid}, offset:{order.offset}, price:{order.price}, volume:{order.volume}, traded: {order.traded}\\\")\\n # self.put_event()\",\n \"def orderInfo(self, orderInfo):\\r\\n\\r\\n self._orderInfo = orderInfo\",\n \"def place_order(self, order: Order) -> None:\\n\\n if order.id in [order.id for order in self.orders]:\\n raise OrderAlreadyCreatedError(order)\\n\\n if not order.symbol.is_enabled:\\n raise SymbolIsNotEnabledError(order.symbol)\\n\\n t = Thread(target=self.__place_order, args=(order,))\\n t.start()\\n\\n self.__sort_orders_by_price()\",\n \"def place_order(cls, order: 'Order') -> 'Order':\\n counter_order_type = OrderType.SELL.value if order.type == OrderType.BUY.value else OrderType.BUY.value\\n counter_orders = None\\n with transaction.atomic():\\n if counter_order_type == OrderType.SELL.value:\\n counter_orders = cls.objects.select_for_update().filter(\\n type=counter_order_type,\\n instrument=order.instrument,\\n price__lte=order.price).order_by('price', 'created_at_dt')\\n elif counter_order_type == OrderType.BUY.value:\\n counter_orders = cls.objects.select_for_update().filter(\\n type=counter_order_type,\\n instrument=order.instrument,\\n price__gte=order.price).order_by('-price', 'created_at_dt')\\n if not counter_orders:\\n # place order into the order book\\n order.save()\\n return order\\n for counter_order in counter_orders:\\n order, counter_order, *balances = cls._trade_orders(\\n order, counter_order)\\n order.save()\\n counter_order.save()\\n for balance in balances:\\n balance.save()\\n if order.status == OrderStatus.COMPLETED:\\n return order\\n return order\",\n \"def postOrder(self,Account=None,side='buy',price=0,amount=0,order=None):\\n\\t\\tif order is not None:\\n\\t\\t\\tif order.Posted is True:\\n\\t\\t\\t\\tprint(\\\"No order posted-order object might have been used\\\")\\n\\t\\t\\t\\treturn -1\\n\\t\\telse:\\n\\t\\t\\tif Account is None:\\n\\t\\t\\t\\tprint(\\\"No order is posted-no account was provided\\\")\\n\\t\\t\\t\\treturn -1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprice=Decimal(price)\\n\\t\\t\\t\\tamount=Decimal(amount)\\n\\t\\t\\t\\torder=Order.Order(Account,side,price,amount)\\n\\t\\tcharge=False\\n\\t\\tif side is 'buy':\\n\\t\\t\\t charge=Account.withdraw(side,price*amount) \\n\\t\\telse:\\n\\t\\t\\tcharge=Account.withdraw(side,amount)\\n\\t\\tif charge is False:\\n\\t\\t\\treturn -1\\n\\t\\telse:\\n\\t\\t\\tif self.register(order) is False:\\n\\t\\t\\t\\treturn -1\\n\\t\\t\\telse:\\n\\t\\t\\t\\torder.Posted=True\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\"+order.Side.upper()+\\\" order \\\"+str(order.ID)+\\\" from account \\\"+str(order.Account.ID)+\\\" is posted\\\\n\\\")\\n\\t\\t\\t\\treturn order.ID\",\n \"def placeOrder(self, dish):\\n # Get a random waiter\\n waiter = choice(self.waiters)\\n # Place the order, returning the result\\n food = waiter.takeOrder(dish, self.chefs)\\n return food\",\n \"def place_sell_order(self):\\n price = request.form[\\\"price\\\"]\\n stocks = request.form[\\\"stocks\\\"]\\n trader_id = request.form[\\\"trader_id\\\"]\\n self.market.place_sell_order(trader_id, price, stocks)\\n return \\\"\\\"\",\n \"def process_order(self, order_event : event.EventOrder) :\\n pass\",\n \"def orderWatch(self, order):\\r\\n\\t\\tself.orders.append(order)\",\n \"def checkout(self, request, order):\\n if not request.user.is_authenticated():\\n if request.method == 'POST' and '_login' in request.POST:\\n loginform = AuthenticationForm(data=request.POST, prefix='login')\\n\\n if loginform.is_valid():\\n user = loginform.get_user()\\n auth.login(request, user)\\n\\n order.user = user\\n order.save()\\n return HttpResponseRedirect('.')\\n else:\\n loginform = AuthenticationForm(prefix='login')\\n else:\\n loginform = None\\n\\n if order.status < order.CHECKOUT:\\n order.update_status(order.CHECKOUT, 'Checkout process started')\\n\\n OrderForm = self.checkout_form(request, order)\\n\\n\\n\\n orderform_kwargs = {\\n 'prefix': 'order',\\n 'instance': order,\\n 'request': request,\\n 'shop': self,\\n }\\n\\n if request.method == 'POST' and '_checkout' in request.POST:\\n orderform = OrderForm(request.POST, **orderform_kwargs)\\n\\n if orderform.is_valid():\\n notes = request.POST.get('order-notes')\\n same_as_billing = request.POST.get('order-shipping_same_as_billing')\\n delivery_address = request.POST.get('order-shipping_address')\\n billing_address = request.POST.get('order-billing_address')\\n delivery_address2 = request.POST.get('order-shipping_address2')\\n billing_address2 = request.POST.get('order-billing_address2')\\n delivery_date = request.POST.get('order-shipping_date')\\n delivery_state = request.POST.get('order-shipping_state')\\n billing_state = request.POST.get('order-billing_state')\\n delivery_city = request.POST.get('order-shipping_city')\\n billing_city = request.POST.get('order-billing_city')\\n delivery_zip_code = request.POST.get('order-shipping_zip_code')\\n billing_zip_code = request.POST.get('order-billing_zip_code')\\n salutation = request.POST.get('order-billing_salutation')\\n billing_country = request.POST.get('order-billing_country')\\n delivery_country = request.POST.get('order-shipping_country')\\n billing_first_name = request.POST.get('order-billing_first_name')\\n billing_last_name = request.POST.get('order-billing_last_name')\\n email = request.POST.get('order-email')\\n billing_contact_number = request.POST.get('order-billing_contact_number')\\n\\n if same_as_billing:\\n billing_address = delivery_address\\n billing_address2 = delivery_address2\\n billing_state = delivery_state\\n billing_city = delivery_city\\n billing_zip_code = delivery_zip_code\\n billing_country = delivery_country\\n\\n \\n # request.session['order-payment_method'] = request.POST.get('order-payment_method','')\\n\\n custom_data = {}\\n custom_data['order_payment_method'] = request.POST.get('order-payment_method','')\\n custom_data['order_notes'] = request.POST.get('order-notes','')\\n custom_data['delivery_address2'] = delivery_address2\\n custom_data['billing_address2'] = billing_address2\\n custom_data['delivery_date'] = delivery_date\\n custom_data['delivery_state'] = delivery_state\\n custom_data['billing_state'] = billing_state\\n custom_data['salutation'] = salutation\\n custom_data['billing_country'] = billing_country\\n custom_data['shipping_country'] = delivery_country\\n custom_data['billing_contact_number'] = billing_contact_number\\n\\n try:\\n order_data = OrderData.objects.get(order=order)\\n except:\\n order_data = OrderData()\\n\\n order_data.order = order\\n order_data.data = simplejson.dumps(custom_data)\\n order_data.save()\\n \\n \\n orderform.save(\\n notes=notes, \\n billing_salutation=salutation,\\n same_as_billing=True if same_as_billing else False,\\n shipping_address=delivery_address,\\n billing_address=billing_address,\\n shipping_address2=delivery_address2,\\n billing_address2=billing_address2,\\n shipping_state=delivery_state,\\n billing_state=billing_state,\\n shipping_city=delivery_city,\\n billing_city=billing_city,\\n shipping_zip_code=delivery_zip_code,\\n billing_zip_code=billing_zip_code,\\n billing_country=billing_country,\\n shipping_country=delivery_country,\\n billing_first_name=billing_first_name,\\n billing_last_name=billing_last_name,\\n email=email\\n )\\n\\n \\\"\\\"\\\"\\n added notes\\n \\\"\\\"\\\"\\n return redirect('plata_shop_discounts')\\n else:\\n\\n orderform = OrderForm(**orderform_kwargs)\\n \\n\\n return self.render_checkout(request, {\\n 'order': order,\\n 'loginform': loginform,\\n 'orderform': orderform,\\n 'progress': 'checkout'\\n })\",\n \"def handle_add(self, controller):\\n \\n try:\\n pizza = controller.customer.pizza ## get a reference to pizza object of the customer\\n \\n except Exception:\\n showinfo(title='Pop-up', message=\\\"No Pizza Created Yet.\\\")\\n return\\n \\n else:\\n # create an order if not exist, and add pizza to order\\n c = controller.customer\\n self.onPress(c) ## update requested data\\n if not c.my_order:\\n c.my_order = Order(c.name, c.address, c.id)\\n \\n c.AddToOrder()\\n controller.show_frame(PageTwo) ## go to my order page\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70376885","0.6521009","0.6342922","0.6326408","0.6183782","0.6176169","0.6154756","0.606741","0.60295796","0.60066897","0.60049874","0.60049874","0.60049874","0.5994649","0.59906405","0.59855217","0.59850776","0.5975906","0.5965155","0.59345704","0.5929625","0.5907162","0.5907162","0.5905161","0.58921456","0.5866677","0.5862132","0.5844496","0.583562","0.5810452","0.58034086","0.5779227","0.57685053","0.5743363","0.5733049","0.5710249","0.5695395","0.5684049","0.56558686","0.5652283","0.5621422","0.5611015","0.5606663","0.5606181","0.5606181","0.5606181","0.55999094","0.5597637","0.5592626","0.5590204","0.55867535","0.55819225","0.55394465","0.5532854","0.5505644","0.54959375","0.5492122","0.5485944","0.5485944","0.54854137","0.54841906","0.5476378","0.54679024","0.5465464","0.54595536","0.5459027","0.5457127","0.5448155","0.54384154","0.5419563","0.540762","0.54004633","0.5398062","0.5396676","0.5393668","0.5375718","0.5373647","0.53701824","0.5357809","0.5356902","0.5352528","0.5352528","0.53445554","0.5343643","0.53397244","0.53361034","0.5334434","0.53317523","0.5321319","0.5321198","0.5319687","0.53164184","0.5312923","0.5291468","0.52885276","0.52854323","0.52773535","0.5269923","0.5269826","0.52668464","0.5265486"],"string":"[\n \"0.70376885\",\n \"0.6521009\",\n \"0.6342922\",\n \"0.6326408\",\n \"0.6183782\",\n \"0.6176169\",\n \"0.6154756\",\n \"0.606741\",\n \"0.60295796\",\n \"0.60066897\",\n \"0.60049874\",\n \"0.60049874\",\n \"0.60049874\",\n \"0.5994649\",\n \"0.59906405\",\n \"0.59855217\",\n \"0.59850776\",\n \"0.5975906\",\n \"0.5965155\",\n \"0.59345704\",\n \"0.5929625\",\n \"0.5907162\",\n \"0.5907162\",\n \"0.5905161\",\n \"0.58921456\",\n \"0.5866677\",\n \"0.5862132\",\n \"0.5844496\",\n \"0.583562\",\n \"0.5810452\",\n \"0.58034086\",\n \"0.5779227\",\n \"0.57685053\",\n \"0.5743363\",\n \"0.5733049\",\n \"0.5710249\",\n \"0.5695395\",\n \"0.5684049\",\n \"0.56558686\",\n \"0.5652283\",\n \"0.5621422\",\n \"0.5611015\",\n \"0.5606663\",\n \"0.5606181\",\n \"0.5606181\",\n \"0.5606181\",\n \"0.55999094\",\n \"0.5597637\",\n \"0.5592626\",\n \"0.5590204\",\n \"0.55867535\",\n \"0.55819225\",\n \"0.55394465\",\n \"0.5532854\",\n \"0.5505644\",\n \"0.54959375\",\n \"0.5492122\",\n \"0.5485944\",\n \"0.5485944\",\n \"0.54854137\",\n \"0.54841906\",\n \"0.5476378\",\n \"0.54679024\",\n \"0.5465464\",\n \"0.54595536\",\n \"0.5459027\",\n \"0.5457127\",\n \"0.5448155\",\n \"0.54384154\",\n \"0.5419563\",\n \"0.540762\",\n \"0.54004633\",\n \"0.5398062\",\n \"0.5396676\",\n \"0.5393668\",\n \"0.5375718\",\n \"0.5373647\",\n \"0.53701824\",\n \"0.5357809\",\n \"0.5356902\",\n \"0.5352528\",\n \"0.5352528\",\n \"0.53445554\",\n \"0.5343643\",\n \"0.53397244\",\n \"0.53361034\",\n \"0.5334434\",\n \"0.53317523\",\n \"0.5321319\",\n \"0.5321198\",\n \"0.5319687\",\n \"0.53164184\",\n \"0.5312923\",\n \"0.5291468\",\n \"0.52885276\",\n \"0.52854323\",\n \"0.52773535\",\n \"0.5269923\",\n \"0.5269826\",\n \"0.52668464\",\n \"0.5265486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":94598,"cells":{"query":{"kind":"string","value":"Given a ManagedProcess protobuf instance, create the corresponding ProcessNode and insert it into the graph."},"document":{"kind":"string","value":"def add_node(self, managed_process_pb):\n node = ProcessNode(managed_process_pb)\n\n if node.name not in self.nodes:\n self.nodes[node.name] = node\n self.logger.info('Created node for [{}]'.format(node.name))\n else:\n self.logger.error(\n 'Detected request to add a managed process using the name [{}] which is already taken'.format(\n node.name))\n raise DuplicateManagedProcessName(\n 'Cannot have more than one managed process with name [{}]'.format(node.name))\n\n return node"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_node_process(name, properties):\n process = NodeProcess(name)\n DB.session.add(process)\n DB.session.commit()\n for p in properties:\n prop = NodeProcessProperty(process.id, p[0], p[1], p[2])\n DB.session.add(prop)\n DB.session.commit()\n return process","def add_process(self, type, name=None, config=None):\n assert name is not None, 'must specify name for now'\n node = Process(type=type, name=name, config=config)\n self.procs[name] = node\n return node","def insert_process_after(self, process, uuid,\n comment='', force_refresh=True):\n parent = self.get_node(uuid)\n children = list(parent.get_children())\n node = ProcessNode.objects.create(process=process, piece=parent.piece,\n comment=comment, parent_id=parent.id)\n for child in children:\n child.parent = node\n child.save()\n\n if force_refresh:\n self.refresh_tree()\n return node","def add_process(self):\n process_id = str(self.processBox.currentText())\n\n arguments = {}\n\n for row in range(0, self.processTableWidget.rowCount()):\n p_id = \"\"\n val = None\n\n if self.processTableWidget.item(row, 0):\n p_id = self.processTableWidget.item(row, 0).text()\n if self.processTableWidget.item(row, 2):\n val = self.processTableWidget.item(row, 2).text()\n if len(val) > 0:\n try:\n val = json.loads(val)\n except json.JSONDecodeError:\n pass\n else:\n val = None\n if p_id != \"\":\n if val:\n arguments[p_id] = val\n\n self.processgraph = self.processgraph.add_process(process_id, arguments)\n # Refresh process graph in GUI\n self.reload_processgraph_view()","def insert_process_before(self, process, uuid,\n comment='', force_refresh=True):\n target = self.get_node(uuid)\n if target == self.root_node:\n raise Exception('Error: Cannot insert before the root node.')\n\n parent = target.parent\n children = list(target.get_siblings(include_self=True))\n node = ProcessNode.objects.create(process=process, piece=parent.piece,\n comment=comment, parent_id=parent.id)\n for child in children:\n child.parent = node\n child.save()\n\n if force_refresh:\n self.refresh_tree()\n return node","def add_process(self, process):\n self.processes[process.namespec()] = process","def process(cls, process_id: str, arguments: dict = None, namespace: Union[str, None] = None, **kwargs):\n arguments = {**(arguments or {}), **kwargs}\n for arg, value in arguments.items():\n if isinstance(value, ProcessBuilderBase):\n arguments[arg] = value.pgnode\n elif isinstance(value,list):\n for index,arrayelement in enumerate(value):\n if(isinstance(arrayelement,ProcessBuilderBase)):\n value[index] = arrayelement.pgnode\n\n for arg in [a for a, v in arguments.items() if v is UNSET]:\n del arguments[arg]\n return cls(PGNode(process_id=process_id, arguments=arguments, namespace=namespace))","def graph_add_process(self, process_id, args) -> 'ImageCollection':\n graph = {\n 'process_id': process_id,\n 'args': args\n }\n\n return RESTProcesses(graph, self.connection)","def _RegisterProcess(self, process):\n if process is None:\n raise ValueError('Missing process.')\n\n if process.pid in self._processes_per_pid:\n raise KeyError(\n 'Already managing process: {0!s} (PID: {1:d})'.format(\n process.name, process.pid))\n\n self._processes_per_pid[process.pid] = process","def addProcessObject(self, process, env, uid=None, gid=None):\n name = process.getName()\n self.processes[name] = (process, env, uid, gid)\n self.delay[name] = self.minRestartDelay\n if self.running:\n self.startProcess(name)","def run_process(self, process, piece='a', number=1, comment='', force_refresh=True):\n branch = self.get_piece(piece)\n node = self._insert_node(process, piece, number, branch, comment)\n if force_refresh: # workaround to force the root node to update\n self.refresh_tree()\n return node","def custom_process_from_process_graph(\n process_spec: Union[dict, Path],\n process_registry: ProcessRegistry = process_registry_100,\n namespace: str = DEFAULT_NAMESPACE\n):\n # TODO: option to hide process graph for (public) listing\n if isinstance(process_spec, Path):\n process_spec = load_json(process_spec)\n process_id = process_spec[\"id\"]\n process_function = _process_function_from_process_graph(process_spec)\n process_registry.add_function(process_function, name=process_id, spec=process_spec, namespace=namespace)","def _create_process_instance(self, process_id, name, module, cls, config, proc_attr):\n # SERVICE INSTANCE.\n process_instance = for_name(module, cls)\n if not isinstance(process_instance, BaseService):\n raise ContainerConfigError(\"Instantiated service not a BaseService %r\" % process_instance)\n\n # Prepare service instance\n process_instance.errcause = \"\"\n process_instance.id = process_id\n process_instance.container = self.container\n process_instance.CFG = config\n process_instance._proc_name = name\n process_instance._proc_start_time = time.time()\n for att, att_val in proc_attr.iteritems():\n setattr(process_instance, att, att_val)\n\n #Unless the process has been started as part of another Org, default to the container Org or the ION Org\n if config.has_key('org_governance_name'):\n process_instance.org_governance_name = config['org_governance_name']\n else:\n process_instance.org_governance_name = CFG.get_safe('container.org_name', CFG.get_safe('system.root_org', 'ION'))\n\n\n # Add stateful process operations\n if hasattr(process_instance, \"_flush_state\"):\n def _flush_state():\n with process_instance._state_lock:\n state_obj = process_instance.container.state_repository.put_state(process_instance.id, process_instance._proc_state,\n state_obj=process_instance._proc_state_obj)\n state_obj.state = None # Make sure memory footprint is low for larger states\n process_instance._proc_state_obj = state_obj\n process_instance._proc_state_changed = False\n\n def _load_state():\n if not hasattr(process_instance, \"_proc_state\"):\n process_instance._proc_state = {}\n try:\n with process_instance._state_lock:\n new_state, state_obj = process_instance.container.state_repository.get_state(process_instance.id)\n process_instance._proc_state.clear()\n process_instance._proc_state.update(new_state)\n process_instance._proc_state_obj = state_obj\n process_instance._proc_state_changed = False\n except NotFound as nf:\n log.debug(\"No persisted state available for process %s\", process_instance.id)\n except Exception as ex:\n log.warn(\"Process %s load state failed: %s\", process_instance.id, str(ex))\n process_instance._flush_state = _flush_state\n process_instance._load_state = _load_state\n process_instance._state_lock = RLock()\n process_instance._proc_state = {}\n process_instance._proc_state_obj = None\n process_instance._proc_state_changed = False\n\n # PROCESS RESTART: Need to check whether this process had persisted state.\n # Note: This could happen anytime during a system run, not just on RESTART boot\n log.debug(\"Loading persisted state for process %s\", process_id)\n process_instance._load_state()\n\n # start service dependencies (RPC clients)\n self._start_process_dependencies(process_instance)\n\n return process_instance","def create(self, context=None):\n values = self.obj_get_changes()\n db_nodegroup = self.dbapi.create_nodegroup(values)\n self._from_db_object(self, db_nodegroup)","def new_process() -> Process:\n return multiprocessing.Process()","def alloc_proc(self, process, delta_t):\n\t\tself._process_list.append(process)","def create_node_type(name, processes):\n node_type = NodeType(name)\n node_type.processes = processes\n DB.session.add(node_type)\n DB.session.commit()\n return node_type","def create_process(\n self,\n model,\n client_plans,\n client_config,\n server_config,\n server_averaging_plan,\n client_protocols=None,\n ):\n process = FLProcess(\n model=model,\n client_plans=client_plans,\n client_config=client_config,\n server_config=server_config,\n client_protocols=client_protocols,\n server_averaging_plan=server_averaging_plan,\n )\n\n self.processes[process.id] = process\n return self.processes[process.id]","def test_addProcess(self):\r\n self.pm.addProcess(\"foo\", [\"arg1\", \"arg2\"],\r\n uid=1, gid=2, env={})\r\n self.assertEqual(self.pm.protocols, {})\r\n self.assertEqual(self.pm.processes,\r\n {\"foo\": ([\"arg1\", \"arg2\"], 1, 2, {})})\r\n self.pm.startService()\r\n self.reactor.advance(0)\r\n self.assertEqual(self.pm.protocols.keys(), [\"foo\"])","def add_task(bpmn, counts, label):\r\n from pm4py.objects.bpmn.bpmn_graph import BPMN\r\n task = BPMN.Task(name=label)\r\n bpmn.add_node(task)\r\n return bpmn, task, counts","def addProcess(self, name, args, uid=None, gid=None, env={}):\n class SimpleProcessObject(object):\n\n def starting(self):\n pass\n\n def stopped(self):\n pass\n\n def getName(self):\n return name\n\n def getCommandLine(self):\n return args\n\n def getFileDescriptors(self):\n return []\n\n self.addProcessObject(SimpleProcessObject(), env, uid, gid)","def _register_process(self, process_instance, name):\n # Add process instance to container's process dict\n if name in self.procs_by_name:\n log.warn(\"Process name already registered in container: %s\" % name)\n self.procs_by_name[name] = process_instance\n self.procs[process_instance.id] = process_instance\n\n # Add Process to resource registry\n # Note: In general the Process resource should be created by the CEI PD, but not all processes are CEI\n # processes. How to deal with this?\n process_instance.errcause = \"registering\"\n\n if process_instance._proc_type != IMMEDIATE_PROCESS_TYPE:\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\n proc_obj = Process(name=process_instance.id, label=name, proctype=process_instance._proc_type)\n proc_id, _ = self.container.resource_registry.create(proc_obj)\n process_instance._proc_res_id = proc_id\n\n # Associate process with container resource\n self.container.resource_registry.create_association(self.cc_id, \"hasProcess\", proc_id)\n else:\n process_instance._proc_res_id = None\n\n # Process type specific registration\n # TODO: Factor out into type specific handler functions\n if process_instance._proc_type == SERVICE_PROCESS_TYPE:\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\n # Registration of SERVICE process: in resource registry\n service_list, _ = self.container.resource_registry.find_resources(restype=\"Service\", name=process_instance.name, id_only=True)\n if service_list:\n process_instance._proc_svc_id = service_list[0]\n if len(service_list) > 1:\n log.warn(\"More than 1 Service resource found with name %s: %s\", process_instance.name, service_list)\n else:\n # We are starting the first process of a service instance\n # TODO: This should be created by the HA Service agent in the future\n svc_obj = Service(name=process_instance.name, exchange_name=process_instance._proc_listen_name, state=ServiceStateEnum.READY)\n process_instance._proc_svc_id, _ = self.container.resource_registry.create(svc_obj)\n\n # Create association to service definition resource\n svcdef_list, _ = self.container.resource_registry.find_resources(restype=\"ServiceDefinition\",\n name=process_instance.name, id_only=True)\n if svcdef_list:\n if len(svcdef_list) > 1:\n log.warn(\"More than 1 ServiceDefinition resource found with name %s: %s\", process_instance.name, svcdef_list)\n self.container.resource_registry.create_association(process_instance._proc_svc_id,\n \"hasServiceDefinition\", svcdef_list[0])\n else:\n log.error(\"Cannot find ServiceDefinition resource for %s\", process_instance.name)\n\n self.container.resource_registry.create_association(process_instance._proc_svc_id, \"hasProcess\", proc_id)\n\n elif process_instance._proc_type == AGENT_PROCESS_TYPE:\n if self.container.has_capability(self.container.CCAP.DIRECTORY):\n # Registration of AGENT process: in Directory\n caps = process_instance.get_capabilities()\n self.container.directory.register(\"/Agents\", process_instance.id,\n **dict(name=process_instance._proc_name,\n container=process_instance.container.id,\n resource_id=process_instance.resource_id,\n agent_id=process_instance.agent_id,\n def_id=process_instance.agent_def_id,\n capabilities=caps))\n\n self._call_proc_state_changed(process_instance, ProcessStateEnum.RUNNING)","def spawnProcess(self, processProtocol, executable, args=(), env={},\r\n path=None, uid=None, gid=None, usePTY=0,\r\n childFDs=None):\r\n\r\n proc = DummyProcess(self, executable, args, env, path,\r\n processProtocol, uid, gid, usePTY, childFDs)\r\n processProtocol.makeConnection(proc)\r\n self.spawnedProcesses.append(proc)\r\n return proc","def addProcess(self, name, proc_config):\n if self.processes.has_key(name):\n raise KeyError(\"remove %s first\" % name)\n p = self.engineProtocol()\n p.service = self\n p.name = name\n proc_config.processProtocol = p\n self.processes[name] = proc_config\n if self.running:\n self.startProcess(name)\n return p.deferred","def add_node(self, ip, port, father_address):\n father = self.find_node(father_address)\n if father is not None:\n node = GraphNode((ip, port))\n node.alive = True\n node.set_parent(father)\n father.add_child(node)\n self.nodes.append(node)\n pass","def graph_add_process(self, process_id, args) -> 'ImageCollection':\n graph = {\n 'process_id': process_id,\n\n }\n\n for key, value in args.items():\n graph[key] = value\n\n #graph = {\n # 'process_id': process_id,\n # 'args': args\n #}\n\n return RestImagery(graph, self.session)","def spawn_process(self, name=None, module=None, cls=None, config=None, process_id=None):\n if process_id and not is_valid_identifier(process_id, ws_sub='_'):\n raise BadRequest(\"Given process_id %s is not a valid identifier\" % process_id)\n\n # Generate a new process id if not provided\n # TODO: Ensure it is system-wide unique\n process_id = process_id or \"%s.%s\" % (self.container.id, self.proc_id_pool.get_id())\n log.debug(\"ProcManager.spawn_process(name=%s, module.cls=%s.%s, config=%s) as pid=%s\", name, module, cls, config, process_id)\n\n process_cfg = deepcopy(CFG)\n if config:\n # Use provided config. Must be dict or DotDict\n if not isinstance(config, DotDict):\n config = DotDict(config)\n if config.get_safe(\"process.config_ref\"):\n # Use a reference\n config_ref = config.get_safe(\"process.config_ref\")\n log.info(\"Enhancing new process spawn config from ref=%s\" % config_ref)\n matches = re.match(r'^([A-Za-z]+):([A-Za-z0-9_\\.]+)/(.*)$', config_ref)\n if matches:\n ref_type, ref_id, ref_ext = matches.groups()\n if ref_type == \"resources\":\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\n try:\n obj = self.container.resource_registry.read(ref_id)\n if obj and hasattr(obj, ref_ext):\n ref_config = getattr(obj, ref_ext)\n if isinstance(ref_config, dict):\n dict_merge(process_cfg, ref_config, inplace=True)\n else:\n raise BadRequest(\"config_ref %s exists but not dict\" % config_ref)\n else:\n raise BadRequest(\"config_ref %s - attribute not found\" % config_ref)\n except NotFound as nf:\n log.warn(\"config_ref %s - object not found\" % config_ref)\n raise\n else:\n log.error(\"Container missing RESOURCE_REGISTRY capability to resolve process config ref %s\" % config_ref)\n elif ref_type == \"objects\":\n if self.container.has_capability(self.container.CCAP.OBJECT_STORE):\n try:\n obj = self.container.object_store.read_doc(ref_id)\n ref_config = obj\n if ref_ext:\n ref_config = get_safe(obj, ref_ext, None)\n if ref_config is None:\n raise BadRequest(\"config_ref %s - attribute not found\" % config_ref)\n\n if isinstance(ref_config, dict):\n dict_merge(process_cfg, ref_config, inplace=True)\n else:\n raise BadRequest(\"config_ref %s exists but not dict\" % config_ref)\n except NotFound as nf:\n log.warn(\"config_ref %s - object not found\" % config_ref)\n raise\n else:\n log.error(\"Container missing OBJECT_STORE capability to resolve process config ref %s\" % config_ref)\n else:\n raise BadRequest(\"Unknown reference type in: %s\" % config_ref)\n\n dict_merge(process_cfg, config, inplace=True)\n if self.container.spawn_args:\n # Override config with spawn args\n dict_merge(process_cfg, self.container.spawn_args, inplace=True)\n\n #log.debug(\"spawn_process() pid=%s process_cfg=%s\", process_id, process_cfg)\n\n # PROCESS TYPE. Determines basic process context (messaging, service interface)\n # One of the constants defined at the top of this file\n\n service_cls = named_any(\"%s.%s\" % (module, cls))\n process_type = get_safe(process_cfg, \"process.type\") or getattr(service_cls, \"process_type\", \"service\")\n\n process_start_mode = get_safe(config, \"process.start_mode\")\n\n process_instance = None\n\n # alert we have a spawning process, but we don't have the instance yet, so give the class instead (more accurate than name)\n self._call_proc_state_changed(\"%s.%s\" % (module, cls), ProcessStateEnum.PENDING)\n\n try:\n # Additional attributes to set with the process instance\n proc_attr = {\"_proc_type\": process_type,\n \"_proc_spawn_cfg\": config\n }\n\n # spawn process by type\n if process_type == SERVICE_PROCESS_TYPE:\n process_instance = self._spawn_service_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n elif process_type == STREAM_PROCESS_TYPE:\n process_instance = self._spawn_stream_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n elif process_type == AGENT_PROCESS_TYPE:\n process_instance = self._spawn_agent_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n elif process_type == STANDALONE_PROCESS_TYPE:\n process_instance = self._spawn_standalone_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n elif process_type == IMMEDIATE_PROCESS_TYPE:\n process_instance = self._spawn_immediate_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n elif process_type == SIMPLE_PROCESS_TYPE:\n process_instance = self._spawn_simple_process(process_id, name, module, cls, process_cfg, proc_attr)\n\n else:\n raise BadRequest(\"Unknown process type: %s\" % process_type)\n\n self._register_process(process_instance, name)\n\n process_instance.errcause = \"OK\"\n log.info(\"ProcManager.spawn_process: %s.%s -> pid=%s OK\", module, cls, process_id)\n\n if process_type == IMMEDIATE_PROCESS_TYPE:\n log.info('Terminating immediate process: %s', process_instance.id)\n self.terminate_process(process_instance.id)\n\n # terminate process also triggers TERMINATING/TERMINATED\n self._call_proc_state_changed(process_instance, ProcessStateEnum.EXITED)\n\n else:\n #Update local policies for the new process\n if self.container.has_capability(self.container.CCAP.GOVERNANCE_CONTROLLER):\n self.container.governance_controller.update_container_policies(process_instance, safe_mode=True)\n\n return process_instance.id\n\n except IonProcessError:\n errcause = process_instance.errcause if process_instance else \"instantiating process\"\n log.exception(\"Error spawning %s %s process (process_id: %s): %s\", name, process_type, process_id, errcause)\n return None\n\n except Exception:\n errcause = process_instance.errcause if process_instance else \"instantiating process\"\n log.exception(\"Error spawning %s %s process (process_id: %s): %s\", name, process_type, process_id, errcause)\n\n # trigger failed notification - catches problems in init/start\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\n\n raise","def add_process(self, model_id, n_cores, n_time, s_time):\n p = Process(n_cores=n_cores, time_needed=n_time, model_id=model_id, start_time=s_time)\n self.process_list.append(p)","def createNode(self, pkg, exe, args, name, nspace):\r\n node = Node(self)\r\n self.callRemote('createNode', pkg, exe, args, name,\r\n nspace).chainDeferred(node)\r\n return node","def _create(self, context, values):\n # initialize load stats from existing instances:\n compute_node = db.compute_node_create(context, values)\n return compute_node","def _spawn_simple_process(self, process_id, name, module, cls, config, proc_attr):\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\n # Add publishers if any...\n publish_streams = get_safe(config, \"process.publish_streams\")\n pub_names = self._set_publisher_endpoints(process_instance, publish_streams)\n\n # cleanup method to delete process queue (@TODO: leaks a bit here - should use XOs)\n def cleanup(*args):\n for name in pub_names:\n p = getattr(process_instance, name)\n p.close()\n\n proc = self.proc_sup.spawn(name=process_instance.id,\n service=process_instance,\n listeners=[],\n proc_name=process_instance._proc_name,\n cleanup_method=cleanup)\n proc.proc._glname = \"ION Proc %s\" % process_instance._proc_name\n self.proc_sup.ensure_ready(proc, \"_spawn_simple_process for %s\" % process_instance.id)\n\n # map gproc to process_instance\n self._spawned_proc_to_process[proc.proc] = process_instance\n\n # set service's reference to process\n process_instance._process = proc\n\n self._process_init(process_instance)\n self._process_start(process_instance)\n\n return process_instance","def remote_createNode(self, pkg, exe, args, name, namespace):\r\n return Node(self, pkg, exe, args, name, namespace)","def describe_process(self, process):\n description = self.client.describeprocess(process.identifier)\n\n new_process = Process.from_owslib(description)\n\n new_process.inputs = process.inputs\n\n new_process.domain = process.domain\n\n new_process.parameters = process.parameters\n\n return new_process","def insert_workflow_module(self, project_id, branch_id, before_module_id, command):\n with self.backend.lock:\n # Get the handle for the specified branch and the branch head\n branch = self.projects.get_branch(project_id=project_id, branch_id=branch_id)\n if branch is None:\n return None\n head = branch.get_head()\n if head is None or len(head.modules) == 0:\n return None\n\n # Get the index of the module at which the new module is inserted\n module_index = None\n modules = head.modules\n for i in range(len(modules)):\n if modules[i].identifier == before_module_id:\n module_index = i\n break\n if module_index is None:\n return None\n\n # Get handle for the inserted module\n context = compute_context(modules[0:module_index])\n # Create handle for the inserted module. The state of the module\n # depends on the state of the backend.\n if head.is_active:\n state = mstate.MODULE_PENDING\n else:\n state = self.backend.next_task_state()\n inserted_module = ModuleHandle(\n command=command,\n state=state,\n external_form=command.to_external_form(\n command=self.packages[command.package_id].get(command.command_id),\n datasets=[ context[name] for name in context if context[name].is_dataset ]\n ),\n provenance=ModuleProvenance(unexecuted=True)\n )\n # Create list of pending modules for the new workflow.\n pending_modules = [inserted_module]\n for m in modules[module_index:]:\n pending_modules.append(\n ModuleHandle(\n command=m.command,\n external_form=m.external_form,\n outputs=m.outputs,\n provenance=m.provenance\n )\n )\n workflow = branch.append_workflow(\n modules=modules[:module_index],\n action=wf.ACTION_INSERT,\n command=inserted_module.command,\n pending_modules=pending_modules\n )\n if not head.is_active:\n self.execute_module(\n project_id=project_id,\n branch_id=branch_id,\n module=workflow.modules[module_index],\n artifacts=context,\n )\n return workflow.modules[module_index:]","def _spawn_standalone_process(self, process_id, name, module, cls, config, proc_attr):\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\n rsvc = self._create_listening_endpoint(node=self.container.node,\n from_name=process_instance.id,\n process=process_instance)\n\n # Add publishers if any...\n publish_streams = get_safe(config, \"process.publish_streams\")\n pub_names = self._set_publisher_endpoints(process_instance, publish_streams)\n\n # cleanup method to delete process queue (@TODO: leaks a bit here - should use XOs)\n def cleanup(*args):\n self._cleanup_method(process_instance.id, rsvc)\n for name in pub_names:\n p = getattr(process_instance, name)\n p.close()\n\n proc = self.proc_sup.spawn(name=process_instance.id,\n service=process_instance,\n listeners=[rsvc],\n proc_name=process_instance._proc_name,\n cleanup_method=cleanup)\n proc.proc._glname = \"ION Proc %s\" % process_instance._proc_name\n self.proc_sup.ensure_ready(proc, \"_spawn_standalone_process for %s\" % process_instance.id)\n\n # map gproc to process_instance\n self._spawned_proc_to_process[proc.proc] = process_instance\n\n # set service's reference to process\n process_instance._process = proc\n\n self._process_init(process_instance)\n self._process_start(process_instance)\n\n try:\n proc.start_listeners()\n except IonProcessError:\n self._process_quit(process_instance)\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\n raise\n\n return process_instance","def create_process(args):\n process = args.process\n\n # create list of valid machines\n valid_machines = []\n valid_types = [cmpy.machines.MealyHMM, \n cmpy.machines.RecurrentEpsilonMachine]\n\n for em in dir(cmpy.machines):\n if em[0].isupper():\n try:\n m_str = 'cmpy.machines.' + em +'()' \n eval(m_str)\n mtype = type(eval(m_str))\n if mtype in valid_types:\n valid_machines.append(em)\n except:\n pass\n\n # remove MealyHMM, RecurrentEpsilonMachine\n valid_machines.remove('MealyHMM')\n valid_machines.remove('RecurrentEpsilonMachine')\n\n # if in valid_machine, try to create instance\n if process in valid_machines:\n eM = eval('cmpy.machines.' + process + '()')\n else: \n error_msg = (\"\\n\\nProcess {} not valid. Try:\\n\\n{}\\n\".format(process,\n valid_machines))\n raise ProcessException(error_msg)\n\n return eM","def add_node (self, node):\n self.network.add_node(node.id)\n self.network.node[node.id] = node","def create_node(self, address, service):\n node = create_node(address, service)\n node.id = address + \"_\" + service\n return node","def register_proc(self, pid: int):\n self.processes.add(pid)","def _spawn_agent_process(self, process_id, name, module, cls, config, proc_attr):\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\n if not isinstance(process_instance, ResourceAgent) and not isinstance(process_instance, SimpleResourceAgent):\n raise ContainerConfigError(\"Agent process must extend ResourceAgent\")\n listeners = []\n\n # Set the resource ID if we get it through the config\n resource_id = get_safe(process_instance.CFG, \"agent.resource_id\")\n if resource_id:\n process_instance.resource_id = resource_id\n\n alistener = self._create_listening_endpoint(node=self.container.node,\n from_name=resource_id,\n process=process_instance)\n\n listeners.append(alistener)\n\n rsvc = self._create_listening_endpoint(node=self.container.node,\n from_name=process_instance.id,\n process=process_instance)\n\n listeners.append(rsvc)\n\n # cleanup method to delete process/agent queue (@TODO: leaks a bit here - should use XOs)\n def agent_cleanup(x):\n self._cleanup_method(process_instance.id, rsvc)\n if resource_id:\n self._cleanup_method(resource_id, alistener)\n\n proc = self.proc_sup.spawn(name=process_instance.id,\n service=process_instance,\n listeners=listeners,\n proc_name=process_instance._proc_name,\n cleanup_method=agent_cleanup)\n proc.proc._glname = \"ION Proc %s\" % process_instance._proc_name\n self.proc_sup.ensure_ready(proc, \"_spawn_agent_process for %s\" % process_instance.id)\n\n # map gproc to process_instance\n self._spawned_proc_to_process[proc.proc] = process_instance\n\n # set service's reference to process\n process_instance._process = proc\n\n # Now call the on_init of the agent.\n self._process_init(process_instance)\n\n if not process_instance.resource_id:\n log.warn(\"New agent pid=%s has no resource_id set\" % process_id)\n\n self._process_start(process_instance)\n\n try:\n proc.start_listeners()\n except IonProcessError:\n self._process_quit(process_instance)\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\n raise\n\n if not process_instance.resource_id:\n log.warn(\"Agent process id=%s does not define resource_id!!\" % process_instance.id)\n\n return process_instance","def register_process(self, data_process_id=''):\n\n # retrieve the data_process object\n data_process_obj = self.clients.resource_registry.read(data_process_id)\n if data_process_obj is None:\n raise NotFound(\"Data Process %s does not exist\" % data_process_id)\n\n #create data producer resource and associate to this data_process_id\n data_producer_obj = IonObject(RT.DataProducer,name=data_process_obj.name, description=\"primary producer resource for this process\", is_primary=True)\n data_producer_id, rev = self.clients.resource_registry.create(data_producer_obj)\n\n # Create association\n self.clients.resource_registry.create_association(data_process_id, PRED.hasDataProducer, data_producer_id)\n\n # TODO: Walk up the assocations to find parent producers:\n # proc->subscription->stream->prod\n\n return data_producer_id","def create_node_handle(node_name, node_type_name, node_meta_type):\n user = get_user()\n node_type = get_node_type(node_type_name)\n node_handle = NodeHandle.objects.create(node_name=node_name, node_type=node_type, node_meta_type=node_meta_type,\n creator=user, modifier=user)\n node_handle.save()\n activitylog.create_node(user, node_handle)\n return node_handle","def _add_node(self, node_name, node_type):\n q = 'MATCH (r:' + node_type + ') WHERE r.name=\"' \\\n + node_name + '\" RETURN r'\n results = self.db.query(q, returns=(client.Node, str, client.Node))\n res = self.db.labels.create(node_type)\n\n if (len(results) == 0):\n r = self.db.nodes.create(name=node_name)\n res.add(r)\n else:\n r = results[0][0]\n return r","def create_node(self, **kwargs):\n if not self.nodes:\n self.get_nodes()\n\n _node = Node(project_id=self.project_id, connector=self.connector, **kwargs)\n\n _node.create()\n self.nodes.append(_node)\n print(\n f\"Created: {_node.name} -- Type: {_node.node_type} -- \"\n f\"Console: {_node.console}\"\n )","def _StartMonitoringProcess(self, process):\n if process is None:\n raise ValueError('Missing process.')\n\n pid = process.pid\n\n if pid in self._process_information_per_pid:\n raise KeyError(\n 'Already monitoring process (PID: {0:d}).'.format(pid))\n\n if pid in self._rpc_clients_per_pid:\n raise KeyError(\n 'RPC client (PID: {0:d}) already exists'.format(pid))\n\n rpc_client = plaso_xmlrpc.XMLProcessStatusRPCClient()\n\n # Make sure that a worker process has started its RPC server.\n # The RPC port will be 0 if no server is available.\n rpc_port = process.rpc_port.value\n time_waited_for_process = 0.0\n while not rpc_port:\n time.sleep(0.1)\n rpc_port = process.rpc_port.value\n time_waited_for_process += 0.1\n\n if time_waited_for_process >= self._RPC_SERVER_TIMEOUT:\n raise IOError(\n 'RPC client unable to determine server (PID: {0:d}) port.'.format(\n pid))\n\n hostname = 'localhost'\n\n if not rpc_client.Open(hostname, rpc_port):\n raise IOError((\n 'RPC client unable to connect to server (PID: {0:d}) '\n 'http://{1:s}:{2:d}').format(pid, hostname, rpc_port))\n\n self._rpc_clients_per_pid[pid] = rpc_client\n self._process_information_per_pid[pid] = process_info.ProcessInfo(pid)","def create_node(self, **kwargs):\n size = kwargs['size'].ram\n params = {\n 'cmd' : 'dreamhost_ps-add_ps',\n 'movedata' : kwargs.get('movedata', 'no'),\n 'type' : kwargs['image'].name,\n 'size' : size\n }\n data = self.connection.request('/', params).object\n return Node(\n id = data['added_web'],\n name = data['added_web'],\n state = NodeState.PENDING,\n public_ip = [],\n private_ip = [],\n driver = self.connection.driver,\n extra = {\n 'type' : kwargs['image'].name\n }\n )","def _instantiate_processes(self, input=None, context=None):\n# FIX: ALLOW Projections (??ProjectionTiming TUPLES) TO BE INTERPOSED BETWEEN MECHANISMS IN PATHWAY\n# FIX: AUGMENT LinearMatrix TO USE FULL_CONNECTIVITY_MATRIX IF len(sender) != len(receiver)\n\n # # MODIFIED 2/8/17 OLD: [SEE BELOW]\n # self.variable = []\n # MODIFIED 2/8/17 END\n self.mechanismsDict = {}\n self._all_mech_tuples = []\n self._allMechanisms = MechanismList(self, self._all_mech_tuples)\n\n # Get list of processes specified in arg to init, possibly appended by EVCMechanism (with prediction processes)\n processes_spec = self.processes\n\n # Assign default Process if PROCESS is empty, or invalid\n if not processes_spec:\n from PsyNeuLink.Components.Process import Process_Base\n processes_spec.append(ProcessTuple(Process_Base(), None))\n\n # If input to system is specified, number of items must equal number of processes with origin mechanisms\n if input is not None and len(input) != len(self.originMechanisms):\n raise SystemError(\"Number of items in input ({}) must equal number of processes ({}) in {} \".\n format(len(input), len(self.originMechanisms),self.name))\n\n #region VALIDATE EACH ENTRY, STANDARDIZE FORMAT AND INSTANTIATE PROCESS\n\n # Convert all entries to (process, input) tuples, with None as filler for absent input\n input_index = input_index_curr = 0\n for i in range(len(processes_spec)):\n\n # MODIFIED 2/8/17 NEW:\n # Get list of origin mechanisms for processes that have already been converted\n # (for use below in assigning input)\n orig_mechs_already_processed = list(p[0].originMechanisms[0] for\n p in processes_spec if isinstance(p,ProcessTuple))\n # MODIFIED 2/8/17 END\n\n # Entry is not a tuple\n # presumably it is a process spec, so enter it as first item of ProcessTuple\n if not isinstance(processes_spec[i], tuple):\n processes_spec[i] = ProcessTuple(processes_spec[i], None)\n\n # Entry is a tuple but not a ProcessTuple, so convert it\n if isinstance(processes_spec[i], tuple) and not isinstance(processes_spec[i], ProcessTuple):\n processes_spec[i] = ProcessTuple(processes_spec[i][0], processes_spec[i][1])\n\n # Input was NOT provided on command line, so get it from the process\n if input is None:\n process = processes_spec[i].process\n process_input = []\n for process_input_state in process.processInputStates:\n process_input.extend(process_input_state.value)\n processes_spec[i] = ProcessTuple(process, process_input)\n # Input was provided on command line, so assign that to input item of tuple\n else:\n # Assign None as input to processes implemented by controller (controller provides their input)\n # (e.g., prediction processes implemented by EVCMechanism)\n if processes_spec[i].process._isControllerProcess:\n processes_spec[i] = ProcessTuple(processes_spec[i].process, None)\n else:\n # MODIFIED 2/8/17 NEW:\n # Replace input item in tuple with one from command line\n # Note: check if origin mechanism for current process is same as any previous one;\n # if it is, use that one (and don't increment index for input\n # otherwise, assign input and increment input_index\n try:\n input_index_curr = orig_mechs_already_processed.index(processes_spec[i][0].originMechanisms[0])\n except ValueError:\n input_index += 1\n processes_spec[i] = ProcessTuple(processes_spec[i].process, input[input_index_curr])\n input_index_curr = input_index\n # MODIFIED 2/8/17 END\n\n # Validate input\n if (processes_spec[i].input is not None and\n not isinstance(processes_spec[i].input,(numbers.Number, list, np.ndarray))):\n raise SystemError(\"Second item of entry {0} ({1}) must be an input value\".\n format(i, processes_spec[i].input))\n\n process = processes_spec[i].process\n process_input = processes_spec[i].input\n\n # # MODIFIED 2/8/17 OLD: [MOVED ASSIGNMENT OF self.variable TO _instantiate_graph()\n # # SINCE THAT IS WHERE SYSTEM'S ORIGIN MECHANISMS ARE IDENTIFIED]\n # self.variable.append(process_input)\n # # MODIFIED 2/8/17 END\n\n # IMPLEMENT: THIS IS WHERE LEARNING SPECIFIED FOR A SYSTEM SHOULD BE IMPLEMENTED FOR EACH PROCESS IN THE\n # SYSTEM; NOTE: IF THE PROCESS IS ALREADY INSTANTIATED WITHOUT LEARNING\n # (FIRST CONDITIONAL BELOW), MAY NEED TO BE RE-INSTANTIATED WITH LEARNING\n # (QUESTION: WHERE TO GET SPECS FOR PROCESS FOR RE-INSTANTIATION??)\n\n # If process item is a Process object, assign process_input as default\n if isinstance(process, Process):\n if process_input is not None:\n process._assign_defaults(variable=process_input, context=context)\n # If learning_rate is specified for system but not for process, then apply to process\n # # MODIFIED 3/21/17 OLD:\n # if self.learning_rate and not process.learning_rate:\n # # FIX: assign_params WANTS TO CREATE A ParamaterState ON process FOR learning_rate\n # process.assign_params(request_set={LEARNING_RATE:self.learning_rate})\n # # MODIFIED 3/21/17 NEW:[learning_rate SHOULD BE NOT BE RE-ASSIGNED FOR PROCESS, BUT RATHER ON EXECUTE]\n # if self.learning_rate is not None and process.learning_rate is None:\n # process.learning_rate = self.learning_rate\n # # MODIFIED 3/21/17 END\n\n # Otherwise, instantiate Process\n else:\n if inspect.isclass(process) and issubclass(process, Process):\n # FIX: MAKE SURE THIS IS CORRECT\n # Provide self as context, so that Process knows it is part of a System (and which one)\n # Note: this is used by Process._instantiate_pathway() when instantiating first Mechanism\n # in Pathway, to override instantiation of projections from Process.input_state\n process = Process(default_input_value=process_input,\n learning_rate=self.learning_rate,\n context=self)\n elif isinstance(process, dict):\n # IMPLEMENT: HANDLE Process specification dict here;\n # include process_input as ??param, and context=self\n raise SystemError(\"Attempt to instantiate process {0} in kwProcesses of {1} \"\n \"using a Process specification dict: not currently supported\".\n format(process.name, self.name))\n else:\n raise SystemError(\"Entry {0} of kwProcesses ({1}) must be a Process object, class, or a \"\n \"specification dict for a Process\".format(i, process))\n\n # # process should now be a Process object; assign to processList\n # self.processList.append(process)\n\n # Assign the Process a reference to this System\n process.systems.append(self)\n if process.learning:\n self.learning = True\n\n # Get max of Process phaseSpecs\n self._phaseSpecMax = int(max(math.floor(process._phaseSpecMax), self._phaseSpecMax))\n\n # Iterate through mechanism tuples in Process' mech_tuples\n # to construct self._all_mech_tuples and mechanismsDict\n # FIX: ??REPLACE WITH: for sender_mech_tuple in Process._mech_tuples\n for sender_mech_tuple in process._mech_tuples:\n\n sender_mech = sender_mech_tuple.mechanism\n\n # THIS IS NOW DONE IN _instantiate_graph\n # # Add system to the Mechanism's list of systems of which it is member\n # if not self in sender_mech_tuple[MECHANISM].systems:\n # sender_mech.systems[self] = INTERNAL\n\n # Assign sender mechanism entry in self.mechanismsDict, with mech_tuple as key and its Process as value\n # (this is used by Process._instantiate_pathway() to determine if Process is part of System)\n # If the sender is already in the System's mechanisms dict\n if sender_mech_tuple.mechanism in self.mechanismsDict:\n existing_mech_tuple = self._allMechanisms._get_tuple_for_mech(sender_mech)\n if not sender_mech_tuple is existing_mech_tuple:\n # Contents of tuple are the same, so use the tuple in _allMechanisms\n if (sender_mech_tuple.phase == existing_mech_tuple.phase and\n sender_mech_tuple.params == existing_mech_tuple.params):\n pass\n # Contents of tuple are different, so raise exception\n else:\n if sender_mech_tuple.phase != existing_mech_tuple.phase:\n offending_tuple_field = 'phase'\n offending_value = PHASE_ITEM\n else:\n offending_tuple_field = 'process_input'\n offending_value = PARAMS_ITEM\n raise SystemError(\"The same mechanism in different processes must have the same parameters:\"\n \"the {} ({}) for {} in {} does not match the value({}) in {}\".\n format(offending_tuple_field,\n sender_mech_tuple.mechanism,\n sender_mech_tuple[offending_value],\n process,\n existing_mech_tuple[offending_value],\n self.mechanismsDict[sender_mech_tuple.mechanism]\n ))\n # Add to entry's list\n self.mechanismsDict[sender_mech].append(process)\n else:\n # Add new entry\n self.mechanismsDict[sender_mech] = [process]\n if not sender_mech_tuple in self._all_mech_tuples:\n self._all_mech_tuples.append(sender_mech_tuple)\n\n process._allMechanisms = MechanismList(process, tuples_list=process._mech_tuples)\n\n # # MODIFIED 2/8/17 OLD: [SEE ABOVE]\n # self.variable = convert_to_np_array(self.variable, 2)\n # # MODIFIED 2/8/17 END\n #\n # # Instantiate processList using process_tuples, and point self.processes to it\n # # Note: this also points self.params[kwProcesses] to self.processes\n self.process_tuples = processes_spec\n self._processList = ProcessList(self, self.process_tuples)\n self.processes = self._processList.processes","async def add_process(self, ctx, process, name):\n name = self.fix_emoji_escapes(name)\n if process in PROCESSES.keys():\n await ctx.send(f\"The process {process} is already being displayed\")\n elif name in PROCESSES.values():\n await ctx.send(f\"The process name {name} is already being displayed\")\n\n else:\n PROCESSES[process] = name\n self.update_processes_config()\n await ctx.send(f\"The process {name} has been added\")","def add_child(self, m, p):\n\t\tn = Node(move=m, parent=self, player_just_moved=p)\n\t\tself.child_nodes.append(n)\n\t\treturn n","def add_node(self, nnode, value):\n new_node = Node(nnode, value)\n self.vert_dict[nnode] = new_node\n return new_node","def _create_process(self, process, name):\n def _run():\n try:\n while True:\n process.loop()\n except KeyboardInterrupt:\n pass\n except:\n self._logger.exception('Process %s died!', name)\n return ProcessEnvironment().create_process(_run, name)","def create_graph():\n # Creates graph from saved graph_def.pb.\n with tf.gfile.FastGFile(sys.argv[1], 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def create_vm(self, account, vminfo):\n node = self.driver(account).ex_create_node_from_template(\n name=vminfo.vm_name,\n template=vminfo.vsphere.template,\n )\n\n vminfo.vm_id = node.id\n\n return node.id","def create_graph():\n # Creates graph from saved graph_def.pb.\n with tf.gfile.FastGFile(os.path.join(\n FLAGS.model_dir, pbfilename), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def _spawn_service_process(self, process_id, name, module, cls, config, proc_attr):\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\n\n listen_name = get_safe(config, \"process.listen_name\") or process_instance.name\n log.debug(\"Service Process (%s) listen_name: %s\", name, listen_name)\n process_instance._proc_listen_name = listen_name\n\n # Service RPC endpoint\n rsvc1 = self._create_listening_endpoint(node=self.container.node,\n from_name=listen_name,\n process=process_instance)\n # Named local RPC endpoint\n rsvc2 = self._create_listening_endpoint(node=self.container.node,\n from_name=process_instance.id,\n process=process_instance)\n\n # cleanup method to delete process queue\n cleanup = lambda _: self._cleanup_method(process_instance.id, rsvc2)\n\n # Start an ION process with the right kind of endpoint factory\n proc = self.proc_sup.spawn(name=process_instance.id,\n service=process_instance,\n listeners=[rsvc1, rsvc2],\n proc_name=process_instance._proc_name,\n cleanup_method=cleanup)\n proc.proc._glname = \"ION Proc %s\" % process_instance._proc_name\n self.proc_sup.ensure_ready(proc, \"_spawn_service_process for %s\" % \",\".join((listen_name, process_instance.id)))\n\n # map gproc to process_instance\n self._spawned_proc_to_process[proc.proc] = process_instance\n\n # set service's reference to process\n process_instance._process = proc\n\n self._process_init(process_instance)\n self._process_start(process_instance)\n\n try:\n proc.start_listeners()\n except IonProcessError:\n self._process_quit(process_instance)\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\n raise\n\n return process_instance","def add_node(graph, node_name, label, shape='record', style='filled', fillcolor='lightgrey'):\n node = Node(name=node_name, shape=shape, style=style, fillcolor=fillcolor, label=label)\n graph.add_node(node)\n return node","def appendProcess(self, pid: int, numberOfVariables: int, processTable, diagnostics) -> int:\n self.memory[pid] = []\n\n for _i in range(numberOfVariables):\n self.memory[pid].append(MemoryItem())\n\n return 0","def _addEntity(self, pid, chunks):\n parent = chunks[pid]\n sub = None\n # Find subject\n for i in range(len(parent.children)):\n child = chunks[parent.children[i]]\n if child.func in SubDict:\n sub = child\n if child.func == \"では\":\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\n pass\n else:\n sub = None\n if sub:\n self._addNode(parent, sub=sub.main)\n self._addEdge(sub.main, parent.main, label=\"陳述\", etype=\"stat\")\n else:\n self._addNode(parent)\n \n # Lopp through all children\n for i in range(len(parent.children)):\n child = chunks[parent.children[i]]\n # If child is noun\n if child.func in SubDict:\n if child.func == \"では\":\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\n pass\n else:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=child.func, etype=\"attr\")\n elif child.type == 0 and child.func in [\"と\", \"などと\"] and child.id + 1 == parent.id and preprocessText(chunks[parent.parent].main) not in [\"交代\", \"交換\"]:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=\"並列\", etype=\"para\")\n self._addEdge(parent.main, child.main, label=\"並列\", etype=\"para\")\n self.para.append([child.main, parent.main])\n elif child.type == 0 and child.func in ParallelDict and child.id + 1 == parent.id:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=\"並列\", etype=\"para\")\n self._addEdge(parent.main, child.main, label=\"並列\", etype=\"para\")\n self.para.append([child.main, parent.main])\n else:\n self._addNode(child)\n self._addEdge(child.main, parent.main, label=child.func, etype=\"attr\")","def insert(self, pid, pname, pparent, pobj, ptype):\r\n self.pids.append(pid)\r\n self.pnames.append(pname)\r\n self.pparents.append(pparent)\r\n self.ptypes.append(ptype)\r\n self.pobjs.append(pobj)","def create_graph():\n # Creates graph from saved graph_def.pb.\n\n # with tf.gfile.FastGFile(os.path.join(\n # FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:\n with tf.gfile.FastGFile(os.path.join(\n FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def insert_node(self, node_tup):\n signature = hashlib.sha256((node_tup[0]+node_tup[4]).encode('utf-8')).hexdigest()\n app_process = sqlite3.connect('app_process::memory:', check_same_thread=False)\n app_process_cursor = app_process.cursor()\n app_process_cursor.execute(\"INSERT INTO nodes VALUES (:ip, :port, uname, :signature)\", {\"ip\":node_tup[0], \"port\":node_tup[1], \"uname\":node_tup[2], \"verifying_key\":node_tup[3]})\n app_process.commit()\n app_process.close()","def create_graph():\n # Creates graph from saved graph_def.pb.\n with tf.gfile.FastGFile(os.path.join(\n MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def addNode(self, nTag, pkg, exe, args, name, namespace):\r\n try:\r\n validateName(nTag)\r\n except IllegalName:\r\n raise InvalidRequest('Node tag is not a valid.')\r\n\r\n if nTag in self._nodes:\r\n raise InvalidRequest(\"Can not use the same node tag '{0}' in the \"\r\n 'same container twice.'.format(nTag))\r\n\r\n node = self._obj.createNode(pkg, exe, args, name, namespace)\r\n self._nodes[nTag] = node\r\n node.notifyOnDeath(self._nodeDied)","def create_graph():\n # Creates graph from saved graph_def.pb.\n with gfile.FastGFile(os.path.join(\n FLAGS.model_dir, 'classify_image_graph_def.pb'), 'r') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def create_graph():\n # Creates graph from saved graph_def.pb.\n with tf.gfile.FastGFile(os.path.join(\n FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def create(self):\n\n self.init_nodes_db()\n\n # group data\n if len(self.depends) == 0:\n grouped = [((), self.data)]\n else:\n grouped = self.data.groupby(self.depends)\n\n # create all the pymc nodes\n for uniq_elem, grouped_data in grouped:\n if not isinstance(uniq_elem, tuple):\n uniq_elem = (uniq_elem,)\n\n # create new kwargs to pass to the new pymc node\n kwargs = self.kwargs.copy()\n\n # update kwarg with the right parent\n for name, parent in self.parents.items():\n kwargs[name] = parent.get_node(self.depends, uniq_elem)\n\n # get node name\n tag, subj_idx = self.create_tag_and_subj_idx(self.depends, uniq_elem)\n node_name = self.create_node_name(tag, subj_idx=subj_idx)\n\n # get value for observed node\n if self.observed:\n if self.pass_dataframe:\n kwargs[\"value\"] = grouped_data[\n self.col_name\n ] # .to_records(index=False)\n else:\n kwargs[\"value\"] = grouped_data[\n self.col_name\n ].values # .to_records(index=False)\n\n # Deterministic nodes require a parent argument that is a\n # dict mapping parent names to parent nodes. Knode wraps\n # this; so here we have to fish out the parent nodes from\n # kwargs, put them into a parent dict and put that back\n # into kwargs, which will make pm.Determinstic() get a\n # parent dict as an argument.\n if self.pymc_node is pm.Deterministic:\n parents_dict = {}\n for name, parent in self.parents.items():\n parents_dict[name] = parent.get_node(self.depends, uniq_elem)\n kwargs.pop(name)\n kwargs[\"parents\"] = parents_dict\n\n if self.observed:\n kwargs[\"parents\"][\"value\"] = kwargs[\"value\"]\n\n # Deterministic nodes require a doc kwarg, we don't really\n # need that so if its not supplied, just use the name\n if self.pymc_node is pm.Deterministic and \"doc\" not in kwargs:\n kwargs[\"doc\"] = node_name\n\n node = self.create_node(node_name, kwargs, grouped_data)\n\n if node is not None:\n self.nodes[uniq_elem] = node\n self.append_node_to_db(node, uniq_elem)","def create(self, context=None):\n values = self.obj_get_changes()\n db_host = self.dbapi.host_create(context, values)\n self._from_db_object(context, self, db_host)","def add_node(self, node: Node):\n prop_str = \",\\n\".join([\"n.%s = '%s'\" % (k, v) for k, v in node.data.items()])\n query = \"\"\"\n MERGE (n:%s {id: '%s'})\n SET %s\n \"\"\" % (\n node.labels,\n norm_id(node.db_ns, node.db_id),\n prop_str,\n )\n return self.create_tx(query)","def to_networkx(self):\n import networkx as nx\n G = nx.DiGraph()\n # G.graph.update(self.config)\n\n if nx.__version__.startswith('1'):\n node_dict = G.node\n else:\n node_dict = G.nodes\n\n def _defaultstyle(node, color, shape='none', **kwargs):\n node_dict[node]['fillcolor'] = color\n node_dict[node]['style'] = 'filled'\n node_dict[node]['shape'] = shape\n node_dict[node].update(kwargs)\n # node_dict[node]['color'] = color\n\n # Add all processes\n # Make inputs and outputs nodes to prevent needing a multigraph\n for proc in self.procs.values():\n G.add_node(proc.name)\n _defaultstyle(proc.name, 'turquoise', shape='ellipse', fontsize=20)\n node_dict[proc.name]['label'] = '{}:\\n{}'.format(proc.name,\n proc.type)\n\n for iport in proc.iports.values():\n iport_name = iport.absname()\n G.add_node(iport_name)\n G.add_edge(iport_name, proc.name)\n node_dict[iport_name]['label'] = iport.name\n _defaultstyle(iport_name, '#fefefe', fontsize=14)\n\n for oport in proc.oports.values():\n oport_name = oport.absname()\n G.add_node(oport_name)\n G.add_edge(proc.name, oport_name)\n node_dict[oport_name]['label'] = oport.name\n _defaultstyle(oport_name, '#f0f0f0', fontsize=14)\n\n # Add all connections\n for proc in self.procs.values():\n for iport in proc.iports.values():\n iport_name = iport.absname()\n for oport in iport.connections:\n if oport is not None:\n oport_name = oport.absname()\n G.add_edge(oport_name, iport_name)\n return G","def add_process(self):\r\n\r\n proc_dict = dict()\r\n total_count = len(self.newest_connections['pid'].unique())\r\n count = 0\r\n for proc in self.newest_connections['pid'].unique():\r\n count += 1\r\n percent = round((count / total_count * 100))\r\n print('{}{}Identifying processes in progress. Accomplished: {}%{}'.format(Colors.GREEN,Colors.BOLD,percent,Colors.END), end='\\r')\r\n output = subprocess.run([\"powershell.exe\", \"-Command\", f'Get-Process -Id {proc} | select-object -Property ProcessName | ft -HideTableHeaders'], capture_output=True, text=True).stdout.strip()\r\n proc_dict[proc] = output\r\n print()\r\n processes = pd.Series(proc_dict)\r\n processes_df = pd.DataFrame(processes.reset_index())\r\n processes_df.columns = ['pid', 'process_name']\r\n if 'process_name' in self.newest_connections:\r\n self.newest_connections = pd.merge(self.newest_connections, processes_df, on=['pid', 'process_name'], how='right')\r\n else:\r\n self.newest_connections = pd.merge(self.newest_connections, processes_df, on='pid', how='right')\r\n return self.newest_connections","def addOnCreate(call, args=(), kwargs={}, nodeClass='*'):\n pass","def add_node(self, metadata, pos):\n node = Node(metadata, pos)\n self.addItem(node)\n self.nodes[node.id] = node\n return node","def __init__(self, pid, binary_path, host_name, node_name, telemetry):\n self.pid = pid\n self.binary_path = binary_path\n self.host_name = host_name\n self.node_name = node_name\n self.telemetry = telemetry","def make_comms(self,comm):\n # For masters we let child_comm be the communicator used to message the node's \n # children, and parent_comm be that used to message the node's parents.\n\n parent_rank = 0\n\n # Case (1)\n if self.num_masters > 1:\n self.make_comms_many(comm)\n if self.is_master:\n parent_comm = self.comm_masters\n if self.comm_masters.Get_rank() == 0: # rank 0 is the super-master\n child_comm = self.comm_masters\n parent_rank = None\n else:\n child_comm = self.comm_block\n # Case (2)\n else:\n self.make_comm_single(comm)\n if self.is_master:\n parent_comm = self.comm_block\n child_comm = self.comm_block\n parent_rank = None\n\n # Process initialization\n from .MPIProcess import MPIWorker, MPIMaster\n if self.is_master:\n self.set_val_data()\n num_sync_workers = self.get_num_sync_workers(child_comm)\n self.process = MPIMaster( parent_comm, parent_rank=parent_rank, \n data=self.data, child_comm=child_comm, num_epochs=self.num_epochs,\n num_sync_workers=num_sync_workers, callbacks=self.callbacks )\n else:\n self.set_train_data()\n self.process = MPIWorker( parent_comm=self.comm_block, parent_rank=parent_rank, \n num_epochs=self.num_epochs, data=self.data, callbacks=self.callbacks )","def create_nodes(self, topogramId, nodes):\n assert type(nodes) is list\n return self.make_request(\"POST\", \"nodes\", { \"topogramId\" : topogramId, \"nodes\" : nodes})","def do_create(self, line):\n if line:\n if line in HBNBCommand.classes:\n class_to_ins = HBNBCommand.classes.get(line)\n new_instance = class_to_ins()\n new_instance.save()\n print(new_instance.id)\n else:\n print(\"** class name missing **\")\n else:\n print(\"** class doesn't exist **\")","def _spawn_immediate_process(self, process_id, name, module, cls, config, proc_attr):\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\n self._process_init(process_instance)\n self._process_start(process_instance)\n return process_instance","def create(self, substrate, comment=''):\n process_tree = ProcessNode.objects.create(process=None, piece='a')\n sample = self.model(substrate=substrate, comment=comment,\n process_tree=process_tree)\n sample.save()\n\n return sample","def create_process(\n target: typing.Callable, args: tuple = (), prepend_lock: bool = False\n) -> Process:\n if prepend_lock:\n args = (LOCK,) + tuple(args)\n process = multiprocessing.Process(target=target, args=args)\n return process","def create_graph():\n with tf.gfile.FastGFile(os.path.join(\n config['inference']['model_dir'], 'output_graph.pb'), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def add_node(self, properties=None, **kwargs):\r\n\t\tif properties is None:\r\n\t\t\tproperties={}\r\n\t\t# may change method sig of Node since we can always combine arguments\r\n\t\t# here\r\n\t\tnode = Node(self._nextid, properties, **kwargs)\r\n\t\tself._nodes[self._nextid] = node\r\n\t\tself._nextid += 1\r\n\t\treturn node","def create_entity(self):\n \n if self.ORION_CB.get_entity(self.params['name']) is None:\n \n print('[INFO]: Create new PID entity')\n \n entity_dict = {\"id\":self.params['name'], \"type\":'PID_controller'}\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\n entity_dict.update({attr:{'value':self.params[attr],'type':'Number'}})\n\n entity_dict.update({'reverse_act':{'value':self.params['reverse_act'],'type':'Text'}})\n \n entity = filip.orion.Entity(entity_dict)#, attrs)\n\n self.ORION_CB.post_entity(entity)\n \n else:\n print('Entity name already assigned')","def add_master_process(self, value=u\"on\"):\n path = [u\"master_process\"]\n self.add_config_item(self._nodeconfig, value, path)","def install_node_instance_subgraph(ctx,instance, graph, hist=None):\n subgraph = graph.subgraph('install_{0}'.format(instance.id))\n\n ct=None\n if hist:\n #get completed tasks for instance\n ct=_completed_tasks(ctx,hist,instance.id)\n\n sequence = subgraph.sequence()\n\n #CREATE\n run=True\n if(hist and 'create' in ct):\n run=False\n\n ctx.logger.info(\"run={} CREATE {}\".format(str(run),instance.id))\n if(run):\n ctx.logger.info(\" hist={} ct={}\".format(str(hist),str(ct)))\n\n if(run):\n sequence.add(\n instance.set_state('initializing'),\n forkjoin(instance.send_event('Creating node'),\n instance.set_state('creating')),\n _add_es_log(ctx,instance,'create',instance.execute_operation('cloudify.interfaces.lifecycle.create')),\n instance.set_state('created'),\n forkjoin(*_relationships_operations(\n instance,\n 'cloudify.interfaces.relationship_lifecycle.preconfigure'\n )))\n\n #CONFIGURE\n run=True\n if(hist and 'configure' in ct):\n run=False\n\n ctx.logger.info(\"run={} CONFIGURE {}\".format(str(run),instance.id))\n\n if(run):\n sequence.add(\n forkjoin(instance.set_state('configuring'),\n instance.send_event('Configuring node')),\n _add_es_log(ctx,instance,'configure',instance.execute_operation('cloudify.interfaces.lifecycle.configure')),\n instance.set_state('configured'),\n forkjoin(*_relationships_operations(\n instance,\n 'cloudify.interfaces.relationship_lifecycle.postconfigure'\n )))\n\n # STARTING\n run=True\n if(hist and 'start' in ct):\n run=False\n\n ctx.logger.info(\"run={} START {}\".format(str(run),instance.id))\n\n if(run):\n sequence.add(\n forkjoin(instance.set_state('starting'),\n instance.send_event('Starting node')),\n instance.execute_operation('cloudify.interfaces.lifecycle.start'))\n\n # If this is a host node, we need to add specific host start\n # tasks such as waiting for it to start and installing the agent\n # worker (if necessary)\n if run and is_host_node(instance):\n sequence.add(*_host_post_start(instance))\n\n sequence.add(\n forkjoin(\n _add_es_log(ctx,instance,'start',instance.execute_operation('cloudify.interfaces.monitoring.start')),\n *_relationships_operations(\n instance,\n 'cloudify.interfaces.relationship_lifecycle.establish'\n )),\n instance.set_state('started'))\n\n subgraph.on_failure = get_install_subgraph_on_failure_handler(ctx,instance)\n return subgraph","def gdb_add_node(node, gdb, rdf, owl):\n gdb_node = gdb.nodes.create()\n node.set_node(gdb_node)\n gdb_node.labels.add([label.split('#')[-1] for label in node.get_labels()])\n for _, pro, obj in rdf.triples((node.get_uri(), None, None)):\n if (pro, RDF.type, owl.DatatypeProperty) in rdf:\n prop_name = pro.split('#')[-1]\n value = obj.split('#')[-1]\n gdb_node.set(prop_name, value)","def node_create(self, parent, path):\n\n q = (\"insert into nodes (parent, path) \"\n \"values (?, ?)\")\n props = (parent, path)\n return self.execute(q, props).lastrowid","def _create_node(self, index: int, name: str, external_id: Optional[str] = None) -> Node:\r\n return Node(graph=self._graph, index=index, name=name, external_id=external_id)","def __init__(self, operator_id: str=None, process_id: str=None): # noqa: E501\n self.swagger_types = {\n 'operator_id': str,\n 'process_id': str\n }\n\n self.attribute_map = {\n 'operator_id': 'operatorId',\n 'process_id': 'processId'\n }\n self._operator_id = operator_id\n self._process_id = process_id","def apply(tree, parameters=None):\r\n from pm4py.objects.bpmn.bpmn_graph import BPMN\r\n counts = Counts()\r\n bpmn = BPMN()\r\n start_event = BPMN.StartEvent(name=\"start\", isInterrupting=True)\r\n end_event = BPMN.EndEvent(name=\"end\")\r\n bpmn.add_node(start_event)\r\n bpmn.add_node(end_event)\r\n bpmn, counts, _, _ = recursively_add_tree(tree, tree, bpmn, start_event, end_event, counts, 0)\r\n bpmn = delete_tau_transitions(bpmn, counts)\r\n\r\n return bpmn","def create_graph():\n\t# Creates graph from saved graph_def.pb.\n\twith tf.gfile.FastGFile('./models/inception_v4.pb', 'rb') as f:\n\t\tgraph_def = tf.GraphDef()\n\t\tgraph_def.ParseFromString(f.read())\n\t\t_ = tf.import_graph_def(graph_def, name='')","def make_node(self, node_property):\n # Try except because Ubigraph is old as hell!\n try: n = self.G.new_vertex()\n except: pass\n for prop, val in node_property.items():\n try: self.G.set_vertex_attribute(n, prop, val)\n except: return make_node(node_property)\n return n","def create_graph():\n with gfile.FastGFile(os.path.join(\n FLAGS.model_dir, FLAGS.model_name), 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n _ = tf.import_graph_def(graph_def, name='')","def test_getStateIncludesProcesses(self):\r\n self.pm.addProcess(\"foo\", [\"arg1\", \"arg2\"],\r\n uid=1, gid=2, env={})\r\n self.assertEqual(self.pm.__getstate__()['processes'],\r\n {'foo': (['arg1', 'arg2'], 1, 2, {})})","def create(cls, user, pool, run_name, experiment, sequencer,\n fwd_cycles, rev_cycles, assay, principal_investigator,\n lanes=None, contacts=None):\n with sql_connection.TRN as TRN:\n # Add the row to the process table\n process_id = cls._common_creation_steps(user)\n\n if fwd_cycles <= 0 or not isinstance(fwd_cycles, int):\n raise ValueError(\"fwd_cycles must be > 0\")\n if rev_cycles <= 0 or not isinstance(rev_cycles, int):\n raise ValueError(\"rev_cycles must be > 0\")\n\n # Add the row to the sequencing table\n sql = \"\"\"INSERT INTO qiita.sequencing_process\n (process_id, pool_composition_id, run_name, experiment,\n sequencer_id, fwd_cycles, rev_cycles, assay,\n principal_investigator, lanes)\n VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n RETURNING sequencing_process_id\"\"\"\n TRN.add(sql, [process_id, pool.id, run_name, experiment,\n sequencer.id, fwd_cycles, rev_cycles, assay,\n principal_investigator.id, dumps(lanes)])\n instance = cls(TRN.execute_fetchlast())\n\n if contacts:\n sql = \"\"\"INSERT INTO qiita.sequencing_process_contacts\n (sequencing_process_id, contact_id)\n VALUES (%s, %s)\"\"\"\n sql_args = [[instance.id, c.id] for c in contacts]\n TRN.add(sql, sql_args, many=True)\n TRN.execute()\n\n return instance","def create(cls, original_args, process_args, base_url, host_url, services):\n return cls(original_args, process_args, base_url, host_url, services)","def convert(cls, node_entry, model_container, node_dict):\n name = node_entry[\"name\"]\n shape = numpy.asarray(\n [a.value for a in node_entry[\"relay_node\"].attrs.newshape], dtype=numpy.int64\n )\n\n input_names = [\n node_entry[\"input_names\"][0],\n add_input(shape, name, \"shape\", model_container),\n ]\n\n node = onnx.helper.make_node(cls.__name__, input_names, node_entry[\"output_names\"])\n model_container.add_nodes([node])","def start_process(check_id, storage, processes):\n\n process = Process(target=perform_check, args=(check_id,\n storage[check_id]['command'],\n storage[check_id]['freq'],))\n\n # Set process name to \"Process+check_id\"\n process.name = 'Process{}'.format(check_id)\n process.start()\n\n # Add process to processes dict with key=pid and value=processname\n processes[process.pid] = process\n storage[check_id]['pid'] = process.pid","def add_node(self, node: base.Node, label: str = None) -> HandleType:\n\n if self._current_group:\n if label and label != self._current_group:\n raise ValueError('The given label does not match the current group: '\n f'{label} vs {self._current_group}.')\n label = self._current_group\n else:\n if not label:\n raise ValueError('Label should not be empty.')\n if label not in self._groups:\n self._groups[label] = [node]\n else:\n self._groups[label].append(node)\n return node.create_handle()","def _internal_add_node(self,\r\n node_name: str,\r\n external_id: Optional[str] = None,\r\n are_neighbors_cached: bool = False,\r\n add_to_cache: bool = False) -> None:\r\n index: int = len(self)\r\n node: Node = self._create_node(index, node_name, external_id)\r\n node.are_neighbors_cached = are_neighbors_cached\r\n self._nodes[index] = node\r\n self._node_name_map[node_name] = node\r\n\r\n if add_to_cache:\r\n db: GraphDatabaseInterface = self._graph.database\r\n db_node: DBNode = db.Node.find_by_name(node.name)\r\n if db_node is None:\r\n db_node = db.Node(node.name, node.external_id)\r\n db_node.are_neighbors_cached = False\r\n db.session.add(db_node)\r\n db.session.commit()","def create_node(self, *labels, **props):\n component = Node(*labels, **props)\n self.graph.create(component)\n return component"],"string":"[\n \"def create_node_process(name, properties):\\n process = NodeProcess(name)\\n DB.session.add(process)\\n DB.session.commit()\\n for p in properties:\\n prop = NodeProcessProperty(process.id, p[0], p[1], p[2])\\n DB.session.add(prop)\\n DB.session.commit()\\n return process\",\n \"def add_process(self, type, name=None, config=None):\\n assert name is not None, 'must specify name for now'\\n node = Process(type=type, name=name, config=config)\\n self.procs[name] = node\\n return node\",\n \"def insert_process_after(self, process, uuid,\\n comment='', force_refresh=True):\\n parent = self.get_node(uuid)\\n children = list(parent.get_children())\\n node = ProcessNode.objects.create(process=process, piece=parent.piece,\\n comment=comment, parent_id=parent.id)\\n for child in children:\\n child.parent = node\\n child.save()\\n\\n if force_refresh:\\n self.refresh_tree()\\n return node\",\n \"def add_process(self):\\n process_id = str(self.processBox.currentText())\\n\\n arguments = {}\\n\\n for row in range(0, self.processTableWidget.rowCount()):\\n p_id = \\\"\\\"\\n val = None\\n\\n if self.processTableWidget.item(row, 0):\\n p_id = self.processTableWidget.item(row, 0).text()\\n if self.processTableWidget.item(row, 2):\\n val = self.processTableWidget.item(row, 2).text()\\n if len(val) > 0:\\n try:\\n val = json.loads(val)\\n except json.JSONDecodeError:\\n pass\\n else:\\n val = None\\n if p_id != \\\"\\\":\\n if val:\\n arguments[p_id] = val\\n\\n self.processgraph = self.processgraph.add_process(process_id, arguments)\\n # Refresh process graph in GUI\\n self.reload_processgraph_view()\",\n \"def insert_process_before(self, process, uuid,\\n comment='', force_refresh=True):\\n target = self.get_node(uuid)\\n if target == self.root_node:\\n raise Exception('Error: Cannot insert before the root node.')\\n\\n parent = target.parent\\n children = list(target.get_siblings(include_self=True))\\n node = ProcessNode.objects.create(process=process, piece=parent.piece,\\n comment=comment, parent_id=parent.id)\\n for child in children:\\n child.parent = node\\n child.save()\\n\\n if force_refresh:\\n self.refresh_tree()\\n return node\",\n \"def add_process(self, process):\\n self.processes[process.namespec()] = process\",\n \"def process(cls, process_id: str, arguments: dict = None, namespace: Union[str, None] = None, **kwargs):\\n arguments = {**(arguments or {}), **kwargs}\\n for arg, value in arguments.items():\\n if isinstance(value, ProcessBuilderBase):\\n arguments[arg] = value.pgnode\\n elif isinstance(value,list):\\n for index,arrayelement in enumerate(value):\\n if(isinstance(arrayelement,ProcessBuilderBase)):\\n value[index] = arrayelement.pgnode\\n\\n for arg in [a for a, v in arguments.items() if v is UNSET]:\\n del arguments[arg]\\n return cls(PGNode(process_id=process_id, arguments=arguments, namespace=namespace))\",\n \"def graph_add_process(self, process_id, args) -> 'ImageCollection':\\n graph = {\\n 'process_id': process_id,\\n 'args': args\\n }\\n\\n return RESTProcesses(graph, self.connection)\",\n \"def _RegisterProcess(self, process):\\n if process is None:\\n raise ValueError('Missing process.')\\n\\n if process.pid in self._processes_per_pid:\\n raise KeyError(\\n 'Already managing process: {0!s} (PID: {1:d})'.format(\\n process.name, process.pid))\\n\\n self._processes_per_pid[process.pid] = process\",\n \"def addProcessObject(self, process, env, uid=None, gid=None):\\n name = process.getName()\\n self.processes[name] = (process, env, uid, gid)\\n self.delay[name] = self.minRestartDelay\\n if self.running:\\n self.startProcess(name)\",\n \"def run_process(self, process, piece='a', number=1, comment='', force_refresh=True):\\n branch = self.get_piece(piece)\\n node = self._insert_node(process, piece, number, branch, comment)\\n if force_refresh: # workaround to force the root node to update\\n self.refresh_tree()\\n return node\",\n \"def custom_process_from_process_graph(\\n process_spec: Union[dict, Path],\\n process_registry: ProcessRegistry = process_registry_100,\\n namespace: str = DEFAULT_NAMESPACE\\n):\\n # TODO: option to hide process graph for (public) listing\\n if isinstance(process_spec, Path):\\n process_spec = load_json(process_spec)\\n process_id = process_spec[\\\"id\\\"]\\n process_function = _process_function_from_process_graph(process_spec)\\n process_registry.add_function(process_function, name=process_id, spec=process_spec, namespace=namespace)\",\n \"def _create_process_instance(self, process_id, name, module, cls, config, proc_attr):\\n # SERVICE INSTANCE.\\n process_instance = for_name(module, cls)\\n if not isinstance(process_instance, BaseService):\\n raise ContainerConfigError(\\\"Instantiated service not a BaseService %r\\\" % process_instance)\\n\\n # Prepare service instance\\n process_instance.errcause = \\\"\\\"\\n process_instance.id = process_id\\n process_instance.container = self.container\\n process_instance.CFG = config\\n process_instance._proc_name = name\\n process_instance._proc_start_time = time.time()\\n for att, att_val in proc_attr.iteritems():\\n setattr(process_instance, att, att_val)\\n\\n #Unless the process has been started as part of another Org, default to the container Org or the ION Org\\n if config.has_key('org_governance_name'):\\n process_instance.org_governance_name = config['org_governance_name']\\n else:\\n process_instance.org_governance_name = CFG.get_safe('container.org_name', CFG.get_safe('system.root_org', 'ION'))\\n\\n\\n # Add stateful process operations\\n if hasattr(process_instance, \\\"_flush_state\\\"):\\n def _flush_state():\\n with process_instance._state_lock:\\n state_obj = process_instance.container.state_repository.put_state(process_instance.id, process_instance._proc_state,\\n state_obj=process_instance._proc_state_obj)\\n state_obj.state = None # Make sure memory footprint is low for larger states\\n process_instance._proc_state_obj = state_obj\\n process_instance._proc_state_changed = False\\n\\n def _load_state():\\n if not hasattr(process_instance, \\\"_proc_state\\\"):\\n process_instance._proc_state = {}\\n try:\\n with process_instance._state_lock:\\n new_state, state_obj = process_instance.container.state_repository.get_state(process_instance.id)\\n process_instance._proc_state.clear()\\n process_instance._proc_state.update(new_state)\\n process_instance._proc_state_obj = state_obj\\n process_instance._proc_state_changed = False\\n except NotFound as nf:\\n log.debug(\\\"No persisted state available for process %s\\\", process_instance.id)\\n except Exception as ex:\\n log.warn(\\\"Process %s load state failed: %s\\\", process_instance.id, str(ex))\\n process_instance._flush_state = _flush_state\\n process_instance._load_state = _load_state\\n process_instance._state_lock = RLock()\\n process_instance._proc_state = {}\\n process_instance._proc_state_obj = None\\n process_instance._proc_state_changed = False\\n\\n # PROCESS RESTART: Need to check whether this process had persisted state.\\n # Note: This could happen anytime during a system run, not just on RESTART boot\\n log.debug(\\\"Loading persisted state for process %s\\\", process_id)\\n process_instance._load_state()\\n\\n # start service dependencies (RPC clients)\\n self._start_process_dependencies(process_instance)\\n\\n return process_instance\",\n \"def create(self, context=None):\\n values = self.obj_get_changes()\\n db_nodegroup = self.dbapi.create_nodegroup(values)\\n self._from_db_object(self, db_nodegroup)\",\n \"def new_process() -> Process:\\n return multiprocessing.Process()\",\n \"def alloc_proc(self, process, delta_t):\\n\\t\\tself._process_list.append(process)\",\n \"def create_node_type(name, processes):\\n node_type = NodeType(name)\\n node_type.processes = processes\\n DB.session.add(node_type)\\n DB.session.commit()\\n return node_type\",\n \"def create_process(\\n self,\\n model,\\n client_plans,\\n client_config,\\n server_config,\\n server_averaging_plan,\\n client_protocols=None,\\n ):\\n process = FLProcess(\\n model=model,\\n client_plans=client_plans,\\n client_config=client_config,\\n server_config=server_config,\\n client_protocols=client_protocols,\\n server_averaging_plan=server_averaging_plan,\\n )\\n\\n self.processes[process.id] = process\\n return self.processes[process.id]\",\n \"def test_addProcess(self):\\r\\n self.pm.addProcess(\\\"foo\\\", [\\\"arg1\\\", \\\"arg2\\\"],\\r\\n uid=1, gid=2, env={})\\r\\n self.assertEqual(self.pm.protocols, {})\\r\\n self.assertEqual(self.pm.processes,\\r\\n {\\\"foo\\\": ([\\\"arg1\\\", \\\"arg2\\\"], 1, 2, {})})\\r\\n self.pm.startService()\\r\\n self.reactor.advance(0)\\r\\n self.assertEqual(self.pm.protocols.keys(), [\\\"foo\\\"])\",\n \"def add_task(bpmn, counts, label):\\r\\n from pm4py.objects.bpmn.bpmn_graph import BPMN\\r\\n task = BPMN.Task(name=label)\\r\\n bpmn.add_node(task)\\r\\n return bpmn, task, counts\",\n \"def addProcess(self, name, args, uid=None, gid=None, env={}):\\n class SimpleProcessObject(object):\\n\\n def starting(self):\\n pass\\n\\n def stopped(self):\\n pass\\n\\n def getName(self):\\n return name\\n\\n def getCommandLine(self):\\n return args\\n\\n def getFileDescriptors(self):\\n return []\\n\\n self.addProcessObject(SimpleProcessObject(), env, uid, gid)\",\n \"def _register_process(self, process_instance, name):\\n # Add process instance to container's process dict\\n if name in self.procs_by_name:\\n log.warn(\\\"Process name already registered in container: %s\\\" % name)\\n self.procs_by_name[name] = process_instance\\n self.procs[process_instance.id] = process_instance\\n\\n # Add Process to resource registry\\n # Note: In general the Process resource should be created by the CEI PD, but not all processes are CEI\\n # processes. How to deal with this?\\n process_instance.errcause = \\\"registering\\\"\\n\\n if process_instance._proc_type != IMMEDIATE_PROCESS_TYPE:\\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\\n proc_obj = Process(name=process_instance.id, label=name, proctype=process_instance._proc_type)\\n proc_id, _ = self.container.resource_registry.create(proc_obj)\\n process_instance._proc_res_id = proc_id\\n\\n # Associate process with container resource\\n self.container.resource_registry.create_association(self.cc_id, \\\"hasProcess\\\", proc_id)\\n else:\\n process_instance._proc_res_id = None\\n\\n # Process type specific registration\\n # TODO: Factor out into type specific handler functions\\n if process_instance._proc_type == SERVICE_PROCESS_TYPE:\\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\\n # Registration of SERVICE process: in resource registry\\n service_list, _ = self.container.resource_registry.find_resources(restype=\\\"Service\\\", name=process_instance.name, id_only=True)\\n if service_list:\\n process_instance._proc_svc_id = service_list[0]\\n if len(service_list) > 1:\\n log.warn(\\\"More than 1 Service resource found with name %s: %s\\\", process_instance.name, service_list)\\n else:\\n # We are starting the first process of a service instance\\n # TODO: This should be created by the HA Service agent in the future\\n svc_obj = Service(name=process_instance.name, exchange_name=process_instance._proc_listen_name, state=ServiceStateEnum.READY)\\n process_instance._proc_svc_id, _ = self.container.resource_registry.create(svc_obj)\\n\\n # Create association to service definition resource\\n svcdef_list, _ = self.container.resource_registry.find_resources(restype=\\\"ServiceDefinition\\\",\\n name=process_instance.name, id_only=True)\\n if svcdef_list:\\n if len(svcdef_list) > 1:\\n log.warn(\\\"More than 1 ServiceDefinition resource found with name %s: %s\\\", process_instance.name, svcdef_list)\\n self.container.resource_registry.create_association(process_instance._proc_svc_id,\\n \\\"hasServiceDefinition\\\", svcdef_list[0])\\n else:\\n log.error(\\\"Cannot find ServiceDefinition resource for %s\\\", process_instance.name)\\n\\n self.container.resource_registry.create_association(process_instance._proc_svc_id, \\\"hasProcess\\\", proc_id)\\n\\n elif process_instance._proc_type == AGENT_PROCESS_TYPE:\\n if self.container.has_capability(self.container.CCAP.DIRECTORY):\\n # Registration of AGENT process: in Directory\\n caps = process_instance.get_capabilities()\\n self.container.directory.register(\\\"/Agents\\\", process_instance.id,\\n **dict(name=process_instance._proc_name,\\n container=process_instance.container.id,\\n resource_id=process_instance.resource_id,\\n agent_id=process_instance.agent_id,\\n def_id=process_instance.agent_def_id,\\n capabilities=caps))\\n\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.RUNNING)\",\n \"def spawnProcess(self, processProtocol, executable, args=(), env={},\\r\\n path=None, uid=None, gid=None, usePTY=0,\\r\\n childFDs=None):\\r\\n\\r\\n proc = DummyProcess(self, executable, args, env, path,\\r\\n processProtocol, uid, gid, usePTY, childFDs)\\r\\n processProtocol.makeConnection(proc)\\r\\n self.spawnedProcesses.append(proc)\\r\\n return proc\",\n \"def addProcess(self, name, proc_config):\\n if self.processes.has_key(name):\\n raise KeyError(\\\"remove %s first\\\" % name)\\n p = self.engineProtocol()\\n p.service = self\\n p.name = name\\n proc_config.processProtocol = p\\n self.processes[name] = proc_config\\n if self.running:\\n self.startProcess(name)\\n return p.deferred\",\n \"def add_node(self, ip, port, father_address):\\n father = self.find_node(father_address)\\n if father is not None:\\n node = GraphNode((ip, port))\\n node.alive = True\\n node.set_parent(father)\\n father.add_child(node)\\n self.nodes.append(node)\\n pass\",\n \"def graph_add_process(self, process_id, args) -> 'ImageCollection':\\n graph = {\\n 'process_id': process_id,\\n\\n }\\n\\n for key, value in args.items():\\n graph[key] = value\\n\\n #graph = {\\n # 'process_id': process_id,\\n # 'args': args\\n #}\\n\\n return RestImagery(graph, self.session)\",\n \"def spawn_process(self, name=None, module=None, cls=None, config=None, process_id=None):\\n if process_id and not is_valid_identifier(process_id, ws_sub='_'):\\n raise BadRequest(\\\"Given process_id %s is not a valid identifier\\\" % process_id)\\n\\n # Generate a new process id if not provided\\n # TODO: Ensure it is system-wide unique\\n process_id = process_id or \\\"%s.%s\\\" % (self.container.id, self.proc_id_pool.get_id())\\n log.debug(\\\"ProcManager.spawn_process(name=%s, module.cls=%s.%s, config=%s) as pid=%s\\\", name, module, cls, config, process_id)\\n\\n process_cfg = deepcopy(CFG)\\n if config:\\n # Use provided config. Must be dict or DotDict\\n if not isinstance(config, DotDict):\\n config = DotDict(config)\\n if config.get_safe(\\\"process.config_ref\\\"):\\n # Use a reference\\n config_ref = config.get_safe(\\\"process.config_ref\\\")\\n log.info(\\\"Enhancing new process spawn config from ref=%s\\\" % config_ref)\\n matches = re.match(r'^([A-Za-z]+):([A-Za-z0-9_\\\\.]+)/(.*)$', config_ref)\\n if matches:\\n ref_type, ref_id, ref_ext = matches.groups()\\n if ref_type == \\\"resources\\\":\\n if self.container.has_capability(self.container.CCAP.RESOURCE_REGISTRY):\\n try:\\n obj = self.container.resource_registry.read(ref_id)\\n if obj and hasattr(obj, ref_ext):\\n ref_config = getattr(obj, ref_ext)\\n if isinstance(ref_config, dict):\\n dict_merge(process_cfg, ref_config, inplace=True)\\n else:\\n raise BadRequest(\\\"config_ref %s exists but not dict\\\" % config_ref)\\n else:\\n raise BadRequest(\\\"config_ref %s - attribute not found\\\" % config_ref)\\n except NotFound as nf:\\n log.warn(\\\"config_ref %s - object not found\\\" % config_ref)\\n raise\\n else:\\n log.error(\\\"Container missing RESOURCE_REGISTRY capability to resolve process config ref %s\\\" % config_ref)\\n elif ref_type == \\\"objects\\\":\\n if self.container.has_capability(self.container.CCAP.OBJECT_STORE):\\n try:\\n obj = self.container.object_store.read_doc(ref_id)\\n ref_config = obj\\n if ref_ext:\\n ref_config = get_safe(obj, ref_ext, None)\\n if ref_config is None:\\n raise BadRequest(\\\"config_ref %s - attribute not found\\\" % config_ref)\\n\\n if isinstance(ref_config, dict):\\n dict_merge(process_cfg, ref_config, inplace=True)\\n else:\\n raise BadRequest(\\\"config_ref %s exists but not dict\\\" % config_ref)\\n except NotFound as nf:\\n log.warn(\\\"config_ref %s - object not found\\\" % config_ref)\\n raise\\n else:\\n log.error(\\\"Container missing OBJECT_STORE capability to resolve process config ref %s\\\" % config_ref)\\n else:\\n raise BadRequest(\\\"Unknown reference type in: %s\\\" % config_ref)\\n\\n dict_merge(process_cfg, config, inplace=True)\\n if self.container.spawn_args:\\n # Override config with spawn args\\n dict_merge(process_cfg, self.container.spawn_args, inplace=True)\\n\\n #log.debug(\\\"spawn_process() pid=%s process_cfg=%s\\\", process_id, process_cfg)\\n\\n # PROCESS TYPE. Determines basic process context (messaging, service interface)\\n # One of the constants defined at the top of this file\\n\\n service_cls = named_any(\\\"%s.%s\\\" % (module, cls))\\n process_type = get_safe(process_cfg, \\\"process.type\\\") or getattr(service_cls, \\\"process_type\\\", \\\"service\\\")\\n\\n process_start_mode = get_safe(config, \\\"process.start_mode\\\")\\n\\n process_instance = None\\n\\n # alert we have a spawning process, but we don't have the instance yet, so give the class instead (more accurate than name)\\n self._call_proc_state_changed(\\\"%s.%s\\\" % (module, cls), ProcessStateEnum.PENDING)\\n\\n try:\\n # Additional attributes to set with the process instance\\n proc_attr = {\\\"_proc_type\\\": process_type,\\n \\\"_proc_spawn_cfg\\\": config\\n }\\n\\n # spawn process by type\\n if process_type == SERVICE_PROCESS_TYPE:\\n process_instance = self._spawn_service_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n elif process_type == STREAM_PROCESS_TYPE:\\n process_instance = self._spawn_stream_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n elif process_type == AGENT_PROCESS_TYPE:\\n process_instance = self._spawn_agent_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n elif process_type == STANDALONE_PROCESS_TYPE:\\n process_instance = self._spawn_standalone_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n elif process_type == IMMEDIATE_PROCESS_TYPE:\\n process_instance = self._spawn_immediate_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n elif process_type == SIMPLE_PROCESS_TYPE:\\n process_instance = self._spawn_simple_process(process_id, name, module, cls, process_cfg, proc_attr)\\n\\n else:\\n raise BadRequest(\\\"Unknown process type: %s\\\" % process_type)\\n\\n self._register_process(process_instance, name)\\n\\n process_instance.errcause = \\\"OK\\\"\\n log.info(\\\"ProcManager.spawn_process: %s.%s -> pid=%s OK\\\", module, cls, process_id)\\n\\n if process_type == IMMEDIATE_PROCESS_TYPE:\\n log.info('Terminating immediate process: %s', process_instance.id)\\n self.terminate_process(process_instance.id)\\n\\n # terminate process also triggers TERMINATING/TERMINATED\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.EXITED)\\n\\n else:\\n #Update local policies for the new process\\n if self.container.has_capability(self.container.CCAP.GOVERNANCE_CONTROLLER):\\n self.container.governance_controller.update_container_policies(process_instance, safe_mode=True)\\n\\n return process_instance.id\\n\\n except IonProcessError:\\n errcause = process_instance.errcause if process_instance else \\\"instantiating process\\\"\\n log.exception(\\\"Error spawning %s %s process (process_id: %s): %s\\\", name, process_type, process_id, errcause)\\n return None\\n\\n except Exception:\\n errcause = process_instance.errcause if process_instance else \\\"instantiating process\\\"\\n log.exception(\\\"Error spawning %s %s process (process_id: %s): %s\\\", name, process_type, process_id, errcause)\\n\\n # trigger failed notification - catches problems in init/start\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\\n\\n raise\",\n \"def add_process(self, model_id, n_cores, n_time, s_time):\\n p = Process(n_cores=n_cores, time_needed=n_time, model_id=model_id, start_time=s_time)\\n self.process_list.append(p)\",\n \"def createNode(self, pkg, exe, args, name, nspace):\\r\\n node = Node(self)\\r\\n self.callRemote('createNode', pkg, exe, args, name,\\r\\n nspace).chainDeferred(node)\\r\\n return node\",\n \"def _create(self, context, values):\\n # initialize load stats from existing instances:\\n compute_node = db.compute_node_create(context, values)\\n return compute_node\",\n \"def _spawn_simple_process(self, process_id, name, module, cls, config, proc_attr):\\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\\n # Add publishers if any...\\n publish_streams = get_safe(config, \\\"process.publish_streams\\\")\\n pub_names = self._set_publisher_endpoints(process_instance, publish_streams)\\n\\n # cleanup method to delete process queue (@TODO: leaks a bit here - should use XOs)\\n def cleanup(*args):\\n for name in pub_names:\\n p = getattr(process_instance, name)\\n p.close()\\n\\n proc = self.proc_sup.spawn(name=process_instance.id,\\n service=process_instance,\\n listeners=[],\\n proc_name=process_instance._proc_name,\\n cleanup_method=cleanup)\\n proc.proc._glname = \\\"ION Proc %s\\\" % process_instance._proc_name\\n self.proc_sup.ensure_ready(proc, \\\"_spawn_simple_process for %s\\\" % process_instance.id)\\n\\n # map gproc to process_instance\\n self._spawned_proc_to_process[proc.proc] = process_instance\\n\\n # set service's reference to process\\n process_instance._process = proc\\n\\n self._process_init(process_instance)\\n self._process_start(process_instance)\\n\\n return process_instance\",\n \"def remote_createNode(self, pkg, exe, args, name, namespace):\\r\\n return Node(self, pkg, exe, args, name, namespace)\",\n \"def describe_process(self, process):\\n description = self.client.describeprocess(process.identifier)\\n\\n new_process = Process.from_owslib(description)\\n\\n new_process.inputs = process.inputs\\n\\n new_process.domain = process.domain\\n\\n new_process.parameters = process.parameters\\n\\n return new_process\",\n \"def insert_workflow_module(self, project_id, branch_id, before_module_id, command):\\n with self.backend.lock:\\n # Get the handle for the specified branch and the branch head\\n branch = self.projects.get_branch(project_id=project_id, branch_id=branch_id)\\n if branch is None:\\n return None\\n head = branch.get_head()\\n if head is None or len(head.modules) == 0:\\n return None\\n\\n # Get the index of the module at which the new module is inserted\\n module_index = None\\n modules = head.modules\\n for i in range(len(modules)):\\n if modules[i].identifier == before_module_id:\\n module_index = i\\n break\\n if module_index is None:\\n return None\\n\\n # Get handle for the inserted module\\n context = compute_context(modules[0:module_index])\\n # Create handle for the inserted module. The state of the module\\n # depends on the state of the backend.\\n if head.is_active:\\n state = mstate.MODULE_PENDING\\n else:\\n state = self.backend.next_task_state()\\n inserted_module = ModuleHandle(\\n command=command,\\n state=state,\\n external_form=command.to_external_form(\\n command=self.packages[command.package_id].get(command.command_id),\\n datasets=[ context[name] for name in context if context[name].is_dataset ]\\n ),\\n provenance=ModuleProvenance(unexecuted=True)\\n )\\n # Create list of pending modules for the new workflow.\\n pending_modules = [inserted_module]\\n for m in modules[module_index:]:\\n pending_modules.append(\\n ModuleHandle(\\n command=m.command,\\n external_form=m.external_form,\\n outputs=m.outputs,\\n provenance=m.provenance\\n )\\n )\\n workflow = branch.append_workflow(\\n modules=modules[:module_index],\\n action=wf.ACTION_INSERT,\\n command=inserted_module.command,\\n pending_modules=pending_modules\\n )\\n if not head.is_active:\\n self.execute_module(\\n project_id=project_id,\\n branch_id=branch_id,\\n module=workflow.modules[module_index],\\n artifacts=context,\\n )\\n return workflow.modules[module_index:]\",\n \"def _spawn_standalone_process(self, process_id, name, module, cls, config, proc_attr):\\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\\n rsvc = self._create_listening_endpoint(node=self.container.node,\\n from_name=process_instance.id,\\n process=process_instance)\\n\\n # Add publishers if any...\\n publish_streams = get_safe(config, \\\"process.publish_streams\\\")\\n pub_names = self._set_publisher_endpoints(process_instance, publish_streams)\\n\\n # cleanup method to delete process queue (@TODO: leaks a bit here - should use XOs)\\n def cleanup(*args):\\n self._cleanup_method(process_instance.id, rsvc)\\n for name in pub_names:\\n p = getattr(process_instance, name)\\n p.close()\\n\\n proc = self.proc_sup.spawn(name=process_instance.id,\\n service=process_instance,\\n listeners=[rsvc],\\n proc_name=process_instance._proc_name,\\n cleanup_method=cleanup)\\n proc.proc._glname = \\\"ION Proc %s\\\" % process_instance._proc_name\\n self.proc_sup.ensure_ready(proc, \\\"_spawn_standalone_process for %s\\\" % process_instance.id)\\n\\n # map gproc to process_instance\\n self._spawned_proc_to_process[proc.proc] = process_instance\\n\\n # set service's reference to process\\n process_instance._process = proc\\n\\n self._process_init(process_instance)\\n self._process_start(process_instance)\\n\\n try:\\n proc.start_listeners()\\n except IonProcessError:\\n self._process_quit(process_instance)\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\\n raise\\n\\n return process_instance\",\n \"def create_process(args):\\n process = args.process\\n\\n # create list of valid machines\\n valid_machines = []\\n valid_types = [cmpy.machines.MealyHMM, \\n cmpy.machines.RecurrentEpsilonMachine]\\n\\n for em in dir(cmpy.machines):\\n if em[0].isupper():\\n try:\\n m_str = 'cmpy.machines.' + em +'()' \\n eval(m_str)\\n mtype = type(eval(m_str))\\n if mtype in valid_types:\\n valid_machines.append(em)\\n except:\\n pass\\n\\n # remove MealyHMM, RecurrentEpsilonMachine\\n valid_machines.remove('MealyHMM')\\n valid_machines.remove('RecurrentEpsilonMachine')\\n\\n # if in valid_machine, try to create instance\\n if process in valid_machines:\\n eM = eval('cmpy.machines.' + process + '()')\\n else: \\n error_msg = (\\\"\\\\n\\\\nProcess {} not valid. Try:\\\\n\\\\n{}\\\\n\\\".format(process,\\n valid_machines))\\n raise ProcessException(error_msg)\\n\\n return eM\",\n \"def add_node (self, node):\\n self.network.add_node(node.id)\\n self.network.node[node.id] = node\",\n \"def create_node(self, address, service):\\n node = create_node(address, service)\\n node.id = address + \\\"_\\\" + service\\n return node\",\n \"def register_proc(self, pid: int):\\n self.processes.add(pid)\",\n \"def _spawn_agent_process(self, process_id, name, module, cls, config, proc_attr):\\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\\n if not isinstance(process_instance, ResourceAgent) and not isinstance(process_instance, SimpleResourceAgent):\\n raise ContainerConfigError(\\\"Agent process must extend ResourceAgent\\\")\\n listeners = []\\n\\n # Set the resource ID if we get it through the config\\n resource_id = get_safe(process_instance.CFG, \\\"agent.resource_id\\\")\\n if resource_id:\\n process_instance.resource_id = resource_id\\n\\n alistener = self._create_listening_endpoint(node=self.container.node,\\n from_name=resource_id,\\n process=process_instance)\\n\\n listeners.append(alistener)\\n\\n rsvc = self._create_listening_endpoint(node=self.container.node,\\n from_name=process_instance.id,\\n process=process_instance)\\n\\n listeners.append(rsvc)\\n\\n # cleanup method to delete process/agent queue (@TODO: leaks a bit here - should use XOs)\\n def agent_cleanup(x):\\n self._cleanup_method(process_instance.id, rsvc)\\n if resource_id:\\n self._cleanup_method(resource_id, alistener)\\n\\n proc = self.proc_sup.spawn(name=process_instance.id,\\n service=process_instance,\\n listeners=listeners,\\n proc_name=process_instance._proc_name,\\n cleanup_method=agent_cleanup)\\n proc.proc._glname = \\\"ION Proc %s\\\" % process_instance._proc_name\\n self.proc_sup.ensure_ready(proc, \\\"_spawn_agent_process for %s\\\" % process_instance.id)\\n\\n # map gproc to process_instance\\n self._spawned_proc_to_process[proc.proc] = process_instance\\n\\n # set service's reference to process\\n process_instance._process = proc\\n\\n # Now call the on_init of the agent.\\n self._process_init(process_instance)\\n\\n if not process_instance.resource_id:\\n log.warn(\\\"New agent pid=%s has no resource_id set\\\" % process_id)\\n\\n self._process_start(process_instance)\\n\\n try:\\n proc.start_listeners()\\n except IonProcessError:\\n self._process_quit(process_instance)\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\\n raise\\n\\n if not process_instance.resource_id:\\n log.warn(\\\"Agent process id=%s does not define resource_id!!\\\" % process_instance.id)\\n\\n return process_instance\",\n \"def register_process(self, data_process_id=''):\\n\\n # retrieve the data_process object\\n data_process_obj = self.clients.resource_registry.read(data_process_id)\\n if data_process_obj is None:\\n raise NotFound(\\\"Data Process %s does not exist\\\" % data_process_id)\\n\\n #create data producer resource and associate to this data_process_id\\n data_producer_obj = IonObject(RT.DataProducer,name=data_process_obj.name, description=\\\"primary producer resource for this process\\\", is_primary=True)\\n data_producer_id, rev = self.clients.resource_registry.create(data_producer_obj)\\n\\n # Create association\\n self.clients.resource_registry.create_association(data_process_id, PRED.hasDataProducer, data_producer_id)\\n\\n # TODO: Walk up the assocations to find parent producers:\\n # proc->subscription->stream->prod\\n\\n return data_producer_id\",\n \"def create_node_handle(node_name, node_type_name, node_meta_type):\\n user = get_user()\\n node_type = get_node_type(node_type_name)\\n node_handle = NodeHandle.objects.create(node_name=node_name, node_type=node_type, node_meta_type=node_meta_type,\\n creator=user, modifier=user)\\n node_handle.save()\\n activitylog.create_node(user, node_handle)\\n return node_handle\",\n \"def _add_node(self, node_name, node_type):\\n q = 'MATCH (r:' + node_type + ') WHERE r.name=\\\"' \\\\\\n + node_name + '\\\" RETURN r'\\n results = self.db.query(q, returns=(client.Node, str, client.Node))\\n res = self.db.labels.create(node_type)\\n\\n if (len(results) == 0):\\n r = self.db.nodes.create(name=node_name)\\n res.add(r)\\n else:\\n r = results[0][0]\\n return r\",\n \"def create_node(self, **kwargs):\\n if not self.nodes:\\n self.get_nodes()\\n\\n _node = Node(project_id=self.project_id, connector=self.connector, **kwargs)\\n\\n _node.create()\\n self.nodes.append(_node)\\n print(\\n f\\\"Created: {_node.name} -- Type: {_node.node_type} -- \\\"\\n f\\\"Console: {_node.console}\\\"\\n )\",\n \"def _StartMonitoringProcess(self, process):\\n if process is None:\\n raise ValueError('Missing process.')\\n\\n pid = process.pid\\n\\n if pid in self._process_information_per_pid:\\n raise KeyError(\\n 'Already monitoring process (PID: {0:d}).'.format(pid))\\n\\n if pid in self._rpc_clients_per_pid:\\n raise KeyError(\\n 'RPC client (PID: {0:d}) already exists'.format(pid))\\n\\n rpc_client = plaso_xmlrpc.XMLProcessStatusRPCClient()\\n\\n # Make sure that a worker process has started its RPC server.\\n # The RPC port will be 0 if no server is available.\\n rpc_port = process.rpc_port.value\\n time_waited_for_process = 0.0\\n while not rpc_port:\\n time.sleep(0.1)\\n rpc_port = process.rpc_port.value\\n time_waited_for_process += 0.1\\n\\n if time_waited_for_process >= self._RPC_SERVER_TIMEOUT:\\n raise IOError(\\n 'RPC client unable to determine server (PID: {0:d}) port.'.format(\\n pid))\\n\\n hostname = 'localhost'\\n\\n if not rpc_client.Open(hostname, rpc_port):\\n raise IOError((\\n 'RPC client unable to connect to server (PID: {0:d}) '\\n 'http://{1:s}:{2:d}').format(pid, hostname, rpc_port))\\n\\n self._rpc_clients_per_pid[pid] = rpc_client\\n self._process_information_per_pid[pid] = process_info.ProcessInfo(pid)\",\n \"def create_node(self, **kwargs):\\n size = kwargs['size'].ram\\n params = {\\n 'cmd' : 'dreamhost_ps-add_ps',\\n 'movedata' : kwargs.get('movedata', 'no'),\\n 'type' : kwargs['image'].name,\\n 'size' : size\\n }\\n data = self.connection.request('/', params).object\\n return Node(\\n id = data['added_web'],\\n name = data['added_web'],\\n state = NodeState.PENDING,\\n public_ip = [],\\n private_ip = [],\\n driver = self.connection.driver,\\n extra = {\\n 'type' : kwargs['image'].name\\n }\\n )\",\n \"def _instantiate_processes(self, input=None, context=None):\\n# FIX: ALLOW Projections (??ProjectionTiming TUPLES) TO BE INTERPOSED BETWEEN MECHANISMS IN PATHWAY\\n# FIX: AUGMENT LinearMatrix TO USE FULL_CONNECTIVITY_MATRIX IF len(sender) != len(receiver)\\n\\n # # MODIFIED 2/8/17 OLD: [SEE BELOW]\\n # self.variable = []\\n # MODIFIED 2/8/17 END\\n self.mechanismsDict = {}\\n self._all_mech_tuples = []\\n self._allMechanisms = MechanismList(self, self._all_mech_tuples)\\n\\n # Get list of processes specified in arg to init, possibly appended by EVCMechanism (with prediction processes)\\n processes_spec = self.processes\\n\\n # Assign default Process if PROCESS is empty, or invalid\\n if not processes_spec:\\n from PsyNeuLink.Components.Process import Process_Base\\n processes_spec.append(ProcessTuple(Process_Base(), None))\\n\\n # If input to system is specified, number of items must equal number of processes with origin mechanisms\\n if input is not None and len(input) != len(self.originMechanisms):\\n raise SystemError(\\\"Number of items in input ({}) must equal number of processes ({}) in {} \\\".\\n format(len(input), len(self.originMechanisms),self.name))\\n\\n #region VALIDATE EACH ENTRY, STANDARDIZE FORMAT AND INSTANTIATE PROCESS\\n\\n # Convert all entries to (process, input) tuples, with None as filler for absent input\\n input_index = input_index_curr = 0\\n for i in range(len(processes_spec)):\\n\\n # MODIFIED 2/8/17 NEW:\\n # Get list of origin mechanisms for processes that have already been converted\\n # (for use below in assigning input)\\n orig_mechs_already_processed = list(p[0].originMechanisms[0] for\\n p in processes_spec if isinstance(p,ProcessTuple))\\n # MODIFIED 2/8/17 END\\n\\n # Entry is not a tuple\\n # presumably it is a process spec, so enter it as first item of ProcessTuple\\n if not isinstance(processes_spec[i], tuple):\\n processes_spec[i] = ProcessTuple(processes_spec[i], None)\\n\\n # Entry is a tuple but not a ProcessTuple, so convert it\\n if isinstance(processes_spec[i], tuple) and not isinstance(processes_spec[i], ProcessTuple):\\n processes_spec[i] = ProcessTuple(processes_spec[i][0], processes_spec[i][1])\\n\\n # Input was NOT provided on command line, so get it from the process\\n if input is None:\\n process = processes_spec[i].process\\n process_input = []\\n for process_input_state in process.processInputStates:\\n process_input.extend(process_input_state.value)\\n processes_spec[i] = ProcessTuple(process, process_input)\\n # Input was provided on command line, so assign that to input item of tuple\\n else:\\n # Assign None as input to processes implemented by controller (controller provides their input)\\n # (e.g., prediction processes implemented by EVCMechanism)\\n if processes_spec[i].process._isControllerProcess:\\n processes_spec[i] = ProcessTuple(processes_spec[i].process, None)\\n else:\\n # MODIFIED 2/8/17 NEW:\\n # Replace input item in tuple with one from command line\\n # Note: check if origin mechanism for current process is same as any previous one;\\n # if it is, use that one (and don't increment index for input\\n # otherwise, assign input and increment input_index\\n try:\\n input_index_curr = orig_mechs_already_processed.index(processes_spec[i][0].originMechanisms[0])\\n except ValueError:\\n input_index += 1\\n processes_spec[i] = ProcessTuple(processes_spec[i].process, input[input_index_curr])\\n input_index_curr = input_index\\n # MODIFIED 2/8/17 END\\n\\n # Validate input\\n if (processes_spec[i].input is not None and\\n not isinstance(processes_spec[i].input,(numbers.Number, list, np.ndarray))):\\n raise SystemError(\\\"Second item of entry {0} ({1}) must be an input value\\\".\\n format(i, processes_spec[i].input))\\n\\n process = processes_spec[i].process\\n process_input = processes_spec[i].input\\n\\n # # MODIFIED 2/8/17 OLD: [MOVED ASSIGNMENT OF self.variable TO _instantiate_graph()\\n # # SINCE THAT IS WHERE SYSTEM'S ORIGIN MECHANISMS ARE IDENTIFIED]\\n # self.variable.append(process_input)\\n # # MODIFIED 2/8/17 END\\n\\n # IMPLEMENT: THIS IS WHERE LEARNING SPECIFIED FOR A SYSTEM SHOULD BE IMPLEMENTED FOR EACH PROCESS IN THE\\n # SYSTEM; NOTE: IF THE PROCESS IS ALREADY INSTANTIATED WITHOUT LEARNING\\n # (FIRST CONDITIONAL BELOW), MAY NEED TO BE RE-INSTANTIATED WITH LEARNING\\n # (QUESTION: WHERE TO GET SPECS FOR PROCESS FOR RE-INSTANTIATION??)\\n\\n # If process item is a Process object, assign process_input as default\\n if isinstance(process, Process):\\n if process_input is not None:\\n process._assign_defaults(variable=process_input, context=context)\\n # If learning_rate is specified for system but not for process, then apply to process\\n # # MODIFIED 3/21/17 OLD:\\n # if self.learning_rate and not process.learning_rate:\\n # # FIX: assign_params WANTS TO CREATE A ParamaterState ON process FOR learning_rate\\n # process.assign_params(request_set={LEARNING_RATE:self.learning_rate})\\n # # MODIFIED 3/21/17 NEW:[learning_rate SHOULD BE NOT BE RE-ASSIGNED FOR PROCESS, BUT RATHER ON EXECUTE]\\n # if self.learning_rate is not None and process.learning_rate is None:\\n # process.learning_rate = self.learning_rate\\n # # MODIFIED 3/21/17 END\\n\\n # Otherwise, instantiate Process\\n else:\\n if inspect.isclass(process) and issubclass(process, Process):\\n # FIX: MAKE SURE THIS IS CORRECT\\n # Provide self as context, so that Process knows it is part of a System (and which one)\\n # Note: this is used by Process._instantiate_pathway() when instantiating first Mechanism\\n # in Pathway, to override instantiation of projections from Process.input_state\\n process = Process(default_input_value=process_input,\\n learning_rate=self.learning_rate,\\n context=self)\\n elif isinstance(process, dict):\\n # IMPLEMENT: HANDLE Process specification dict here;\\n # include process_input as ??param, and context=self\\n raise SystemError(\\\"Attempt to instantiate process {0} in kwProcesses of {1} \\\"\\n \\\"using a Process specification dict: not currently supported\\\".\\n format(process.name, self.name))\\n else:\\n raise SystemError(\\\"Entry {0} of kwProcesses ({1}) must be a Process object, class, or a \\\"\\n \\\"specification dict for a Process\\\".format(i, process))\\n\\n # # process should now be a Process object; assign to processList\\n # self.processList.append(process)\\n\\n # Assign the Process a reference to this System\\n process.systems.append(self)\\n if process.learning:\\n self.learning = True\\n\\n # Get max of Process phaseSpecs\\n self._phaseSpecMax = int(max(math.floor(process._phaseSpecMax), self._phaseSpecMax))\\n\\n # Iterate through mechanism tuples in Process' mech_tuples\\n # to construct self._all_mech_tuples and mechanismsDict\\n # FIX: ??REPLACE WITH: for sender_mech_tuple in Process._mech_tuples\\n for sender_mech_tuple in process._mech_tuples:\\n\\n sender_mech = sender_mech_tuple.mechanism\\n\\n # THIS IS NOW DONE IN _instantiate_graph\\n # # Add system to the Mechanism's list of systems of which it is member\\n # if not self in sender_mech_tuple[MECHANISM].systems:\\n # sender_mech.systems[self] = INTERNAL\\n\\n # Assign sender mechanism entry in self.mechanismsDict, with mech_tuple as key and its Process as value\\n # (this is used by Process._instantiate_pathway() to determine if Process is part of System)\\n # If the sender is already in the System's mechanisms dict\\n if sender_mech_tuple.mechanism in self.mechanismsDict:\\n existing_mech_tuple = self._allMechanisms._get_tuple_for_mech(sender_mech)\\n if not sender_mech_tuple is existing_mech_tuple:\\n # Contents of tuple are the same, so use the tuple in _allMechanisms\\n if (sender_mech_tuple.phase == existing_mech_tuple.phase and\\n sender_mech_tuple.params == existing_mech_tuple.params):\\n pass\\n # Contents of tuple are different, so raise exception\\n else:\\n if sender_mech_tuple.phase != existing_mech_tuple.phase:\\n offending_tuple_field = 'phase'\\n offending_value = PHASE_ITEM\\n else:\\n offending_tuple_field = 'process_input'\\n offending_value = PARAMS_ITEM\\n raise SystemError(\\\"The same mechanism in different processes must have the same parameters:\\\"\\n \\\"the {} ({}) for {} in {} does not match the value({}) in {}\\\".\\n format(offending_tuple_field,\\n sender_mech_tuple.mechanism,\\n sender_mech_tuple[offending_value],\\n process,\\n existing_mech_tuple[offending_value],\\n self.mechanismsDict[sender_mech_tuple.mechanism]\\n ))\\n # Add to entry's list\\n self.mechanismsDict[sender_mech].append(process)\\n else:\\n # Add new entry\\n self.mechanismsDict[sender_mech] = [process]\\n if not sender_mech_tuple in self._all_mech_tuples:\\n self._all_mech_tuples.append(sender_mech_tuple)\\n\\n process._allMechanisms = MechanismList(process, tuples_list=process._mech_tuples)\\n\\n # # MODIFIED 2/8/17 OLD: [SEE ABOVE]\\n # self.variable = convert_to_np_array(self.variable, 2)\\n # # MODIFIED 2/8/17 END\\n #\\n # # Instantiate processList using process_tuples, and point self.processes to it\\n # # Note: this also points self.params[kwProcesses] to self.processes\\n self.process_tuples = processes_spec\\n self._processList = ProcessList(self, self.process_tuples)\\n self.processes = self._processList.processes\",\n \"async def add_process(self, ctx, process, name):\\n name = self.fix_emoji_escapes(name)\\n if process in PROCESSES.keys():\\n await ctx.send(f\\\"The process {process} is already being displayed\\\")\\n elif name in PROCESSES.values():\\n await ctx.send(f\\\"The process name {name} is already being displayed\\\")\\n\\n else:\\n PROCESSES[process] = name\\n self.update_processes_config()\\n await ctx.send(f\\\"The process {name} has been added\\\")\",\n \"def add_child(self, m, p):\\n\\t\\tn = Node(move=m, parent=self, player_just_moved=p)\\n\\t\\tself.child_nodes.append(n)\\n\\t\\treturn n\",\n \"def add_node(self, nnode, value):\\n new_node = Node(nnode, value)\\n self.vert_dict[nnode] = new_node\\n return new_node\",\n \"def _create_process(self, process, name):\\n def _run():\\n try:\\n while True:\\n process.loop()\\n except KeyboardInterrupt:\\n pass\\n except:\\n self._logger.exception('Process %s died!', name)\\n return ProcessEnvironment().create_process(_run, name)\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n with tf.gfile.FastGFile(sys.argv[1], 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def create_vm(self, account, vminfo):\\n node = self.driver(account).ex_create_node_from_template(\\n name=vminfo.vm_name,\\n template=vminfo.vsphere.template,\\n )\\n\\n vminfo.vm_id = node.id\\n\\n return node.id\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n with tf.gfile.FastGFile(os.path.join(\\n FLAGS.model_dir, pbfilename), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def _spawn_service_process(self, process_id, name, module, cls, config, proc_attr):\\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\\n\\n listen_name = get_safe(config, \\\"process.listen_name\\\") or process_instance.name\\n log.debug(\\\"Service Process (%s) listen_name: %s\\\", name, listen_name)\\n process_instance._proc_listen_name = listen_name\\n\\n # Service RPC endpoint\\n rsvc1 = self._create_listening_endpoint(node=self.container.node,\\n from_name=listen_name,\\n process=process_instance)\\n # Named local RPC endpoint\\n rsvc2 = self._create_listening_endpoint(node=self.container.node,\\n from_name=process_instance.id,\\n process=process_instance)\\n\\n # cleanup method to delete process queue\\n cleanup = lambda _: self._cleanup_method(process_instance.id, rsvc2)\\n\\n # Start an ION process with the right kind of endpoint factory\\n proc = self.proc_sup.spawn(name=process_instance.id,\\n service=process_instance,\\n listeners=[rsvc1, rsvc2],\\n proc_name=process_instance._proc_name,\\n cleanup_method=cleanup)\\n proc.proc._glname = \\\"ION Proc %s\\\" % process_instance._proc_name\\n self.proc_sup.ensure_ready(proc, \\\"_spawn_service_process for %s\\\" % \\\",\\\".join((listen_name, process_instance.id)))\\n\\n # map gproc to process_instance\\n self._spawned_proc_to_process[proc.proc] = process_instance\\n\\n # set service's reference to process\\n process_instance._process = proc\\n\\n self._process_init(process_instance)\\n self._process_start(process_instance)\\n\\n try:\\n proc.start_listeners()\\n except IonProcessError:\\n self._process_quit(process_instance)\\n self._call_proc_state_changed(process_instance, ProcessStateEnum.FAILED)\\n raise\\n\\n return process_instance\",\n \"def add_node(graph, node_name, label, shape='record', style='filled', fillcolor='lightgrey'):\\n node = Node(name=node_name, shape=shape, style=style, fillcolor=fillcolor, label=label)\\n graph.add_node(node)\\n return node\",\n \"def appendProcess(self, pid: int, numberOfVariables: int, processTable, diagnostics) -> int:\\n self.memory[pid] = []\\n\\n for _i in range(numberOfVariables):\\n self.memory[pid].append(MemoryItem())\\n\\n return 0\",\n \"def _addEntity(self, pid, chunks):\\n parent = chunks[pid]\\n sub = None\\n # Find subject\\n for i in range(len(parent.children)):\\n child = chunks[parent.children[i]]\\n if child.func in SubDict:\\n sub = child\\n if child.func == \\\"では\\\":\\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\\n pass\\n else:\\n sub = None\\n if sub:\\n self._addNode(parent, sub=sub.main)\\n self._addEdge(sub.main, parent.main, label=\\\"陳述\\\", etype=\\\"stat\\\")\\n else:\\n self._addNode(parent)\\n \\n # Lopp through all children\\n for i in range(len(parent.children)):\\n child = chunks[parent.children[i]]\\n # If child is noun\\n if child.func in SubDict:\\n if child.func == \\\"では\\\":\\n if child.negative != 0 or any([val.negative != 0 for key, val in self.G.successors(child.main)]):\\n pass\\n else:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=child.func, etype=\\\"attr\\\")\\n elif child.type == 0 and child.func in [\\\"と\\\", \\\"などと\\\"] and child.id + 1 == parent.id and preprocessText(chunks[parent.parent].main) not in [\\\"交代\\\", \\\"交換\\\"]:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self._addEdge(parent.main, child.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self.para.append([child.main, parent.main])\\n elif child.type == 0 and child.func in ParallelDict and child.id + 1 == parent.id:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self._addEdge(parent.main, child.main, label=\\\"並列\\\", etype=\\\"para\\\")\\n self.para.append([child.main, parent.main])\\n else:\\n self._addNode(child)\\n self._addEdge(child.main, parent.main, label=child.func, etype=\\\"attr\\\")\",\n \"def insert(self, pid, pname, pparent, pobj, ptype):\\r\\n self.pids.append(pid)\\r\\n self.pnames.append(pname)\\r\\n self.pparents.append(pparent)\\r\\n self.ptypes.append(ptype)\\r\\n self.pobjs.append(pobj)\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n\\n # with tf.gfile.FastGFile(os.path.join(\\n # FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:\\n with tf.gfile.FastGFile(os.path.join(\\n FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def insert_node(self, node_tup):\\n signature = hashlib.sha256((node_tup[0]+node_tup[4]).encode('utf-8')).hexdigest()\\n app_process = sqlite3.connect('app_process::memory:', check_same_thread=False)\\n app_process_cursor = app_process.cursor()\\n app_process_cursor.execute(\\\"INSERT INTO nodes VALUES (:ip, :port, uname, :signature)\\\", {\\\"ip\\\":node_tup[0], \\\"port\\\":node_tup[1], \\\"uname\\\":node_tup[2], \\\"verifying_key\\\":node_tup[3]})\\n app_process.commit()\\n app_process.close()\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n with tf.gfile.FastGFile(os.path.join(\\n MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def addNode(self, nTag, pkg, exe, args, name, namespace):\\r\\n try:\\r\\n validateName(nTag)\\r\\n except IllegalName:\\r\\n raise InvalidRequest('Node tag is not a valid.')\\r\\n\\r\\n if nTag in self._nodes:\\r\\n raise InvalidRequest(\\\"Can not use the same node tag '{0}' in the \\\"\\r\\n 'same container twice.'.format(nTag))\\r\\n\\r\\n node = self._obj.createNode(pkg, exe, args, name, namespace)\\r\\n self._nodes[nTag] = node\\r\\n node.notifyOnDeath(self._nodeDied)\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n with gfile.FastGFile(os.path.join(\\n FLAGS.model_dir, 'classify_image_graph_def.pb'), 'r') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def create_graph():\\n # Creates graph from saved graph_def.pb.\\n with tf.gfile.FastGFile(os.path.join(\\n FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def create(self):\\n\\n self.init_nodes_db()\\n\\n # group data\\n if len(self.depends) == 0:\\n grouped = [((), self.data)]\\n else:\\n grouped = self.data.groupby(self.depends)\\n\\n # create all the pymc nodes\\n for uniq_elem, grouped_data in grouped:\\n if not isinstance(uniq_elem, tuple):\\n uniq_elem = (uniq_elem,)\\n\\n # create new kwargs to pass to the new pymc node\\n kwargs = self.kwargs.copy()\\n\\n # update kwarg with the right parent\\n for name, parent in self.parents.items():\\n kwargs[name] = parent.get_node(self.depends, uniq_elem)\\n\\n # get node name\\n tag, subj_idx = self.create_tag_and_subj_idx(self.depends, uniq_elem)\\n node_name = self.create_node_name(tag, subj_idx=subj_idx)\\n\\n # get value for observed node\\n if self.observed:\\n if self.pass_dataframe:\\n kwargs[\\\"value\\\"] = grouped_data[\\n self.col_name\\n ] # .to_records(index=False)\\n else:\\n kwargs[\\\"value\\\"] = grouped_data[\\n self.col_name\\n ].values # .to_records(index=False)\\n\\n # Deterministic nodes require a parent argument that is a\\n # dict mapping parent names to parent nodes. Knode wraps\\n # this; so here we have to fish out the parent nodes from\\n # kwargs, put them into a parent dict and put that back\\n # into kwargs, which will make pm.Determinstic() get a\\n # parent dict as an argument.\\n if self.pymc_node is pm.Deterministic:\\n parents_dict = {}\\n for name, parent in self.parents.items():\\n parents_dict[name] = parent.get_node(self.depends, uniq_elem)\\n kwargs.pop(name)\\n kwargs[\\\"parents\\\"] = parents_dict\\n\\n if self.observed:\\n kwargs[\\\"parents\\\"][\\\"value\\\"] = kwargs[\\\"value\\\"]\\n\\n # Deterministic nodes require a doc kwarg, we don't really\\n # need that so if its not supplied, just use the name\\n if self.pymc_node is pm.Deterministic and \\\"doc\\\" not in kwargs:\\n kwargs[\\\"doc\\\"] = node_name\\n\\n node = self.create_node(node_name, kwargs, grouped_data)\\n\\n if node is not None:\\n self.nodes[uniq_elem] = node\\n self.append_node_to_db(node, uniq_elem)\",\n \"def create(self, context=None):\\n values = self.obj_get_changes()\\n db_host = self.dbapi.host_create(context, values)\\n self._from_db_object(context, self, db_host)\",\n \"def add_node(self, node: Node):\\n prop_str = \\\",\\\\n\\\".join([\\\"n.%s = '%s'\\\" % (k, v) for k, v in node.data.items()])\\n query = \\\"\\\"\\\"\\n MERGE (n:%s {id: '%s'})\\n SET %s\\n \\\"\\\"\\\" % (\\n node.labels,\\n norm_id(node.db_ns, node.db_id),\\n prop_str,\\n )\\n return self.create_tx(query)\",\n \"def to_networkx(self):\\n import networkx as nx\\n G = nx.DiGraph()\\n # G.graph.update(self.config)\\n\\n if nx.__version__.startswith('1'):\\n node_dict = G.node\\n else:\\n node_dict = G.nodes\\n\\n def _defaultstyle(node, color, shape='none', **kwargs):\\n node_dict[node]['fillcolor'] = color\\n node_dict[node]['style'] = 'filled'\\n node_dict[node]['shape'] = shape\\n node_dict[node].update(kwargs)\\n # node_dict[node]['color'] = color\\n\\n # Add all processes\\n # Make inputs and outputs nodes to prevent needing a multigraph\\n for proc in self.procs.values():\\n G.add_node(proc.name)\\n _defaultstyle(proc.name, 'turquoise', shape='ellipse', fontsize=20)\\n node_dict[proc.name]['label'] = '{}:\\\\n{}'.format(proc.name,\\n proc.type)\\n\\n for iport in proc.iports.values():\\n iport_name = iport.absname()\\n G.add_node(iport_name)\\n G.add_edge(iport_name, proc.name)\\n node_dict[iport_name]['label'] = iport.name\\n _defaultstyle(iport_name, '#fefefe', fontsize=14)\\n\\n for oport in proc.oports.values():\\n oport_name = oport.absname()\\n G.add_node(oport_name)\\n G.add_edge(proc.name, oport_name)\\n node_dict[oport_name]['label'] = oport.name\\n _defaultstyle(oport_name, '#f0f0f0', fontsize=14)\\n\\n # Add all connections\\n for proc in self.procs.values():\\n for iport in proc.iports.values():\\n iport_name = iport.absname()\\n for oport in iport.connections:\\n if oport is not None:\\n oport_name = oport.absname()\\n G.add_edge(oport_name, iport_name)\\n return G\",\n \"def add_process(self):\\r\\n\\r\\n proc_dict = dict()\\r\\n total_count = len(self.newest_connections['pid'].unique())\\r\\n count = 0\\r\\n for proc in self.newest_connections['pid'].unique():\\r\\n count += 1\\r\\n percent = round((count / total_count * 100))\\r\\n print('{}{}Identifying processes in progress. Accomplished: {}%{}'.format(Colors.GREEN,Colors.BOLD,percent,Colors.END), end='\\\\r')\\r\\n output = subprocess.run([\\\"powershell.exe\\\", \\\"-Command\\\", f'Get-Process -Id {proc} | select-object -Property ProcessName | ft -HideTableHeaders'], capture_output=True, text=True).stdout.strip()\\r\\n proc_dict[proc] = output\\r\\n print()\\r\\n processes = pd.Series(proc_dict)\\r\\n processes_df = pd.DataFrame(processes.reset_index())\\r\\n processes_df.columns = ['pid', 'process_name']\\r\\n if 'process_name' in self.newest_connections:\\r\\n self.newest_connections = pd.merge(self.newest_connections, processes_df, on=['pid', 'process_name'], how='right')\\r\\n else:\\r\\n self.newest_connections = pd.merge(self.newest_connections, processes_df, on='pid', how='right')\\r\\n return self.newest_connections\",\n \"def addOnCreate(call, args=(), kwargs={}, nodeClass='*'):\\n pass\",\n \"def add_node(self, metadata, pos):\\n node = Node(metadata, pos)\\n self.addItem(node)\\n self.nodes[node.id] = node\\n return node\",\n \"def __init__(self, pid, binary_path, host_name, node_name, telemetry):\\n self.pid = pid\\n self.binary_path = binary_path\\n self.host_name = host_name\\n self.node_name = node_name\\n self.telemetry = telemetry\",\n \"def make_comms(self,comm):\\n # For masters we let child_comm be the communicator used to message the node's \\n # children, and parent_comm be that used to message the node's parents.\\n\\n parent_rank = 0\\n\\n # Case (1)\\n if self.num_masters > 1:\\n self.make_comms_many(comm)\\n if self.is_master:\\n parent_comm = self.comm_masters\\n if self.comm_masters.Get_rank() == 0: # rank 0 is the super-master\\n child_comm = self.comm_masters\\n parent_rank = None\\n else:\\n child_comm = self.comm_block\\n # Case (2)\\n else:\\n self.make_comm_single(comm)\\n if self.is_master:\\n parent_comm = self.comm_block\\n child_comm = self.comm_block\\n parent_rank = None\\n\\n # Process initialization\\n from .MPIProcess import MPIWorker, MPIMaster\\n if self.is_master:\\n self.set_val_data()\\n num_sync_workers = self.get_num_sync_workers(child_comm)\\n self.process = MPIMaster( parent_comm, parent_rank=parent_rank, \\n data=self.data, child_comm=child_comm, num_epochs=self.num_epochs,\\n num_sync_workers=num_sync_workers, callbacks=self.callbacks )\\n else:\\n self.set_train_data()\\n self.process = MPIWorker( parent_comm=self.comm_block, parent_rank=parent_rank, \\n num_epochs=self.num_epochs, data=self.data, callbacks=self.callbacks )\",\n \"def create_nodes(self, topogramId, nodes):\\n assert type(nodes) is list\\n return self.make_request(\\\"POST\\\", \\\"nodes\\\", { \\\"topogramId\\\" : topogramId, \\\"nodes\\\" : nodes})\",\n \"def do_create(self, line):\\n if line:\\n if line in HBNBCommand.classes:\\n class_to_ins = HBNBCommand.classes.get(line)\\n new_instance = class_to_ins()\\n new_instance.save()\\n print(new_instance.id)\\n else:\\n print(\\\"** class name missing **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"def _spawn_immediate_process(self, process_id, name, module, cls, config, proc_attr):\\n process_instance = self._create_process_instance(process_id, name, module, cls, config, proc_attr)\\n self._process_init(process_instance)\\n self._process_start(process_instance)\\n return process_instance\",\n \"def create(self, substrate, comment=''):\\n process_tree = ProcessNode.objects.create(process=None, piece='a')\\n sample = self.model(substrate=substrate, comment=comment,\\n process_tree=process_tree)\\n sample.save()\\n\\n return sample\",\n \"def create_process(\\n target: typing.Callable, args: tuple = (), prepend_lock: bool = False\\n) -> Process:\\n if prepend_lock:\\n args = (LOCK,) + tuple(args)\\n process = multiprocessing.Process(target=target, args=args)\\n return process\",\n \"def create_graph():\\n with tf.gfile.FastGFile(os.path.join(\\n config['inference']['model_dir'], 'output_graph.pb'), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def add_node(self, properties=None, **kwargs):\\r\\n\\t\\tif properties is None:\\r\\n\\t\\t\\tproperties={}\\r\\n\\t\\t# may change method sig of Node since we can always combine arguments\\r\\n\\t\\t# here\\r\\n\\t\\tnode = Node(self._nextid, properties, **kwargs)\\r\\n\\t\\tself._nodes[self._nextid] = node\\r\\n\\t\\tself._nextid += 1\\r\\n\\t\\treturn node\",\n \"def create_entity(self):\\n \\n if self.ORION_CB.get_entity(self.params['name']) is None:\\n \\n print('[INFO]: Create new PID entity')\\n \\n entity_dict = {\\\"id\\\":self.params['name'], \\\"type\\\":'PID_controller'}\\n for attr in ['Kp', 'Ti', 'Td', 'lim_low', 'lim_high', 'setpoint']:\\n entity_dict.update({attr:{'value':self.params[attr],'type':'Number'}})\\n\\n entity_dict.update({'reverse_act':{'value':self.params['reverse_act'],'type':'Text'}})\\n \\n entity = filip.orion.Entity(entity_dict)#, attrs)\\n\\n self.ORION_CB.post_entity(entity)\\n \\n else:\\n print('Entity name already assigned')\",\n \"def add_master_process(self, value=u\\\"on\\\"):\\n path = [u\\\"master_process\\\"]\\n self.add_config_item(self._nodeconfig, value, path)\",\n \"def install_node_instance_subgraph(ctx,instance, graph, hist=None):\\n subgraph = graph.subgraph('install_{0}'.format(instance.id))\\n\\n ct=None\\n if hist:\\n #get completed tasks for instance\\n ct=_completed_tasks(ctx,hist,instance.id)\\n\\n sequence = subgraph.sequence()\\n\\n #CREATE\\n run=True\\n if(hist and 'create' in ct):\\n run=False\\n\\n ctx.logger.info(\\\"run={} CREATE {}\\\".format(str(run),instance.id))\\n if(run):\\n ctx.logger.info(\\\" hist={} ct={}\\\".format(str(hist),str(ct)))\\n\\n if(run):\\n sequence.add(\\n instance.set_state('initializing'),\\n forkjoin(instance.send_event('Creating node'),\\n instance.set_state('creating')),\\n _add_es_log(ctx,instance,'create',instance.execute_operation('cloudify.interfaces.lifecycle.create')),\\n instance.set_state('created'),\\n forkjoin(*_relationships_operations(\\n instance,\\n 'cloudify.interfaces.relationship_lifecycle.preconfigure'\\n )))\\n\\n #CONFIGURE\\n run=True\\n if(hist and 'configure' in ct):\\n run=False\\n\\n ctx.logger.info(\\\"run={} CONFIGURE {}\\\".format(str(run),instance.id))\\n\\n if(run):\\n sequence.add(\\n forkjoin(instance.set_state('configuring'),\\n instance.send_event('Configuring node')),\\n _add_es_log(ctx,instance,'configure',instance.execute_operation('cloudify.interfaces.lifecycle.configure')),\\n instance.set_state('configured'),\\n forkjoin(*_relationships_operations(\\n instance,\\n 'cloudify.interfaces.relationship_lifecycle.postconfigure'\\n )))\\n\\n # STARTING\\n run=True\\n if(hist and 'start' in ct):\\n run=False\\n\\n ctx.logger.info(\\\"run={} START {}\\\".format(str(run),instance.id))\\n\\n if(run):\\n sequence.add(\\n forkjoin(instance.set_state('starting'),\\n instance.send_event('Starting node')),\\n instance.execute_operation('cloudify.interfaces.lifecycle.start'))\\n\\n # If this is a host node, we need to add specific host start\\n # tasks such as waiting for it to start and installing the agent\\n # worker (if necessary)\\n if run and is_host_node(instance):\\n sequence.add(*_host_post_start(instance))\\n\\n sequence.add(\\n forkjoin(\\n _add_es_log(ctx,instance,'start',instance.execute_operation('cloudify.interfaces.monitoring.start')),\\n *_relationships_operations(\\n instance,\\n 'cloudify.interfaces.relationship_lifecycle.establish'\\n )),\\n instance.set_state('started'))\\n\\n subgraph.on_failure = get_install_subgraph_on_failure_handler(ctx,instance)\\n return subgraph\",\n \"def gdb_add_node(node, gdb, rdf, owl):\\n gdb_node = gdb.nodes.create()\\n node.set_node(gdb_node)\\n gdb_node.labels.add([label.split('#')[-1] for label in node.get_labels()])\\n for _, pro, obj in rdf.triples((node.get_uri(), None, None)):\\n if (pro, RDF.type, owl.DatatypeProperty) in rdf:\\n prop_name = pro.split('#')[-1]\\n value = obj.split('#')[-1]\\n gdb_node.set(prop_name, value)\",\n \"def node_create(self, parent, path):\\n\\n q = (\\\"insert into nodes (parent, path) \\\"\\n \\\"values (?, ?)\\\")\\n props = (parent, path)\\n return self.execute(q, props).lastrowid\",\n \"def _create_node(self, index: int, name: str, external_id: Optional[str] = None) -> Node:\\r\\n return Node(graph=self._graph, index=index, name=name, external_id=external_id)\",\n \"def __init__(self, operator_id: str=None, process_id: str=None): # noqa: E501\\n self.swagger_types = {\\n 'operator_id': str,\\n 'process_id': str\\n }\\n\\n self.attribute_map = {\\n 'operator_id': 'operatorId',\\n 'process_id': 'processId'\\n }\\n self._operator_id = operator_id\\n self._process_id = process_id\",\n \"def apply(tree, parameters=None):\\r\\n from pm4py.objects.bpmn.bpmn_graph import BPMN\\r\\n counts = Counts()\\r\\n bpmn = BPMN()\\r\\n start_event = BPMN.StartEvent(name=\\\"start\\\", isInterrupting=True)\\r\\n end_event = BPMN.EndEvent(name=\\\"end\\\")\\r\\n bpmn.add_node(start_event)\\r\\n bpmn.add_node(end_event)\\r\\n bpmn, counts, _, _ = recursively_add_tree(tree, tree, bpmn, start_event, end_event, counts, 0)\\r\\n bpmn = delete_tau_transitions(bpmn, counts)\\r\\n\\r\\n return bpmn\",\n \"def create_graph():\\n\\t# Creates graph from saved graph_def.pb.\\n\\twith tf.gfile.FastGFile('./models/inception_v4.pb', 'rb') as f:\\n\\t\\tgraph_def = tf.GraphDef()\\n\\t\\tgraph_def.ParseFromString(f.read())\\n\\t\\t_ = tf.import_graph_def(graph_def, name='')\",\n \"def make_node(self, node_property):\\n # Try except because Ubigraph is old as hell!\\n try: n = self.G.new_vertex()\\n except: pass\\n for prop, val in node_property.items():\\n try: self.G.set_vertex_attribute(n, prop, val)\\n except: return make_node(node_property)\\n return n\",\n \"def create_graph():\\n with gfile.FastGFile(os.path.join(\\n FLAGS.model_dir, FLAGS.model_name), 'rb') as f:\\n graph_def = tf.GraphDef()\\n graph_def.ParseFromString(f.read())\\n _ = tf.import_graph_def(graph_def, name='')\",\n \"def test_getStateIncludesProcesses(self):\\r\\n self.pm.addProcess(\\\"foo\\\", [\\\"arg1\\\", \\\"arg2\\\"],\\r\\n uid=1, gid=2, env={})\\r\\n self.assertEqual(self.pm.__getstate__()['processes'],\\r\\n {'foo': (['arg1', 'arg2'], 1, 2, {})})\",\n \"def create(cls, user, pool, run_name, experiment, sequencer,\\n fwd_cycles, rev_cycles, assay, principal_investigator,\\n lanes=None, contacts=None):\\n with sql_connection.TRN as TRN:\\n # Add the row to the process table\\n process_id = cls._common_creation_steps(user)\\n\\n if fwd_cycles <= 0 or not isinstance(fwd_cycles, int):\\n raise ValueError(\\\"fwd_cycles must be > 0\\\")\\n if rev_cycles <= 0 or not isinstance(rev_cycles, int):\\n raise ValueError(\\\"rev_cycles must be > 0\\\")\\n\\n # Add the row to the sequencing table\\n sql = \\\"\\\"\\\"INSERT INTO qiita.sequencing_process\\n (process_id, pool_composition_id, run_name, experiment,\\n sequencer_id, fwd_cycles, rev_cycles, assay,\\n principal_investigator, lanes)\\n VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\\n RETURNING sequencing_process_id\\\"\\\"\\\"\\n TRN.add(sql, [process_id, pool.id, run_name, experiment,\\n sequencer.id, fwd_cycles, rev_cycles, assay,\\n principal_investigator.id, dumps(lanes)])\\n instance = cls(TRN.execute_fetchlast())\\n\\n if contacts:\\n sql = \\\"\\\"\\\"INSERT INTO qiita.sequencing_process_contacts\\n (sequencing_process_id, contact_id)\\n VALUES (%s, %s)\\\"\\\"\\\"\\n sql_args = [[instance.id, c.id] for c in contacts]\\n TRN.add(sql, sql_args, many=True)\\n TRN.execute()\\n\\n return instance\",\n \"def create(cls, original_args, process_args, base_url, host_url, services):\\n return cls(original_args, process_args, base_url, host_url, services)\",\n \"def convert(cls, node_entry, model_container, node_dict):\\n name = node_entry[\\\"name\\\"]\\n shape = numpy.asarray(\\n [a.value for a in node_entry[\\\"relay_node\\\"].attrs.newshape], dtype=numpy.int64\\n )\\n\\n input_names = [\\n node_entry[\\\"input_names\\\"][0],\\n add_input(shape, name, \\\"shape\\\", model_container),\\n ]\\n\\n node = onnx.helper.make_node(cls.__name__, input_names, node_entry[\\\"output_names\\\"])\\n model_container.add_nodes([node])\",\n \"def start_process(check_id, storage, processes):\\n\\n process = Process(target=perform_check, args=(check_id,\\n storage[check_id]['command'],\\n storage[check_id]['freq'],))\\n\\n # Set process name to \\\"Process+check_id\\\"\\n process.name = 'Process{}'.format(check_id)\\n process.start()\\n\\n # Add process to processes dict with key=pid and value=processname\\n processes[process.pid] = process\\n storage[check_id]['pid'] = process.pid\",\n \"def add_node(self, node: base.Node, label: str = None) -> HandleType:\\n\\n if self._current_group:\\n if label and label != self._current_group:\\n raise ValueError('The given label does not match the current group: '\\n f'{label} vs {self._current_group}.')\\n label = self._current_group\\n else:\\n if not label:\\n raise ValueError('Label should not be empty.')\\n if label not in self._groups:\\n self._groups[label] = [node]\\n else:\\n self._groups[label].append(node)\\n return node.create_handle()\",\n \"def _internal_add_node(self,\\r\\n node_name: str,\\r\\n external_id: Optional[str] = None,\\r\\n are_neighbors_cached: bool = False,\\r\\n add_to_cache: bool = False) -> None:\\r\\n index: int = len(self)\\r\\n node: Node = self._create_node(index, node_name, external_id)\\r\\n node.are_neighbors_cached = are_neighbors_cached\\r\\n self._nodes[index] = node\\r\\n self._node_name_map[node_name] = node\\r\\n\\r\\n if add_to_cache:\\r\\n db: GraphDatabaseInterface = self._graph.database\\r\\n db_node: DBNode = db.Node.find_by_name(node.name)\\r\\n if db_node is None:\\r\\n db_node = db.Node(node.name, node.external_id)\\r\\n db_node.are_neighbors_cached = False\\r\\n db.session.add(db_node)\\r\\n db.session.commit()\",\n \"def create_node(self, *labels, **props):\\n component = Node(*labels, **props)\\n self.graph.create(component)\\n return component\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6773554","0.64495486","0.60057384","0.5933061","0.5871152","0.58102006","0.5744983","0.57229316","0.5719331","0.56770027","0.5667292","0.5577523","0.5570289","0.55650806","0.55393213","0.55308384","0.5441535","0.5393277","0.5389408","0.5335206","0.5284067","0.52781135","0.5264026","0.5255529","0.51701474","0.511998","0.5096278","0.50743943","0.506606","0.5064148","0.50633234","0.5059192","0.5045069","0.50444794","0.5037553","0.50362134","0.502722","0.5003698","0.49956","0.499276","0.49727276","0.49715364","0.4961798","0.49591076","0.49442244","0.49442202","0.49438566","0.49435452","0.49377686","0.49154758","0.49147627","0.48982224","0.48978114","0.48890415","0.48839453","0.4880518","0.48703298","0.48683643","0.48589337","0.48584604","0.48531654","0.48395008","0.48334613","0.48222396","0.4819775","0.48018578","0.47913826","0.4790842","0.47895315","0.47875214","0.47731692","0.47710845","0.47624952","0.4760939","0.4754353","0.47496465","0.4749525","0.47466192","0.4736989","0.47369385","0.47369376","0.4736794","0.47308484","0.47233763","0.47072658","0.46985626","0.46936235","0.46898368","0.4682593","0.46811616","0.46714586","0.4671449","0.46701804","0.4669723","0.46670312","0.46582624","0.4652312","0.46515527","0.46448004","0.46420422"],"string":"[\n \"0.6773554\",\n \"0.64495486\",\n \"0.60057384\",\n \"0.5933061\",\n \"0.5871152\",\n \"0.58102006\",\n \"0.5744983\",\n \"0.57229316\",\n \"0.5719331\",\n \"0.56770027\",\n \"0.5667292\",\n \"0.5577523\",\n \"0.5570289\",\n \"0.55650806\",\n \"0.55393213\",\n \"0.55308384\",\n \"0.5441535\",\n \"0.5393277\",\n \"0.5389408\",\n \"0.5335206\",\n \"0.5284067\",\n \"0.52781135\",\n \"0.5264026\",\n \"0.5255529\",\n \"0.51701474\",\n \"0.511998\",\n \"0.5096278\",\n \"0.50743943\",\n \"0.506606\",\n \"0.5064148\",\n \"0.50633234\",\n \"0.5059192\",\n \"0.5045069\",\n \"0.50444794\",\n \"0.5037553\",\n \"0.50362134\",\n \"0.502722\",\n \"0.5003698\",\n \"0.49956\",\n \"0.499276\",\n \"0.49727276\",\n \"0.49715364\",\n \"0.4961798\",\n \"0.49591076\",\n \"0.49442244\",\n \"0.49442202\",\n \"0.49438566\",\n \"0.49435452\",\n \"0.49377686\",\n \"0.49154758\",\n \"0.49147627\",\n \"0.48982224\",\n \"0.48978114\",\n \"0.48890415\",\n \"0.48839453\",\n \"0.4880518\",\n \"0.48703298\",\n \"0.48683643\",\n \"0.48589337\",\n \"0.48584604\",\n \"0.48531654\",\n \"0.48395008\",\n \"0.48334613\",\n \"0.48222396\",\n \"0.4819775\",\n \"0.48018578\",\n \"0.47913826\",\n \"0.4790842\",\n \"0.47895315\",\n \"0.47875214\",\n \"0.47731692\",\n \"0.47710845\",\n \"0.47624952\",\n \"0.4760939\",\n \"0.4754353\",\n \"0.47496465\",\n \"0.4749525\",\n \"0.47466192\",\n \"0.4736989\",\n \"0.47369385\",\n \"0.47369376\",\n \"0.4736794\",\n \"0.47308484\",\n \"0.47233763\",\n \"0.47072658\",\n \"0.46985626\",\n \"0.46936235\",\n \"0.46898368\",\n \"0.4682593\",\n \"0.46811616\",\n \"0.46714586\",\n \"0.4671449\",\n \"0.46701804\",\n \"0.4669723\",\n \"0.46670312\",\n \"0.46582624\",\n \"0.4652312\",\n \"0.46515527\",\n \"0.46448004\",\n \"0.46420422\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7839599"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":94599,"cells":{"query":{"kind":"string","value":"Reset all adjacency information and rebuild it after adding nodes. This implements a collection of sanity checks (making sure every subscribed topic has a publisher, etc.) which, if they all pass, mean we can safely / sanely construct the dependency graph."},"document":{"kind":"string","value":"def build(self):\n self.logger.info('Rebuilding adjacency information')\n self.edges = collections.defaultdict(list)\n\n topic_to_publisher = collections.defaultdict(list)\n topic_to_subscribers = collections.defaultdict(list)\n node_to_missing_deps = collections.defaultdict(list)\n\n result = True\n\n for node in self.nodes.values():\n for topic in node.provided_topics.keys():\n topic_to_publisher[topic].append(node)\n\n for topic in node.required_topics:\n topic_to_subscribers[topic].append(node)\n\n for dep in node.additional_dependencies:\n if dep not in self.nodes:\n node_to_missing_deps[node].append(dep)\n\n if len(node_to_missing_deps) > 0:\n result = False\n msg = io.StringIO()\n print('Found [{}] managed processes with missing dependencies'.format(len(node_to_missing_deps)), file=msg)\n fmt = ' Managed process [{}] is missing [{}]'\n\n for (node, missing) in node_to_missing_deps.items():\n print(fmt.format(node.name, ', '.join(missing)), file=msg)\n self.logger.error(msg.getvalue())\n\n missing_publishers = []\n for topic in topic_to_subscribers.keys():\n if topic not in topic_to_publisher:\n missing_publishers.append(topic)\n\n if len(missing_publishers) > 0:\n result = False\n msg = io.StringIO()\n print('Found [{}] topics that do not have publishers'.format(len(missing_publishers)), file=msg)\n fmt = ' Topic [{}] with subscribers [{}]'\n\n for topic in missing_publishers:\n print(fmt.format(topic, ', '.join([x.name for x in topic_to_subscribers[topic]])), file=msg)\n self.logger.error(msg.getvalue())\n\n if not result:\n self.logger.error('Found errors when building adjacency information')\n raise GraphBuildError(\n 'Found errors when building adjacency information / graph edges. Check log for details')\n\n # Now we have enough information to build our edges. Phase 1: pub/sub stuff\n for (topic, subscribers) in topic_to_subscribers.items():\n publishers = topic_to_publisher[topic]\n\n for p in publishers:\n for s in subscribers:\n self.edges[p].append(s)\n\n # Phase 2: additional dependencies\n for node in self.nodes.values():\n for dep in node.additional_dependencies:\n src = self.nodes[dep]\n self.edges[src].append(node)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _reset_state(self):\n # Directed graph, (u, v) => v depends on u. u, v are pairs of (rule_name, rule_dir_abs)\n # Used for generating Topological Sort\n self._rule_to_dependency_graph_adjlist = {}\n self._topologically_sorted_build_rule_names = []\n\n # List of (dependency_name, dependency_dir_abs) for each build rule\n self._rule_to_dependency_list = {}\n\n # Space for rough work :P\n self._unresolved_commands = set()","def ResetGraph(self):\n self.nodes = []\n self.edges = []\n self.connections = []\n Node.resetNodeCount()\n Edge.resetEdgesCount()","def reset_graph(self):\n self.nodes = {}\n self.add_node(self.initial_state)\n self.add_node(self.final_state)","def clear(self):\r\n self.nodes = collections.defaultdict(list)\r\n self.nodes_mapping = collections.defaultdict(list)\r\n self.edges = 0\r\n #self.children_length={}\r\n self.parents_length = collections.defaultdict(lambda : collections.defaultdict(int))","def populate_graph(self):\n if self.edges and self.vertices:\n graph = Graph()\n for edge in self.edges:\n graph.add_edge(edge)\n self.graph = graph\n else:\n print(\"Populate edges & vertices first, then populate graph!\")","def reset_graph(self):\n self.graph = OrderedDict()","def _build_dependency_graph(self):\n\n #\n # Find the binary roles\n #\n nodes, roles = self._find_roles()\n\n #\n # Build the graph\n #\n working_list = list(set(nodes.keys()))\n\n setters = [b for b, r in roles.items() if Role.SETTER in r or Role.SETTER_GETTER in r]\n\n while working_list:\n b = working_list[0]\n working_list = working_list[1:]\n\n if nodes[b] not in self._graph:\n self._graph[nodes[b]] = []\n\n # it's a root node\n if Role.GETTER not in roles[b] and Role.SETTER_GETTER not in roles[b]:\n nodes[b].set_root()\n\n # takes params from some other binary\n else:\n is_orphan = True\n for setter in setters:\n setter_strings_set = set(nodes[setter].role_strings)\n node_strings_set = set(nodes[b].role_strings)\n if setter_strings_set.intersection(node_strings_set):\n if nodes[setter] not in self._graph:\n self._graph[nodes[setter]] = []\n self._graph[nodes[setter]].append(nodes[b])\n is_orphan = False\n\n # mark orphans\n if is_orphan:\n nodes[b].set_orphan()\n\n # Clean up\n for k, childs in self._graph.iteritems():\n self._graph[k] = list(set(childs))\n\n # set leaves\n for k, c in self._graph.iteritems():\n if not c:\n k.set_leaf()\n\n # post processing:\n # remove those nodes that are not orphans\n # and are not network parsers\n\n nodes = self.nodes\n children = [c for x in self._graph.values() for c in x if x]\n leafs_non_orphan = [n for n in nodes if n.leaf and not n.orphan]\n seed_names = [x.split('/')[-1] for x in self._seed_bins]\n spurious_nodes = [n for n in leafs_non_orphan if n not in children and n.bin.split('/')[-1] not in seed_names]\n for to_rem in spurious_nodes:\n del self._graph[to_rem]","def _restoreGraph(self):\n\n # self.tempG = self.g.copy()\n\n if nx.is_directed(self.g):\n self.tempG = nx.DiGraph(self.g)\n else:\n self.tempG = nx.Graph(self.g)\n self.deletedEdges = []\n self.deletedNodes = []","def _reset_topological_order(self):\n self._topological_order = self._input_nodes[:]\n self.sorted = False","def _create_connections(self):\n self.predecessors = {}\n self.successors = {}\n for nd in self.nodes:\n self.predecessors[nd.name] = []\n self.successors[nd.name] = []\n\n for (nd_out, nd_in) in self.edges:\n self.predecessors[nd_in.name].append(nd_out)\n self.successors[nd_out.name].append(nd_in)","def __init__(self):\n self._adjacency_list = {\n\n }","def _internal_build(self):\n self.nodes = self.__tree.Nodes()\n self.edges = self.__tree.Edges()\n self.augmentedEdges = {}\n for key, val in self.__tree.AugmentedEdges().items():\n self.augmentedEdges[key] = list(val)\n self.root = self.__tree.Root()\n\n seen = set()\n self.branches = set()\n\n # Find all of the branching nodes in the tree, degree > 1\n # That is, they appear in more than one edge\n for e1, e2 in self.edges:\n if e1 not in seen:\n seen.add(e1)\n else:\n self.branches.add(e1)\n\n if e2 not in seen:\n seen.add(e2)\n else:\n self.branches.add(e2)\n\n # The nodes that are not branches are leaves\n self.leaves = set(self.nodes.keys()) - self.branches\n self.leaves.remove(self.root)","def _build_graph_general(self): \n\n #Find a canonical coloring scheme\n #Each node has a color that is determined by the non-mapped aspects\n nodecolors=set()\n for nl in self.net.iter_node_layers():\n nodecolors.add(self._slice_node_layer_not_allowed(nl))\n nodecolors_sorted=sorted(list(nodecolors))\n del nodecolors\n self._assert_full_order(nodecolors_sorted)\n self.colormap=dict( ((color,colorid) for colorid,color in enumerate(nodecolors_sorted) ))\n\n #each aux node has a color that is determined by the aspect\n self.auxcolormap=dict( ((auxcolor, auxcolorid+len(self.colormap)) for auxcolorid,auxcolor in enumerate(sorted(self.asp)) ) )\n\n\n #Add the underlying network\n #node-layers:\n for nl in self.net.iter_node_layers():\n nlid=self._get_node_id(nl)\n color=self._slice_node_layer_not_allowed(nl)\n colorid=self.colormap[color]\n self.add_node(nlid,colorid)\n\n #edges between node-layers:\n for nl1 in self.net.iter_node_layers():\n for nl2 in self.net[nl1]:\n nl1id=self._get_node_id(nl1)\n nl2id=self._get_node_id(nl2)\n self.add_link(nl1id,nl2id)\n\n\n #Add the auxiliary nodes and edges\n #add the aux nodes\n for a in self.asp:\n for elayer in self.net.slices[a]:\n auxid=self._get_auxnode_id( (a,elayer) )\n auxcolorid=self.auxcolormap[a]\n self.add_node(auxid,auxcolorid)\n \n #add the aux edges\n for nl in self.net.iter_node_layers():\n for a in self.asp:\n nlid=self._get_node_id(nl)\n auxid=self._get_auxnode_id( (a,nl[a]) )\n self.add_link(nlid,auxid)","def update(self):\n\n for node in self.nodes:\n for edge in node.edges:\n for i, edge_node in enumerate(edge.nodes):\n if edge_node.id != node.id:\n edge_node.add_edge(edge)\n\n return self","def reset(self):\n\t\tself.graph = OrderedDict()\n\t\tself.bottoms = OrderedDict()\n\t\tself.output_shape = OrderedDict()\n\t\tself.cur_tensor = None\n\t\tself.cur_id = None\n\t\tself.tmp_list = []\n\t\tself.log_init()","def reset_graph(self):\n raise NotImplementedError","def complete_graph(self):\n root_nodes = set()\n\n for name, a_block in self.wf['action'].items():\n\n a_block['name'] = name\n\n for n in a_block.get('needs', []):\n if not self.wf['action'][n].get('next', None):\n self.wf['action'][n]['next'] = set()\n self.wf['action'][n]['next'].add(name)\n\n if not a_block.get('needs', None):\n root_nodes.add(name)\n\n self.wf['root'] = root_nodes","def start_new_graph(self):\n self.nodes = {}\n self.reset_graph()","def populate_graph(self):","def _clean_graph(self):\n for entry_node in self._entry_nodes:\n self._clean_graph_visit(entry_node.get_func_first_node(), {})","def clear(self):\n \n self.node_set.clear()\n self.prefix.clear()\n self.suffix.clear()\n self.num_node = 0\n self.edges = 0","def rebuild_graph_data(self, consolidator=None):\n\n if len(self.nodes) == 0:\n raise DomainException(\"No nodes supplied to graph!\")\n\n if consolidator != None:\n for node in self.nodes.values():\n na = set()\n nat = set()\n no = set()\n nr = set()\n ni = set()\n nir = set()\n nrc = Counter()\n for atype, attribute in node.attributes:\n try:\n atype = consolidator(atype)\n except ConsolidatorException:\n continue\n na.add((atype, attribute))\n nat.add(atype)\n\n for rtype, dest in node.outgoing_relations:\n try:\n rtype = consolidator(rtype)\n except ConsolidatorException:\n continue\n no.add((rtype, dest))\n nr.add(rtype)\n nrc[rtype] += 1\n \n for rtype, pred in node.incoming_relations:\n try:\n rtype = consolidator(rtype)\n except ConsolidatorException:\n continue\n ni.add((rtype, pred))\n nir.add(rtype)\n nrc[rtype] += 1\n\n #update values\n node.attributes = na\n node.outgoing_relations = no\n node.incoming_relations = ni\n node.rtypes = nr\n node.i_rtypes = nir\n node.atypes = nat\n node.rtype_count = nrc\n\n # ==== compute member variables ====\n self.usage_map = self.map_uses()\n self.usage_counts = {x:len(y) for x,y in self.usage_map.items()}\n self.rtype_vectors = self.index_rtypes()\n self.node_vectors = self.index_nodes()\n self.rkdtree_keys, _rvalues = zip(*self.rtype_vectors.items())\n self.rkdtree = cKDTree(_rvalues)\n self.nkdtree_keys, _nvalues = zip(*self.node_vectors.items())\n self.nkdtree = cKDTree(_nvalues)\n\n # ==== precompute some vector constructs ====\n for node in self.nodes.values():\n node.compute_dicts(self)\n\n # ==== compute tf-idf weights for all nodes ====\n\n #calculate number of nodes containing rtype and \n #find maximum frequency rtype for any single node\n maxftd = 0\n c2 = Counter()\n for y in self.nodes.values():\n for k,z in y.rtype_count.items():\n c2[k] += 1\n if z > maxftd:\n maxftd = z\n\n #calculate augmented term frequency\n tf = Counter()\n for x,y in self.nodes.items():\n for z,v in y.rtype_count.items():\n tf[(x,z)] = 0.5 + 0.5*(v/maxftd)\n\n #calculate inverse document frequency\n idf = Counter()\n N = len(self.nodes)\n for x in c2:\n idf[x] = log(N / c2[x])\n\n tfidf = {}\n for x,y in self.nodes.items():\n for z in y.rtype_count:\n tmp = tfidf.setdefault(x,{})\n tmp[z] = tf[(x,z)] * idf[z]\n\n self.tfidf = tfidf\n self.dirty = False","def prepare(self):\n if self._ready_nodes is not None:\n raise ValueError(\"cannot prepare() more than once\")\n\n self._ready_nodes = [\n i.node for i in self._node2info.values() if i.npredecessors == 0\n ]\n # ready_nodes is set before we look for cycles on purpose:\n # if the user wants to catch the CycleError, that's fine,\n # they can continue using the instance to grab as many\n # nodes as possible before cycles block more progress\n cycle = self._find_cycle()\n if cycle:\n raise CycleError(f\"nodes are in a cycle\", cycle)","def _reset_gradients(self):\n self.grad = None # gradient itself\n self.grad_fn = None # functions to call for gradient\n self.grad_expected = 0 # number of gradients expected from parents\n self.grad_received = 0 # number of gradients received from parents\n self.children = [] # children of node in graph\n self.ctx = AutogradContext() # contexts for AutogradFunctions","def _reset_traversal_state(self):\n for n in self.nodes.values():\n n.reset_traversal_state()","def clean_local_memory(self):\n self.namespaces = self._rdf_builder.namespaces\n self.dataset = self._rdf_builder.dataset\n\n self.ontology_graph = self._rdf_builder.ontology_graph\n self.instance_graph = self._rdf_builder.instance_graph\n self.claim_graph = self._rdf_builder.claim_graph\n self.perspective_graph = self._rdf_builder.perspective_graph\n self.interaction_graph = self._rdf_builder.interaction_graph","def _setup_graph(self):\n pass","def _setup_graph(self):\n pass","def init_graph(self):\n import dgl\n\n adj_list = []\n for rel_type in range(1, self.n_relations, 1):\n edge_idxs = self.ckg.filter_edges(\n lambda edge: edge.data[\"relation_id\"] == rel_type\n )\n sub_graph = (\n dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True)\n .adjacency_matrix(transpose=False, scipy_fmt=\"coo\")\n .astype(\"float\")\n )\n rowsum = np.array(sub_graph.sum(1))\n d_inv = np.power(rowsum, -1).flatten()\n d_inv[np.isinf(d_inv)] = 0.0\n d_mat_inv = sp.diags(d_inv)\n norm_adj = d_mat_inv.dot(sub_graph).tocoo()\n adj_list.append(norm_adj)\n\n final_adj_matrix = sum(adj_list).tocoo()\n indices = torch.LongTensor([final_adj_matrix.row, final_adj_matrix.col])\n values = torch.FloatTensor(final_adj_matrix.data)\n adj_matrix_tensor = torch.sparse.FloatTensor(indices, values, self.matrix_size)\n return adj_matrix_tensor.to(self.device)","def reset_edges(self):\n super().reset_edges()\n\n # If we're in default state, notheing to rest\n if self._modified_weighted_adj_matrices is None:\n return\n\n # Degrees are reset, so we need to reset the original weight scaling\n if self.scale_weights and not self.scaling_skipped:\n self._scale_weights_to_degree()\n self._generate_weighted_adj_matrices()\n else:\n # No weight scaling so just load prev values from cache\n self.weighted_adj_matrices = {**self.weighted_adj_matrices, **self._modified_weighted_adj_matrices}\n self._modified_weighted_adj_matrices = None","def setup_ant(self):\n self.visited_nodes[1:] = []\n self.actual_node = self.start_pos","def __init__(self):\n self._graph = DirectedGraph()\n self._graph_copies = []","def prepare_graph(\n self,\n adjacency,\n weights,\n weighted=False,\n undirected=False,\n force_dense=True,\n noselfloop=True,\n verbose=True,\n ):\n\n # df_adj = pd.read_csv(in_folder + adj_name, index_col=0) # read adjacency file\n print(\"\\nAdjacency shape: {0}\".format(adjacency.shape), flush=True)\n\n # create the graph adding nodes and edges\n A = self.read_graph(\n adj=adjacency,\n weights=weights,\n weighted=weighted,\n undirected=undirected,\n noselfloop=noselfloop,\n verbose=verbose,\n )\n\n nodes = list(A[0].nodes)\n print(\"\\nNumber of nodes =\", len(nodes), flush=True)\n print(\"Number of layers =\", len(A), flush=True)\n if verbose:\n self.print_graph_stat(A)\n\n # save the multilayer network in a tensor with all layers\n if force_dense:\n B = self.build_B_from_A(A, nodes=nodes)\n else:\n B = self.build_sparse_B_from_A(A)\n\n return A, B, nodes","def formAdjacencyMatrix(self):\n self.adjacencyMatrix = dict()\n for i in self.node:\n self.adjacencyMatrix[i] = dict()\n for j in self.node:\n self.adjacencyMatrix[i][j] = 0\n \n for ij in self.link:\n self.adjacencyMatrix[self.link[ij].tail][self.link[ij].head] = 1","def finalize(self):\n # Establish forward/reverse star lists, set travel times to free-flow\n for i in self.node:\n self.node[i].forwardStar = list()\n self.node[i].reverseStar = list()\n \n for ij in self.link:\n self.node[self.link[ij].tail].forwardStar.append(ij)\n self.node[self.link[ij].head].reverseStar.append(ij)\n self.link[ij].cost = self.link[ij].freeFlowTime + self.link[ij].length * self.distanceFactor + self.link[ij].toll * self.tollFactor\n self.link[ij].flow = 0\n \n for OD in self.ODpair:\n self.ODpair[OD].leastCost = 0","def reset_edges(self):\n\n # Ensure original edges are stored in cache, otherwise nothing to do.\n if self._modified_edges is None or self._weighted_modified_edges is None:\n return\n\n # Restore the former value from cache\n self.adj_matrices = {**self.adj_matrices, **self._modified_edges}\n self.degree_weighted_matrices = {**self.degree_weighted_matrices, **self._weighted_modified_edges}\n self.in_degree = {**self.in_degree, **self._orig_in_degree}\n self.out_degree = {**self.out_degree, **self._orig_out_degree}\n\n # Reset the edge and degree cache\n self._modified_edges = None\n self._weighted_modified_edges = None\n self._orig_in_degree = dict()\n self._orig_out_degree = dict()","def _refine_matrix_with_additional_connections(self):\n new_graph = self.graph.copy()\n for node in tqdm.tqdm(self.graph.nodes(), disable=not self.verbose):\n if self.graph.node[node][\"type\"] == \"hashtag\":\n for neighbour in self.graph.neighbors(node):\n if self.graph.node[neighbour][\"type\"] == \"username\":\n for other_node in self.graph.neighbors(neighbour):\n if self.graph.node[other_node][\"type\"] == \"hashtag\" \\\n and not self.graph.has_edge(node, other_node) \\\n and not node == other_node:\n new_graph.add_edge(node, other_node)\n self.graph = new_graph","def refresh_metadata(self):\n #self.node_index = None\n #self.edge_index = None\n #self._calc_edge_centers = False\n #self._calc_cell_centers = False\n #self._calc_vcenters = False\n self._node_to_edges = None\n self._node_to_cells = None","def build_graph(self):\n for each_list in self.lab.look():\n vertice = self._add_vertice(each_list)\n if vertice:\n self.unvisited.add(vertice)\n self.graph.addEdge((self.current, vertice))\n \n self.unvisited -= self.visited\n self._connect_neighbours()","def fix_graph(self,graph):\n graph_compleate_reachable = False\n while not graph_compleate_reachable:\n not_reachable_in ,not_reachable_out = self.not_reachable(graph)\n for n in not_reachable_in:\n graph.add_edge(self.random.randint(0,n-1),n)\n for n in not_reachable_out:\n graph.add_edge(n,self.random.randint(n+1, self.nodes-1))\n graph_compleate_reachable = len(not_reachable_in)==0 and len(not_reachable_out)==0\n return graph","def setUp(self):\n self.complete = nx.Graph()\n self.complete.add_edge(1, 2)\n self.complete.add_edge(2, 3)\n self.complete.add_edge(1, 3)\n\n self.small_tree = nx.Graph()\n self.small_tree.add_edge(1, 2)\n self.small_tree.add_edge(2, 3)\n self.small_tree.add_edge(3, 4)\n self.small_tree.add_edge(1, 4)\n self.small_tree.add_edge(2, 4)\n self.small_tree.add_edge(4, 5)\n self.small_tree.add_edge(5, 6)\n self.small_tree.add_edge(5, 7)\n self.small_tree.add_edge(6, 7)\n\n self.deterministic_graph = nx.Graph()\n self.deterministic_graph.add_edge(1, 2)\n self.deterministic_graph.add_edge(1, 3)\n self.deterministic_graph.add_edge(3, 4)\n self.deterministic_graph.add_edge(2, 4)\n self.deterministic_graph.add_edge(3, 5)\n self.deterministic_graph.add_edge(4, 5)\n self.deterministic_graph.add_edge(3, 6)\n self.deterministic_graph.add_edge(5, 6)","def make_complete_graph(num_nodes):\n complete_digraph = {}\n if num_nodes > 0 and type(num_nodes) == int:\n neighbors = set([idx for idx in range(num_nodes)])\n for idx in range(num_nodes):\n complete_digraph[idx] = neighbors.copy() #creates adjacency set\n complete_digraph[idx].remove(idx) # pop out self-loop \n return complete_digraph","def _pre_setup(self):\n super()._pre_setup()\n self.create_connections_indices()","def _pre_setup(self):\n super()._pre_setup()\n self.create_connections_indices()","def _pre_setup(self):\n super()._pre_setup()\n self.create_connections_indices()","def legalize_graph(gm: pippy.fx.GraphModule) -> pippy.fx.GraphModule:\n indeg = {node: 0 for node in gm.graph.nodes}\n new_graph = pippy.fx.Graph()\n # Track how many unfulfilled dependencies each node has\n for node in gm.graph.nodes:\n for user in node.users:\n indeg[user] += 1\n queue: collections.deque = collections.deque()\n # Add all nodes with no dependencies to the queue\n for node in gm.graph.nodes:\n if indeg[node] == 0:\n queue.append(node)\n env: Dict[pippy.fx.Node, pippy.fx.Node] = {}\n # Pop nodes from the queue, and add nodes that have had all their\n # dependencies fulfilled\n while len(queue) > 0:\n cur = queue.popleft()\n env[cur] = new_graph.node_copy(cur, lambda x: env[x])\n for user in cur.users:\n indeg[user] -= 1\n if indeg[user] == 0:\n queue.append(user)\n # If the new graph's size is not as large as the old one, then there must be\n # a cycle (i.e. some node's dependencies were not satisfied.)\n if len(new_graph.nodes) < len(gm.graph.nodes):\n raise RuntimeError(f\"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}\")\n gm.graph = new_graph\n return gm","def clear(self):\n self.nodes = list()\n self.inputs = list()\n self.nodes += [self]","def setup(self):\n switches, links = self.generateTopology()\n self.graph = KytosGraph()\n self.graph.clear()\n self.graph.update_nodes(switches)\n self.graph.update_links(links)\n self.graph.set_path_fun(nx.shortest_simple_paths)","def _build_graph(self):\n pass","def __init__(self, nodes=None, edges=None):\n self._nodes = []\n self.nodes = nodes\n self._edges = []\n self.edges = edges\n self._create_connections()\n self._sorted_nodes = None\n self._node_wip = []","def clear(self):\n self.mismatch_error = None\n self.pt_outs = None\n self._onnx_graph = None\n self.upper_graph_info = None\n self.lower_graph_info = None","def reset_visited(self):\n self.__visited = False","def __init__(self):\n\t\tself.edges = defaultdict(list)\n\t\tself.weights = {}\n\t\tself.connections = {}","def __init__(self, directed=True):\n self.nodes = set()\n self.edges = []\n self.directed = directed","def _initilise_graph_db(self):\n for collector in self.collectors:\n collector.init_graph_db()","def processLoadedLinkNodes(self, isLoadingModel):\r\n \r\n if(isLoadingModel):\r\n #=====================================================\r\n # Hierarchical structure maintenance (See HierarchicalASGNode.py)\r\n #=====================================================\r\n for node in self.newLinkNodeQueue:\r\n # NOTE: node.in_connections_ could be > 1 in the metamodel since \r\n # AToM3 considers meta-model relations to have hierarchy active!\r\n # Of course I didn't intend this, and it has no meaning, so ignore!\r\n if(node.isHierarchicalLink() and len(node.in_connections_) == 1):\r\n for child in node.out_connections_:\r\n child._setHierParent(node.in_connections_[0])\r\n node.in_connections_[0]._addHierChildrenList(node.out_connections_) \r\n \r\n\r\n #=======================================================================\r\n # QOCA Constraints\r\n # Only do this if we are actually using QOCA in the first place...\r\n #=======================================================================\r\n if(isNotUsingQoca()): \r\n return\r\n \r\n for node in self.newLinkNodeQueue:\r\n # Apply any QOCA linear constraints\r\n # For graph grammars, graphObject_ may be none, so GGrule.py will also\r\n # trigger QOCA in its replaceSides() method\r\n if(hasattr(node, 'QOCA') and not node.__dict__.has_key('QOCA')):\r\n # Make sure that node has a method called 'QOCA', not an attribute\r\n node.QOCA(None)\r\n \r\n # Clean out the queue\r\n self.newLinkNodeQueue = []","def clean(self):\n\t\tfor v in self:\n\t\t\tv.reset_distance()\n\t\t\tv.reset_predecessor()\n\t\t\tv.reset_visited()","def __init__(self):\n self.adjList = {}","def reset_nn(self): # Clear current network\n self.weights = np.zeros((p.num_rovers, self.n_weights))\n self.in_layer = np.zeros((p.num_rovers, self.n_inputs))\n self.hid_layer = np.zeros((p.num_rovers, self.n_nodes))\n self.out_layer = np.zeros((p.num_rovers, self.n_outputs))","def _init_nodes(self, nodes):\n attributes = self.get_node_attributes()\n for node in nodes:\n if not self._is_node_added(node):\n self._nodes.append(self._get_node_as_dictionary(node, attributes))","def topo_sort(self):\n # TODO: detect cycles\n self.find_reachable_nodes()\n # save list of nodes in topo order\n self.nodes = []\n # assign each node an id field incrementally\n cur_id = 0\n # count visited outgoing edges for each node\n unvisited = {}\n for nid, node in list(self.found.items()):\n unvisited[nid] = node.nout\n queue = [self.root]\n #print >>sys.stderr, '+++'\n while queue:\n # take off nodes whose all outgoing edges are visited from\n # queue head\n node = queue.pop(0)\n self.nodes.append(node)\n node.hg = self\n node.id = cur_id\n cur_id += 1\n for edge in node.incoming:\n edge.hg = self\n for tailnode in edge.tail:\n #print >>sys.stderr, tailnode\n unvisited[id(tailnode)] -= 1\n if unvisited[id(tailnode)] == 0:\n queue.append(tailnode)\n self.sanity_check()\n self.tasks_done.add('topo_sort')","def abort(self):\n for node in self.dep_graph.nodes_iter():\n role = self.roles[node]\n role.new_rep = role.cur_rep\n role.new_hosts = list(role.cur_hosts)\n for edge in self.dep_graph.edges_iter():\n edge_data = self.dep_graph.get_edge_data(*edge)\n edge_data['new_weight'] = edge_data['cur_weight']","def create_initial_graph(self):\n # Initialise weights\n for link in self.gene_links:\n link.weight = random.uniform(weight_init_min, weight_init_max)\n # Initialise biases\n for node in self.gene_nodes:\n node.bias = random.uniform(bias_init_min, bias_init_max)\n if node.can_modify:\n node.act_func = self.act_set.get_random_activation_func()\n if node.act_func in [activations.gaussian, activations.sin]:\n if node.act_func.__name__[0] == \"g\":\n node.freq += random.uniform(-guass_freq_adjust, guass_freq_adjust)\n elif node.act_func.__name__[0] == \"s\":\n node.freq += random.uniform(-sin_freq_adjust, sin_freq_adjust)\n node.amp += random.uniform(-func_amp_adjust, func_amp_adjust)\n node.vshift += random.uniform(-func_vshift_adjust, func_vshift_adjust)","def reset(self):\n self.visited = set()\n del self.targets[0]","def _init_graph(self):\n self.G = nx.Graph()\n self.G.add_nodes_from([1,2,3,4,5])\n self.G.add_edges_from([(1,2),(2,3),(2,4)\\\n ,(2,5),(3,4),(4,5)])","def build_graph(self):\n\t\tself._create_placeholders()\n\t\tself._create_embedding()\n\t\tself._create_recurrent_layers()\n\t\tself._create_de_embedding()\n\t\tself._create_loss()\n\t\tself._create_optimizer()\n\t\tself._create_summaries()","def build_graph(self):\n self._reset_iterator_memory()\n self._construct_graph_handler()\n assert self.graph_handler\n for rxn_id in self.graph_handler.get_valid_reaction_ids():\n rxn = db.Reaction(rxn_id, self._reactions)\n self.graph_handler.add_rxn(rxn)","def topology_complete(self):\n\t\tfor i in range(len(self.sites) - 1):\n\t\t\tfor j in range(i + 1, len(self.sites)):\n\t\t\t\tself.sites[i].neighbors.append(self.sites[j])\n\t\t\t\tself.sites[j].neighbors.append(self.sites[i])","def __init__(self):\n self.data_graph = self._initialise_data()\n\n self.messages_sent = []\n self.messages_received = []\n\n self.answered_true = set()\n self.implied_true = set()\n\n self.current_subgraph = set()","def clear(self):\n self._nodes = { }\n self._arcs = set()","def solve(self):\n self.left -= len(self.nodes)\n \n def depths(x,depth = 0):\n depth+=1\n for y in self.graph[x]:\n if y in self.nodes:\n self.nodes.remove(y)\n depth = depths(y,depth)\n return depth\n \n while len(self.nodes):\n x = self.nodes.pop()\n self.firstGen.append(depths(x))\n #print self.graph\n #print self.nodes\n #print self.firstGen","def __initialize_connection_strengths(G):\n G_prime = G.__deepcopy__() # construct a deepcopy of the graph\n # for every vertex in the graph, initialize the connection strength to zero\n for node in G_prime.get_nodeset(): node.add_attribute(StoerWagner.CONNECTION_STRENGTH_ATTRIBUTE, float(0))\n return G_prime # return the new graph","def _initializeAdjacencyList(self):\n\n if self.comm.rank == 0:\n # First, create a dictionary of common edges shared by components\n edgeToFace = {}\n for elemID in self.bdfInfo.elements:\n elemInfo = self.bdfInfo.elements[elemID]\n elemConn = elemInfo.nodes\n compID = self.meshLoader.nastranToTACSCompIDDict[elemInfo.pid]\n nnodes = len(elemConn)\n if nnodes >= 2:\n for j in range(nnodes):\n nodeID1 = elemConn[j]\n nodeID2 = elemConn[(j + 1) % nnodes]\n\n if nodeID1 < nodeID2:\n key = (nodeID1, nodeID2)\n else:\n key = (nodeID2, nodeID1)\n\n if key not in edgeToFace:\n edgeToFace[key] = [compID]\n elif compID not in edgeToFace[key]:\n edgeToFace[key].append(compID)\n\n # Now we loop back over each element and each edge. By\n # using the edgeToFace dictionary, we can now determine\n # which components IDs (jComp) are connected to the\n # current component ID (iComp).\n self.adjacentComps = []\n\n for edgeKey in edgeToFace:\n if len(edgeToFace[edgeKey]) >= 2:\n for i, iComp in enumerate(edgeToFace[edgeKey][:-1]):\n for jComp in edgeToFace[edgeKey][i + 1 :]:\n if iComp < jComp:\n dvKey = (iComp, jComp)\n else:\n dvKey = (jComp, iComp)\n if dvKey not in self.adjacentComps:\n self.adjacentComps.append(dvKey)\n\n else:\n self.adjacentComps = None\n\n # Wait for root\n self.comm.barrier()","def _update_dead_nodes(self) -> None:\n with self._dead_node_lock:\n self._dead_nodes = self._seen_nodes - self._running_nodes","def cfdProcessNodeTopology(self):\r\n self.nodeElements = self.cfdInvertConnectivity(self.elementNodes)\r\n self.nodeFaces = self.cfdInvertConnectivity(self.faceNodes)","def direct_network(self):\n #print list(self.get_subgraphs())\n graphs = [self._depth_first_directed(g) for g in self.get_subgraphs()]\n self._network = reduce(lambda a, b: nx.union(a, b), graphs)","def fit(self, adjacency: Union[sparse.csr_matrix, np.ndarray], seeds: Union[np.ndarray, dict] = None) \\\n -> 'Propagation':\n adjacency = check_format(adjacency)\n n = adjacency.shape[0]\n index_seed, index_remain, labels_seed = self._instanciate_vars(adjacency, seeds)\n\n if self.node_order == 'random':\n np.random.shuffle(index_remain)\n elif self.node_order == 'decreasing':\n index = np.argsort(-adjacency.T.dot(np.ones(n))).astype(np.int32)\n index_remain = index[index_remain]\n elif self.node_order == 'increasing':\n index = np.argsort(adjacency.T.dot(np.ones(n))).astype(np.int32)\n index_remain = index[index_remain]\n\n labels = -np.ones(n, dtype=np.int32)\n labels[index_seed] = labels_seed\n labels_remain = np.zeros_like(index_remain, dtype=np.int32)\n\n indptr = adjacency.indptr.astype(np.int32)\n indices = adjacency.indices.astype(np.int32)\n if self.weighted:\n data = adjacency.data.astype(np.float32)\n else:\n data = np.ones(n, dtype=np.float32)\n\n t = 0\n while t < self.n_iter and not np.array_equal(labels_remain, labels[index_remain]):\n t += 1\n labels_remain = labels[index_remain].copy()\n labels = np.asarray(vote_update(indptr, indices, data, labels, index_remain))\n\n membership = membership_matrix(labels)\n membership = normalize(adjacency.dot(membership))\n\n self.labels_ = labels\n self.membership_ = membership\n\n return self","def reset(self):\n super(CheckMayaAbstract, self).reset()\n self.errorNodes = list()\n self._errorDict = {}","def make_complete_graph(num_nodes):\n xgraph = {} #Create a Blank Dict\n if num_nodes - 1 < 0: # checks to see if the num_nodes is less then 0 (negative number) if it is return empty graph (dict). Could probably combine the If statments for negative nodes and 1 node together\n return xgraph\n if num_nodes - 1 == 0: # If the number of nodes is 1 or returns a one node dict because there are no edges to compute\n xgraph[0] = set([]) # creates a dict that represents a single node graph as per the requirement\n return xgraph # the empty Graph\n else:\n for base_node in range(num_nodes): # This portion starts the build phase. for each node it will compute the theretical maximum amount of edges\n xlist = set([]) # defines an empty list. We first build a list for each node and the append to a dict. This list is erased with each iteration\n #print base_node # testing - REMOVE\n for edge_node in range(num_nodes):\n #print edge_node # testing - REMOVE\n if edge_node != base_node: #No Looping is allowed for this project. Therefor we check to insure the we are not counting a self node connection (edge_node NOT equal base_node)\n xlist.add(edge_node) # Populating list that will be added to dict\n\n xgraph[base_node] = xlist # Appending created list to the dict\n\n return xgraph # returning populated dict","def make_complete_graph(num_nodes):\n xgraph = {} #Create a Blank Dict\n if num_nodes - 1 < 0: # checks to see if the num_nodes is less then 0 (negative number) if it is return empty graph (dict). Could probably combine the If statments for negative nodes and 1 node together\n return xgraph\n if num_nodes - 1 == 0: # If the number of nodes is 1 or returns a one node dict because there are no edges to compute\n xgraph[0] = set([]) # creates a dict that represents a single node graph as per the requirement\n return xgraph # the empty Graph\n else:\n for base_node in range(num_nodes): # This portion starts the build phase. for each node it will compute the theretical maximum amount of edges\n xlist = set([]) # defines an empty list. We first build a list for each node and the append to a dict. This list is erased with each iteration\n #print base_node # testing - REMOVE\n for edge_node in range(num_nodes):\n #print edge_node # testing - REMOVE\n if edge_node != base_node: #No Looping is allowed for this project. Therefor we check to insure the we are not counting a self node connection (edge_node NOT equal base_node)\n xlist.add(edge_node) # Populating list that will be added to dict\n\n xgraph[base_node] = xlist # Appending created list to the dict\n\n return xgraph # returning populated dict","def initGraphs(self):\n \n self.graph = ConjunctiveGraph()\n # Create a separate graph for annotations\n self.annotationGraph = ConjunctiveGraph()\n \n self.log.debug('Adding namespaces to graphs')\n # Bind namespaces to graphs\n for namespace in self.namespaces:\n self.graph.namespace_manager.bind(namespace, self.namespaces[namespace])\n\n # Same for annotation graph\n for namespace in self.annotationNamespaces:\n self.annotationGraph.namespace_manager.bind(namespace, self.annotationNamespaces[namespace])\n \n # Add schema information\n self.log.debug('Adding some schema information (dimension and measure properties) ')\n self.addDataCellProperty()\n\n # Add dimensions \n self.graph.add((self.namespaces['tablink']['dimension'], RDF.type, self.namespaces['qb']['DimensionProperty']))\n \n #self.graph.add((self.namespaces['tablink']['label'], RDF.type, RDF['Property']))","def clean_edges(self):\n for from_node in self.all_nodes():\n for to_node in self.all_nodes():\n if from_node == to_node:\n continue\n dup = list(filter(lambda x: x.from_node == from_node and x.to_node == to_node, self.edges))\n if len(dup) > 1:\n for d in dup[1:]:\n self.edges.remove(d)","def __init__(self, nodes=[], edges=[], connections=[], directed=False, isNetwork=False):\n Node.count=0\n Edge.count=0\n self.nodes = [n for n in nodes]\n self.edges = [e for e in edges]\n self.connections = [(a, b) for (a, b) in connections]\n self.isDirected = directed\n self.isNetwork = isNetwork","def _CreateAdjacencyListGraph(self):\n graph = dict()\n for nodes in self.nodes:\n graph[nodes[0]] = set()\n for edges in self.edges:\n graph[edges[0]].add(edges[1])\n return graph","def reset(self):\n self._clusters = {}\n self._clusters_val = {}\n self._centroids = {}\n self.store()","def build_graph(self):\n pass","def clean_edges(self):","def reset_weight_zero(self):\n self.node_embedding = np.random.uniform(low=-0.5, high=0.5, size=(self.vocab_size, self.layer1_size)).astype(\n np.float32)\n self.context_embedding = np.zeros((self.vocab_size, self.layer1_size), dtype=np.float32)\n\n self.centroid = np.zeros((self.k, self.layer1_size), dtype=np.float32)\n self.covariance_mat = np.zeros((self.k, self.layer1_size, self.layer1_size), dtype=np.float32)\n self.inv_covariance_mat = np.zeros((self.k, self.layer1_size, self.layer1_size), dtype=np.float32)\n self.pi = np.zeros((self.vocab_size, self.k), dtype=np.float32)\n log.info(\"reset communities data| k: {}\".format(self.k))","def init_adjacency(self, A):\n A[A==0] = self.INFINITY","def add_resets_edges(graph, start):\n for node in graph.nodes:\n neighbors = list(graph[node])\n if neighbors == [node]:\n graph.add_edge(node, start, label=\"RESET / \")","def transition(self):\n for node in self.net.nodes():\n if node not in self.evidence:\n self.update_node(node)","def __init__(self):\n\t\tself.edges = defaultdict(list)\n\t\tself.weights = {}","def __init__(self):\n\t\tself.edges = defaultdict(list)\n\t\tself.weights = {}","def _generate_graph(self) -> None:\n self.g_ = nx.random_partition_graph(list(self._community_sizes),\n p_in=self.community_p_in,\n p_out=self.community_p_out,\n seed=self.seed)\n\n for _, nv in self.g_.nodes.data():\n nv[\"infected\"] = 0\n nv[\"immune\"] = False\n nv[\"alive\"] = True\n nv[\"_edges\"] = []\n nv[\"isolated\"] = False\n nv[\"mask\"] = 0.0","def generate_full_adj(self):\n edges = np.zeros(shape=(self.n_balls, self.n_balls))\n row_idx = 0 # start filling adjacency mat from root node\n col_idx = 1 # skip the root node and start from 2nd node\n for l in range(self.nl):\n for n in range(self.nn[l]):\n edges[row_idx, col_idx:col_idx + self.nc[l]] = 1\n # Increase counters after filling connections for a parent node\n col_idx += self.nc[l]\n row_idx += 1\n return edges","def update_node2edge(self):\n self.node2edge = {e.child : e for e in self.edge}\n childrenset = set(self.node2edge.keys())\n rootset = set(e.parent for e in self.edge).difference(childrenset)\n if len(rootset) > 1:\n raise Warning(\"there should be a single root: \" + str(rootset))\n if len(rootset) == 0:\n raise Exception(\"there should be at least one root!\")\n self.root = rootset.pop()","def setUp(self):\n self.complete = nx.Graph()\n self.complete.add_edge(1, 2)\n self.complete.add_edge(2, 3)\n self.complete.add_edge(1, 3)\n\n self.small_tree = nx.Graph()\n self.small_tree.add_edge(1, 3)\n self.small_tree.add_edge(4, 3)\n self.small_tree.add_edge(2, 3)\n self.small_tree.add_edge(3, 5)\n self.small_tree.add_edge(5, 6)\n self.small_tree.add_edge(5, 7)\n self.small_tree.add_edge(6, 7)\n\n self.deterministic_graph = nx.Graph()\n self.deterministic_graph.add_edge(0, 1) # deg(0) = 1\n\n self.deterministic_graph.add_edge(1, 2) # deg(1) = 2\n\n self.deterministic_graph.add_edge(2, 3)\n self.deterministic_graph.add_edge(2, 4) # deg(2) = 3\n\n self.deterministic_graph.add_edge(3, 4)\n self.deterministic_graph.add_edge(3, 5)\n self.deterministic_graph.add_edge(3, 6) # deg(3) = 4\n\n self.deterministic_graph.add_edge(4, 5)\n self.deterministic_graph.add_edge(4, 6)\n self.deterministic_graph.add_edge(4, 7) # deg(4) = 5\n\n self.deterministic_graph.add_edge(5, 6)\n self.deterministic_graph.add_edge(5, 7)\n self.deterministic_graph.add_edge(5, 8)\n self.deterministic_graph.add_edge(5, 9) # deg(5) = 6\n\n self.deterministic_graph.add_edge(6, 7)\n self.deterministic_graph.add_edge(6, 8)\n self.deterministic_graph.add_edge(6, 9) # deg(6) = 6\n\n self.deterministic_graph.add_edge(7, 8)\n self.deterministic_graph.add_edge(7, 9) # deg(7) = 5\n\n self.deterministic_graph.add_edge(8, 9) # deg(8) = 4","def init_vertices(self):\n self.vertices = []\n for key in self.graph:\n self.vertices.append(self.Vertex(key, self.graph[key]))","def update(self):\r\n self.g = self.create_graph()","def clear_nastran(self):\n self.eid_map = {}\n self.nid_map = {}\n self.eid_to_nid_map = {}\n self.element_ids = None\n self.node_ids = None"],"string":"[\n \"def _reset_state(self):\\n # Directed graph, (u, v) => v depends on u. u, v are pairs of (rule_name, rule_dir_abs)\\n # Used for generating Topological Sort\\n self._rule_to_dependency_graph_adjlist = {}\\n self._topologically_sorted_build_rule_names = []\\n\\n # List of (dependency_name, dependency_dir_abs) for each build rule\\n self._rule_to_dependency_list = {}\\n\\n # Space for rough work :P\\n self._unresolved_commands = set()\",\n \"def ResetGraph(self):\\n self.nodes = []\\n self.edges = []\\n self.connections = []\\n Node.resetNodeCount()\\n Edge.resetEdgesCount()\",\n \"def reset_graph(self):\\n self.nodes = {}\\n self.add_node(self.initial_state)\\n self.add_node(self.final_state)\",\n \"def clear(self):\\r\\n self.nodes = collections.defaultdict(list)\\r\\n self.nodes_mapping = collections.defaultdict(list)\\r\\n self.edges = 0\\r\\n #self.children_length={}\\r\\n self.parents_length = collections.defaultdict(lambda : collections.defaultdict(int))\",\n \"def populate_graph(self):\\n if self.edges and self.vertices:\\n graph = Graph()\\n for edge in self.edges:\\n graph.add_edge(edge)\\n self.graph = graph\\n else:\\n print(\\\"Populate edges & vertices first, then populate graph!\\\")\",\n \"def reset_graph(self):\\n self.graph = OrderedDict()\",\n \"def _build_dependency_graph(self):\\n\\n #\\n # Find the binary roles\\n #\\n nodes, roles = self._find_roles()\\n\\n #\\n # Build the graph\\n #\\n working_list = list(set(nodes.keys()))\\n\\n setters = [b for b, r in roles.items() if Role.SETTER in r or Role.SETTER_GETTER in r]\\n\\n while working_list:\\n b = working_list[0]\\n working_list = working_list[1:]\\n\\n if nodes[b] not in self._graph:\\n self._graph[nodes[b]] = []\\n\\n # it's a root node\\n if Role.GETTER not in roles[b] and Role.SETTER_GETTER not in roles[b]:\\n nodes[b].set_root()\\n\\n # takes params from some other binary\\n else:\\n is_orphan = True\\n for setter in setters:\\n setter_strings_set = set(nodes[setter].role_strings)\\n node_strings_set = set(nodes[b].role_strings)\\n if setter_strings_set.intersection(node_strings_set):\\n if nodes[setter] not in self._graph:\\n self._graph[nodes[setter]] = []\\n self._graph[nodes[setter]].append(nodes[b])\\n is_orphan = False\\n\\n # mark orphans\\n if is_orphan:\\n nodes[b].set_orphan()\\n\\n # Clean up\\n for k, childs in self._graph.iteritems():\\n self._graph[k] = list(set(childs))\\n\\n # set leaves\\n for k, c in self._graph.iteritems():\\n if not c:\\n k.set_leaf()\\n\\n # post processing:\\n # remove those nodes that are not orphans\\n # and are not network parsers\\n\\n nodes = self.nodes\\n children = [c for x in self._graph.values() for c in x if x]\\n leafs_non_orphan = [n for n in nodes if n.leaf and not n.orphan]\\n seed_names = [x.split('/')[-1] for x in self._seed_bins]\\n spurious_nodes = [n for n in leafs_non_orphan if n not in children and n.bin.split('/')[-1] not in seed_names]\\n for to_rem in spurious_nodes:\\n del self._graph[to_rem]\",\n \"def _restoreGraph(self):\\n\\n # self.tempG = self.g.copy()\\n\\n if nx.is_directed(self.g):\\n self.tempG = nx.DiGraph(self.g)\\n else:\\n self.tempG = nx.Graph(self.g)\\n self.deletedEdges = []\\n self.deletedNodes = []\",\n \"def _reset_topological_order(self):\\n self._topological_order = self._input_nodes[:]\\n self.sorted = False\",\n \"def _create_connections(self):\\n self.predecessors = {}\\n self.successors = {}\\n for nd in self.nodes:\\n self.predecessors[nd.name] = []\\n self.successors[nd.name] = []\\n\\n for (nd_out, nd_in) in self.edges:\\n self.predecessors[nd_in.name].append(nd_out)\\n self.successors[nd_out.name].append(nd_in)\",\n \"def __init__(self):\\n self._adjacency_list = {\\n\\n }\",\n \"def _internal_build(self):\\n self.nodes = self.__tree.Nodes()\\n self.edges = self.__tree.Edges()\\n self.augmentedEdges = {}\\n for key, val in self.__tree.AugmentedEdges().items():\\n self.augmentedEdges[key] = list(val)\\n self.root = self.__tree.Root()\\n\\n seen = set()\\n self.branches = set()\\n\\n # Find all of the branching nodes in the tree, degree > 1\\n # That is, they appear in more than one edge\\n for e1, e2 in self.edges:\\n if e1 not in seen:\\n seen.add(e1)\\n else:\\n self.branches.add(e1)\\n\\n if e2 not in seen:\\n seen.add(e2)\\n else:\\n self.branches.add(e2)\\n\\n # The nodes that are not branches are leaves\\n self.leaves = set(self.nodes.keys()) - self.branches\\n self.leaves.remove(self.root)\",\n \"def _build_graph_general(self): \\n\\n #Find a canonical coloring scheme\\n #Each node has a color that is determined by the non-mapped aspects\\n nodecolors=set()\\n for nl in self.net.iter_node_layers():\\n nodecolors.add(self._slice_node_layer_not_allowed(nl))\\n nodecolors_sorted=sorted(list(nodecolors))\\n del nodecolors\\n self._assert_full_order(nodecolors_sorted)\\n self.colormap=dict( ((color,colorid) for colorid,color in enumerate(nodecolors_sorted) ))\\n\\n #each aux node has a color that is determined by the aspect\\n self.auxcolormap=dict( ((auxcolor, auxcolorid+len(self.colormap)) for auxcolorid,auxcolor in enumerate(sorted(self.asp)) ) )\\n\\n\\n #Add the underlying network\\n #node-layers:\\n for nl in self.net.iter_node_layers():\\n nlid=self._get_node_id(nl)\\n color=self._slice_node_layer_not_allowed(nl)\\n colorid=self.colormap[color]\\n self.add_node(nlid,colorid)\\n\\n #edges between node-layers:\\n for nl1 in self.net.iter_node_layers():\\n for nl2 in self.net[nl1]:\\n nl1id=self._get_node_id(nl1)\\n nl2id=self._get_node_id(nl2)\\n self.add_link(nl1id,nl2id)\\n\\n\\n #Add the auxiliary nodes and edges\\n #add the aux nodes\\n for a in self.asp:\\n for elayer in self.net.slices[a]:\\n auxid=self._get_auxnode_id( (a,elayer) )\\n auxcolorid=self.auxcolormap[a]\\n self.add_node(auxid,auxcolorid)\\n \\n #add the aux edges\\n for nl in self.net.iter_node_layers():\\n for a in self.asp:\\n nlid=self._get_node_id(nl)\\n auxid=self._get_auxnode_id( (a,nl[a]) )\\n self.add_link(nlid,auxid)\",\n \"def update(self):\\n\\n for node in self.nodes:\\n for edge in node.edges:\\n for i, edge_node in enumerate(edge.nodes):\\n if edge_node.id != node.id:\\n edge_node.add_edge(edge)\\n\\n return self\",\n \"def reset(self):\\n\\t\\tself.graph = OrderedDict()\\n\\t\\tself.bottoms = OrderedDict()\\n\\t\\tself.output_shape = OrderedDict()\\n\\t\\tself.cur_tensor = None\\n\\t\\tself.cur_id = None\\n\\t\\tself.tmp_list = []\\n\\t\\tself.log_init()\",\n \"def reset_graph(self):\\n raise NotImplementedError\",\n \"def complete_graph(self):\\n root_nodes = set()\\n\\n for name, a_block in self.wf['action'].items():\\n\\n a_block['name'] = name\\n\\n for n in a_block.get('needs', []):\\n if not self.wf['action'][n].get('next', None):\\n self.wf['action'][n]['next'] = set()\\n self.wf['action'][n]['next'].add(name)\\n\\n if not a_block.get('needs', None):\\n root_nodes.add(name)\\n\\n self.wf['root'] = root_nodes\",\n \"def start_new_graph(self):\\n self.nodes = {}\\n self.reset_graph()\",\n \"def populate_graph(self):\",\n \"def _clean_graph(self):\\n for entry_node in self._entry_nodes:\\n self._clean_graph_visit(entry_node.get_func_first_node(), {})\",\n \"def clear(self):\\n \\n self.node_set.clear()\\n self.prefix.clear()\\n self.suffix.clear()\\n self.num_node = 0\\n self.edges = 0\",\n \"def rebuild_graph_data(self, consolidator=None):\\n\\n if len(self.nodes) == 0:\\n raise DomainException(\\\"No nodes supplied to graph!\\\")\\n\\n if consolidator != None:\\n for node in self.nodes.values():\\n na = set()\\n nat = set()\\n no = set()\\n nr = set()\\n ni = set()\\n nir = set()\\n nrc = Counter()\\n for atype, attribute in node.attributes:\\n try:\\n atype = consolidator(atype)\\n except ConsolidatorException:\\n continue\\n na.add((atype, attribute))\\n nat.add(atype)\\n\\n for rtype, dest in node.outgoing_relations:\\n try:\\n rtype = consolidator(rtype)\\n except ConsolidatorException:\\n continue\\n no.add((rtype, dest))\\n nr.add(rtype)\\n nrc[rtype] += 1\\n \\n for rtype, pred in node.incoming_relations:\\n try:\\n rtype = consolidator(rtype)\\n except ConsolidatorException:\\n continue\\n ni.add((rtype, pred))\\n nir.add(rtype)\\n nrc[rtype] += 1\\n\\n #update values\\n node.attributes = na\\n node.outgoing_relations = no\\n node.incoming_relations = ni\\n node.rtypes = nr\\n node.i_rtypes = nir\\n node.atypes = nat\\n node.rtype_count = nrc\\n\\n # ==== compute member variables ====\\n self.usage_map = self.map_uses()\\n self.usage_counts = {x:len(y) for x,y in self.usage_map.items()}\\n self.rtype_vectors = self.index_rtypes()\\n self.node_vectors = self.index_nodes()\\n self.rkdtree_keys, _rvalues = zip(*self.rtype_vectors.items())\\n self.rkdtree = cKDTree(_rvalues)\\n self.nkdtree_keys, _nvalues = zip(*self.node_vectors.items())\\n self.nkdtree = cKDTree(_nvalues)\\n\\n # ==== precompute some vector constructs ====\\n for node in self.nodes.values():\\n node.compute_dicts(self)\\n\\n # ==== compute tf-idf weights for all nodes ====\\n\\n #calculate number of nodes containing rtype and \\n #find maximum frequency rtype for any single node\\n maxftd = 0\\n c2 = Counter()\\n for y in self.nodes.values():\\n for k,z in y.rtype_count.items():\\n c2[k] += 1\\n if z > maxftd:\\n maxftd = z\\n\\n #calculate augmented term frequency\\n tf = Counter()\\n for x,y in self.nodes.items():\\n for z,v in y.rtype_count.items():\\n tf[(x,z)] = 0.5 + 0.5*(v/maxftd)\\n\\n #calculate inverse document frequency\\n idf = Counter()\\n N = len(self.nodes)\\n for x in c2:\\n idf[x] = log(N / c2[x])\\n\\n tfidf = {}\\n for x,y in self.nodes.items():\\n for z in y.rtype_count:\\n tmp = tfidf.setdefault(x,{})\\n tmp[z] = tf[(x,z)] * idf[z]\\n\\n self.tfidf = tfidf\\n self.dirty = False\",\n \"def prepare(self):\\n if self._ready_nodes is not None:\\n raise ValueError(\\\"cannot prepare() more than once\\\")\\n\\n self._ready_nodes = [\\n i.node for i in self._node2info.values() if i.npredecessors == 0\\n ]\\n # ready_nodes is set before we look for cycles on purpose:\\n # if the user wants to catch the CycleError, that's fine,\\n # they can continue using the instance to grab as many\\n # nodes as possible before cycles block more progress\\n cycle = self._find_cycle()\\n if cycle:\\n raise CycleError(f\\\"nodes are in a cycle\\\", cycle)\",\n \"def _reset_gradients(self):\\n self.grad = None # gradient itself\\n self.grad_fn = None # functions to call for gradient\\n self.grad_expected = 0 # number of gradients expected from parents\\n self.grad_received = 0 # number of gradients received from parents\\n self.children = [] # children of node in graph\\n self.ctx = AutogradContext() # contexts for AutogradFunctions\",\n \"def _reset_traversal_state(self):\\n for n in self.nodes.values():\\n n.reset_traversal_state()\",\n \"def clean_local_memory(self):\\n self.namespaces = self._rdf_builder.namespaces\\n self.dataset = self._rdf_builder.dataset\\n\\n self.ontology_graph = self._rdf_builder.ontology_graph\\n self.instance_graph = self._rdf_builder.instance_graph\\n self.claim_graph = self._rdf_builder.claim_graph\\n self.perspective_graph = self._rdf_builder.perspective_graph\\n self.interaction_graph = self._rdf_builder.interaction_graph\",\n \"def _setup_graph(self):\\n pass\",\n \"def _setup_graph(self):\\n pass\",\n \"def init_graph(self):\\n import dgl\\n\\n adj_list = []\\n for rel_type in range(1, self.n_relations, 1):\\n edge_idxs = self.ckg.filter_edges(\\n lambda edge: edge.data[\\\"relation_id\\\"] == rel_type\\n )\\n sub_graph = (\\n dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True)\\n .adjacency_matrix(transpose=False, scipy_fmt=\\\"coo\\\")\\n .astype(\\\"float\\\")\\n )\\n rowsum = np.array(sub_graph.sum(1))\\n d_inv = np.power(rowsum, -1).flatten()\\n d_inv[np.isinf(d_inv)] = 0.0\\n d_mat_inv = sp.diags(d_inv)\\n norm_adj = d_mat_inv.dot(sub_graph).tocoo()\\n adj_list.append(norm_adj)\\n\\n final_adj_matrix = sum(adj_list).tocoo()\\n indices = torch.LongTensor([final_adj_matrix.row, final_adj_matrix.col])\\n values = torch.FloatTensor(final_adj_matrix.data)\\n adj_matrix_tensor = torch.sparse.FloatTensor(indices, values, self.matrix_size)\\n return adj_matrix_tensor.to(self.device)\",\n \"def reset_edges(self):\\n super().reset_edges()\\n\\n # If we're in default state, notheing to rest\\n if self._modified_weighted_adj_matrices is None:\\n return\\n\\n # Degrees are reset, so we need to reset the original weight scaling\\n if self.scale_weights and not self.scaling_skipped:\\n self._scale_weights_to_degree()\\n self._generate_weighted_adj_matrices()\\n else:\\n # No weight scaling so just load prev values from cache\\n self.weighted_adj_matrices = {**self.weighted_adj_matrices, **self._modified_weighted_adj_matrices}\\n self._modified_weighted_adj_matrices = None\",\n \"def setup_ant(self):\\n self.visited_nodes[1:] = []\\n self.actual_node = self.start_pos\",\n \"def __init__(self):\\n self._graph = DirectedGraph()\\n self._graph_copies = []\",\n \"def prepare_graph(\\n self,\\n adjacency,\\n weights,\\n weighted=False,\\n undirected=False,\\n force_dense=True,\\n noselfloop=True,\\n verbose=True,\\n ):\\n\\n # df_adj = pd.read_csv(in_folder + adj_name, index_col=0) # read adjacency file\\n print(\\\"\\\\nAdjacency shape: {0}\\\".format(adjacency.shape), flush=True)\\n\\n # create the graph adding nodes and edges\\n A = self.read_graph(\\n adj=adjacency,\\n weights=weights,\\n weighted=weighted,\\n undirected=undirected,\\n noselfloop=noselfloop,\\n verbose=verbose,\\n )\\n\\n nodes = list(A[0].nodes)\\n print(\\\"\\\\nNumber of nodes =\\\", len(nodes), flush=True)\\n print(\\\"Number of layers =\\\", len(A), flush=True)\\n if verbose:\\n self.print_graph_stat(A)\\n\\n # save the multilayer network in a tensor with all layers\\n if force_dense:\\n B = self.build_B_from_A(A, nodes=nodes)\\n else:\\n B = self.build_sparse_B_from_A(A)\\n\\n return A, B, nodes\",\n \"def formAdjacencyMatrix(self):\\n self.adjacencyMatrix = dict()\\n for i in self.node:\\n self.adjacencyMatrix[i] = dict()\\n for j in self.node:\\n self.adjacencyMatrix[i][j] = 0\\n \\n for ij in self.link:\\n self.adjacencyMatrix[self.link[ij].tail][self.link[ij].head] = 1\",\n \"def finalize(self):\\n # Establish forward/reverse star lists, set travel times to free-flow\\n for i in self.node:\\n self.node[i].forwardStar = list()\\n self.node[i].reverseStar = list()\\n \\n for ij in self.link:\\n self.node[self.link[ij].tail].forwardStar.append(ij)\\n self.node[self.link[ij].head].reverseStar.append(ij)\\n self.link[ij].cost = self.link[ij].freeFlowTime + self.link[ij].length * self.distanceFactor + self.link[ij].toll * self.tollFactor\\n self.link[ij].flow = 0\\n \\n for OD in self.ODpair:\\n self.ODpair[OD].leastCost = 0\",\n \"def reset_edges(self):\\n\\n # Ensure original edges are stored in cache, otherwise nothing to do.\\n if self._modified_edges is None or self._weighted_modified_edges is None:\\n return\\n\\n # Restore the former value from cache\\n self.adj_matrices = {**self.adj_matrices, **self._modified_edges}\\n self.degree_weighted_matrices = {**self.degree_weighted_matrices, **self._weighted_modified_edges}\\n self.in_degree = {**self.in_degree, **self._orig_in_degree}\\n self.out_degree = {**self.out_degree, **self._orig_out_degree}\\n\\n # Reset the edge and degree cache\\n self._modified_edges = None\\n self._weighted_modified_edges = None\\n self._orig_in_degree = dict()\\n self._orig_out_degree = dict()\",\n \"def _refine_matrix_with_additional_connections(self):\\n new_graph = self.graph.copy()\\n for node in tqdm.tqdm(self.graph.nodes(), disable=not self.verbose):\\n if self.graph.node[node][\\\"type\\\"] == \\\"hashtag\\\":\\n for neighbour in self.graph.neighbors(node):\\n if self.graph.node[neighbour][\\\"type\\\"] == \\\"username\\\":\\n for other_node in self.graph.neighbors(neighbour):\\n if self.graph.node[other_node][\\\"type\\\"] == \\\"hashtag\\\" \\\\\\n and not self.graph.has_edge(node, other_node) \\\\\\n and not node == other_node:\\n new_graph.add_edge(node, other_node)\\n self.graph = new_graph\",\n \"def refresh_metadata(self):\\n #self.node_index = None\\n #self.edge_index = None\\n #self._calc_edge_centers = False\\n #self._calc_cell_centers = False\\n #self._calc_vcenters = False\\n self._node_to_edges = None\\n self._node_to_cells = None\",\n \"def build_graph(self):\\n for each_list in self.lab.look():\\n vertice = self._add_vertice(each_list)\\n if vertice:\\n self.unvisited.add(vertice)\\n self.graph.addEdge((self.current, vertice))\\n \\n self.unvisited -= self.visited\\n self._connect_neighbours()\",\n \"def fix_graph(self,graph):\\n graph_compleate_reachable = False\\n while not graph_compleate_reachable:\\n not_reachable_in ,not_reachable_out = self.not_reachable(graph)\\n for n in not_reachable_in:\\n graph.add_edge(self.random.randint(0,n-1),n)\\n for n in not_reachable_out:\\n graph.add_edge(n,self.random.randint(n+1, self.nodes-1))\\n graph_compleate_reachable = len(not_reachable_in)==0 and len(not_reachable_out)==0\\n return graph\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 2)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 4)\\n self.small_tree.add_edge(1, 4)\\n self.small_tree.add_edge(2, 4)\\n self.small_tree.add_edge(4, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(1, 2)\\n self.deterministic_graph.add_edge(1, 3)\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(2, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(3, 6)\\n self.deterministic_graph.add_edge(5, 6)\",\n \"def make_complete_graph(num_nodes):\\n complete_digraph = {}\\n if num_nodes > 0 and type(num_nodes) == int:\\n neighbors = set([idx for idx in range(num_nodes)])\\n for idx in range(num_nodes):\\n complete_digraph[idx] = neighbors.copy() #creates adjacency set\\n complete_digraph[idx].remove(idx) # pop out self-loop \\n return complete_digraph\",\n \"def _pre_setup(self):\\n super()._pre_setup()\\n self.create_connections_indices()\",\n \"def _pre_setup(self):\\n super()._pre_setup()\\n self.create_connections_indices()\",\n \"def _pre_setup(self):\\n super()._pre_setup()\\n self.create_connections_indices()\",\n \"def legalize_graph(gm: pippy.fx.GraphModule) -> pippy.fx.GraphModule:\\n indeg = {node: 0 for node in gm.graph.nodes}\\n new_graph = pippy.fx.Graph()\\n # Track how many unfulfilled dependencies each node has\\n for node in gm.graph.nodes:\\n for user in node.users:\\n indeg[user] += 1\\n queue: collections.deque = collections.deque()\\n # Add all nodes with no dependencies to the queue\\n for node in gm.graph.nodes:\\n if indeg[node] == 0:\\n queue.append(node)\\n env: Dict[pippy.fx.Node, pippy.fx.Node] = {}\\n # Pop nodes from the queue, and add nodes that have had all their\\n # dependencies fulfilled\\n while len(queue) > 0:\\n cur = queue.popleft()\\n env[cur] = new_graph.node_copy(cur, lambda x: env[x])\\n for user in cur.users:\\n indeg[user] -= 1\\n if indeg[user] == 0:\\n queue.append(user)\\n # If the new graph's size is not as large as the old one, then there must be\\n # a cycle (i.e. some node's dependencies were not satisfied.)\\n if len(new_graph.nodes) < len(gm.graph.nodes):\\n raise RuntimeError(f\\\"Input graph has cycles, unable to add {[node for node in indeg if indeg[node] != 0]}\\\")\\n gm.graph = new_graph\\n return gm\",\n \"def clear(self):\\n self.nodes = list()\\n self.inputs = list()\\n self.nodes += [self]\",\n \"def setup(self):\\n switches, links = self.generateTopology()\\n self.graph = KytosGraph()\\n self.graph.clear()\\n self.graph.update_nodes(switches)\\n self.graph.update_links(links)\\n self.graph.set_path_fun(nx.shortest_simple_paths)\",\n \"def _build_graph(self):\\n pass\",\n \"def __init__(self, nodes=None, edges=None):\\n self._nodes = []\\n self.nodes = nodes\\n self._edges = []\\n self.edges = edges\\n self._create_connections()\\n self._sorted_nodes = None\\n self._node_wip = []\",\n \"def clear(self):\\n self.mismatch_error = None\\n self.pt_outs = None\\n self._onnx_graph = None\\n self.upper_graph_info = None\\n self.lower_graph_info = None\",\n \"def reset_visited(self):\\n self.__visited = False\",\n \"def __init__(self):\\n\\t\\tself.edges = defaultdict(list)\\n\\t\\tself.weights = {}\\n\\t\\tself.connections = {}\",\n \"def __init__(self, directed=True):\\n self.nodes = set()\\n self.edges = []\\n self.directed = directed\",\n \"def _initilise_graph_db(self):\\n for collector in self.collectors:\\n collector.init_graph_db()\",\n \"def processLoadedLinkNodes(self, isLoadingModel):\\r\\n \\r\\n if(isLoadingModel):\\r\\n #=====================================================\\r\\n # Hierarchical structure maintenance (See HierarchicalASGNode.py)\\r\\n #=====================================================\\r\\n for node in self.newLinkNodeQueue:\\r\\n # NOTE: node.in_connections_ could be > 1 in the metamodel since \\r\\n # AToM3 considers meta-model relations to have hierarchy active!\\r\\n # Of course I didn't intend this, and it has no meaning, so ignore!\\r\\n if(node.isHierarchicalLink() and len(node.in_connections_) == 1):\\r\\n for child in node.out_connections_:\\r\\n child._setHierParent(node.in_connections_[0])\\r\\n node.in_connections_[0]._addHierChildrenList(node.out_connections_) \\r\\n \\r\\n\\r\\n #=======================================================================\\r\\n # QOCA Constraints\\r\\n # Only do this if we are actually using QOCA in the first place...\\r\\n #=======================================================================\\r\\n if(isNotUsingQoca()): \\r\\n return\\r\\n \\r\\n for node in self.newLinkNodeQueue:\\r\\n # Apply any QOCA linear constraints\\r\\n # For graph grammars, graphObject_ may be none, so GGrule.py will also\\r\\n # trigger QOCA in its replaceSides() method\\r\\n if(hasattr(node, 'QOCA') and not node.__dict__.has_key('QOCA')):\\r\\n # Make sure that node has a method called 'QOCA', not an attribute\\r\\n node.QOCA(None)\\r\\n \\r\\n # Clean out the queue\\r\\n self.newLinkNodeQueue = []\",\n \"def clean(self):\\n\\t\\tfor v in self:\\n\\t\\t\\tv.reset_distance()\\n\\t\\t\\tv.reset_predecessor()\\n\\t\\t\\tv.reset_visited()\",\n \"def __init__(self):\\n self.adjList = {}\",\n \"def reset_nn(self): # Clear current network\\n self.weights = np.zeros((p.num_rovers, self.n_weights))\\n self.in_layer = np.zeros((p.num_rovers, self.n_inputs))\\n self.hid_layer = np.zeros((p.num_rovers, self.n_nodes))\\n self.out_layer = np.zeros((p.num_rovers, self.n_outputs))\",\n \"def _init_nodes(self, nodes):\\n attributes = self.get_node_attributes()\\n for node in nodes:\\n if not self._is_node_added(node):\\n self._nodes.append(self._get_node_as_dictionary(node, attributes))\",\n \"def topo_sort(self):\\n # TODO: detect cycles\\n self.find_reachable_nodes()\\n # save list of nodes in topo order\\n self.nodes = []\\n # assign each node an id field incrementally\\n cur_id = 0\\n # count visited outgoing edges for each node\\n unvisited = {}\\n for nid, node in list(self.found.items()):\\n unvisited[nid] = node.nout\\n queue = [self.root]\\n #print >>sys.stderr, '+++'\\n while queue:\\n # take off nodes whose all outgoing edges are visited from\\n # queue head\\n node = queue.pop(0)\\n self.nodes.append(node)\\n node.hg = self\\n node.id = cur_id\\n cur_id += 1\\n for edge in node.incoming:\\n edge.hg = self\\n for tailnode in edge.tail:\\n #print >>sys.stderr, tailnode\\n unvisited[id(tailnode)] -= 1\\n if unvisited[id(tailnode)] == 0:\\n queue.append(tailnode)\\n self.sanity_check()\\n self.tasks_done.add('topo_sort')\",\n \"def abort(self):\\n for node in self.dep_graph.nodes_iter():\\n role = self.roles[node]\\n role.new_rep = role.cur_rep\\n role.new_hosts = list(role.cur_hosts)\\n for edge in self.dep_graph.edges_iter():\\n edge_data = self.dep_graph.get_edge_data(*edge)\\n edge_data['new_weight'] = edge_data['cur_weight']\",\n \"def create_initial_graph(self):\\n # Initialise weights\\n for link in self.gene_links:\\n link.weight = random.uniform(weight_init_min, weight_init_max)\\n # Initialise biases\\n for node in self.gene_nodes:\\n node.bias = random.uniform(bias_init_min, bias_init_max)\\n if node.can_modify:\\n node.act_func = self.act_set.get_random_activation_func()\\n if node.act_func in [activations.gaussian, activations.sin]:\\n if node.act_func.__name__[0] == \\\"g\\\":\\n node.freq += random.uniform(-guass_freq_adjust, guass_freq_adjust)\\n elif node.act_func.__name__[0] == \\\"s\\\":\\n node.freq += random.uniform(-sin_freq_adjust, sin_freq_adjust)\\n node.amp += random.uniform(-func_amp_adjust, func_amp_adjust)\\n node.vshift += random.uniform(-func_vshift_adjust, func_vshift_adjust)\",\n \"def reset(self):\\n self.visited = set()\\n del self.targets[0]\",\n \"def _init_graph(self):\\n self.G = nx.Graph()\\n self.G.add_nodes_from([1,2,3,4,5])\\n self.G.add_edges_from([(1,2),(2,3),(2,4)\\\\\\n ,(2,5),(3,4),(4,5)])\",\n \"def build_graph(self):\\n\\t\\tself._create_placeholders()\\n\\t\\tself._create_embedding()\\n\\t\\tself._create_recurrent_layers()\\n\\t\\tself._create_de_embedding()\\n\\t\\tself._create_loss()\\n\\t\\tself._create_optimizer()\\n\\t\\tself._create_summaries()\",\n \"def build_graph(self):\\n self._reset_iterator_memory()\\n self._construct_graph_handler()\\n assert self.graph_handler\\n for rxn_id in self.graph_handler.get_valid_reaction_ids():\\n rxn = db.Reaction(rxn_id, self._reactions)\\n self.graph_handler.add_rxn(rxn)\",\n \"def topology_complete(self):\\n\\t\\tfor i in range(len(self.sites) - 1):\\n\\t\\t\\tfor j in range(i + 1, len(self.sites)):\\n\\t\\t\\t\\tself.sites[i].neighbors.append(self.sites[j])\\n\\t\\t\\t\\tself.sites[j].neighbors.append(self.sites[i])\",\n \"def __init__(self):\\n self.data_graph = self._initialise_data()\\n\\n self.messages_sent = []\\n self.messages_received = []\\n\\n self.answered_true = set()\\n self.implied_true = set()\\n\\n self.current_subgraph = set()\",\n \"def clear(self):\\n self._nodes = { }\\n self._arcs = set()\",\n \"def solve(self):\\n self.left -= len(self.nodes)\\n \\n def depths(x,depth = 0):\\n depth+=1\\n for y in self.graph[x]:\\n if y in self.nodes:\\n self.nodes.remove(y)\\n depth = depths(y,depth)\\n return depth\\n \\n while len(self.nodes):\\n x = self.nodes.pop()\\n self.firstGen.append(depths(x))\\n #print self.graph\\n #print self.nodes\\n #print self.firstGen\",\n \"def __initialize_connection_strengths(G):\\n G_prime = G.__deepcopy__() # construct a deepcopy of the graph\\n # for every vertex in the graph, initialize the connection strength to zero\\n for node in G_prime.get_nodeset(): node.add_attribute(StoerWagner.CONNECTION_STRENGTH_ATTRIBUTE, float(0))\\n return G_prime # return the new graph\",\n \"def _initializeAdjacencyList(self):\\n\\n if self.comm.rank == 0:\\n # First, create a dictionary of common edges shared by components\\n edgeToFace = {}\\n for elemID in self.bdfInfo.elements:\\n elemInfo = self.bdfInfo.elements[elemID]\\n elemConn = elemInfo.nodes\\n compID = self.meshLoader.nastranToTACSCompIDDict[elemInfo.pid]\\n nnodes = len(elemConn)\\n if nnodes >= 2:\\n for j in range(nnodes):\\n nodeID1 = elemConn[j]\\n nodeID2 = elemConn[(j + 1) % nnodes]\\n\\n if nodeID1 < nodeID2:\\n key = (nodeID1, nodeID2)\\n else:\\n key = (nodeID2, nodeID1)\\n\\n if key not in edgeToFace:\\n edgeToFace[key] = [compID]\\n elif compID not in edgeToFace[key]:\\n edgeToFace[key].append(compID)\\n\\n # Now we loop back over each element and each edge. By\\n # using the edgeToFace dictionary, we can now determine\\n # which components IDs (jComp) are connected to the\\n # current component ID (iComp).\\n self.adjacentComps = []\\n\\n for edgeKey in edgeToFace:\\n if len(edgeToFace[edgeKey]) >= 2:\\n for i, iComp in enumerate(edgeToFace[edgeKey][:-1]):\\n for jComp in edgeToFace[edgeKey][i + 1 :]:\\n if iComp < jComp:\\n dvKey = (iComp, jComp)\\n else:\\n dvKey = (jComp, iComp)\\n if dvKey not in self.adjacentComps:\\n self.adjacentComps.append(dvKey)\\n\\n else:\\n self.adjacentComps = None\\n\\n # Wait for root\\n self.comm.barrier()\",\n \"def _update_dead_nodes(self) -> None:\\n with self._dead_node_lock:\\n self._dead_nodes = self._seen_nodes - self._running_nodes\",\n \"def cfdProcessNodeTopology(self):\\r\\n self.nodeElements = self.cfdInvertConnectivity(self.elementNodes)\\r\\n self.nodeFaces = self.cfdInvertConnectivity(self.faceNodes)\",\n \"def direct_network(self):\\n #print list(self.get_subgraphs())\\n graphs = [self._depth_first_directed(g) for g in self.get_subgraphs()]\\n self._network = reduce(lambda a, b: nx.union(a, b), graphs)\",\n \"def fit(self, adjacency: Union[sparse.csr_matrix, np.ndarray], seeds: Union[np.ndarray, dict] = None) \\\\\\n -> 'Propagation':\\n adjacency = check_format(adjacency)\\n n = adjacency.shape[0]\\n index_seed, index_remain, labels_seed = self._instanciate_vars(adjacency, seeds)\\n\\n if self.node_order == 'random':\\n np.random.shuffle(index_remain)\\n elif self.node_order == 'decreasing':\\n index = np.argsort(-adjacency.T.dot(np.ones(n))).astype(np.int32)\\n index_remain = index[index_remain]\\n elif self.node_order == 'increasing':\\n index = np.argsort(adjacency.T.dot(np.ones(n))).astype(np.int32)\\n index_remain = index[index_remain]\\n\\n labels = -np.ones(n, dtype=np.int32)\\n labels[index_seed] = labels_seed\\n labels_remain = np.zeros_like(index_remain, dtype=np.int32)\\n\\n indptr = adjacency.indptr.astype(np.int32)\\n indices = adjacency.indices.astype(np.int32)\\n if self.weighted:\\n data = adjacency.data.astype(np.float32)\\n else:\\n data = np.ones(n, dtype=np.float32)\\n\\n t = 0\\n while t < self.n_iter and not np.array_equal(labels_remain, labels[index_remain]):\\n t += 1\\n labels_remain = labels[index_remain].copy()\\n labels = np.asarray(vote_update(indptr, indices, data, labels, index_remain))\\n\\n membership = membership_matrix(labels)\\n membership = normalize(adjacency.dot(membership))\\n\\n self.labels_ = labels\\n self.membership_ = membership\\n\\n return self\",\n \"def reset(self):\\n super(CheckMayaAbstract, self).reset()\\n self.errorNodes = list()\\n self._errorDict = {}\",\n \"def make_complete_graph(num_nodes):\\n xgraph = {} #Create a Blank Dict\\n if num_nodes - 1 < 0: # checks to see if the num_nodes is less then 0 (negative number) if it is return empty graph (dict). Could probably combine the If statments for negative nodes and 1 node together\\n return xgraph\\n if num_nodes - 1 == 0: # If the number of nodes is 1 or returns a one node dict because there are no edges to compute\\n xgraph[0] = set([]) # creates a dict that represents a single node graph as per the requirement\\n return xgraph # the empty Graph\\n else:\\n for base_node in range(num_nodes): # This portion starts the build phase. for each node it will compute the theretical maximum amount of edges\\n xlist = set([]) # defines an empty list. We first build a list for each node and the append to a dict. This list is erased with each iteration\\n #print base_node # testing - REMOVE\\n for edge_node in range(num_nodes):\\n #print edge_node # testing - REMOVE\\n if edge_node != base_node: #No Looping is allowed for this project. Therefor we check to insure the we are not counting a self node connection (edge_node NOT equal base_node)\\n xlist.add(edge_node) # Populating list that will be added to dict\\n\\n xgraph[base_node] = xlist # Appending created list to the dict\\n\\n return xgraph # returning populated dict\",\n \"def make_complete_graph(num_nodes):\\n xgraph = {} #Create a Blank Dict\\n if num_nodes - 1 < 0: # checks to see if the num_nodes is less then 0 (negative number) if it is return empty graph (dict). Could probably combine the If statments for negative nodes and 1 node together\\n return xgraph\\n if num_nodes - 1 == 0: # If the number of nodes is 1 or returns a one node dict because there are no edges to compute\\n xgraph[0] = set([]) # creates a dict that represents a single node graph as per the requirement\\n return xgraph # the empty Graph\\n else:\\n for base_node in range(num_nodes): # This portion starts the build phase. for each node it will compute the theretical maximum amount of edges\\n xlist = set([]) # defines an empty list. We first build a list for each node and the append to a dict. This list is erased with each iteration\\n #print base_node # testing - REMOVE\\n for edge_node in range(num_nodes):\\n #print edge_node # testing - REMOVE\\n if edge_node != base_node: #No Looping is allowed for this project. Therefor we check to insure the we are not counting a self node connection (edge_node NOT equal base_node)\\n xlist.add(edge_node) # Populating list that will be added to dict\\n\\n xgraph[base_node] = xlist # Appending created list to the dict\\n\\n return xgraph # returning populated dict\",\n \"def initGraphs(self):\\n \\n self.graph = ConjunctiveGraph()\\n # Create a separate graph for annotations\\n self.annotationGraph = ConjunctiveGraph()\\n \\n self.log.debug('Adding namespaces to graphs')\\n # Bind namespaces to graphs\\n for namespace in self.namespaces:\\n self.graph.namespace_manager.bind(namespace, self.namespaces[namespace])\\n\\n # Same for annotation graph\\n for namespace in self.annotationNamespaces:\\n self.annotationGraph.namespace_manager.bind(namespace, self.annotationNamespaces[namespace])\\n \\n # Add schema information\\n self.log.debug('Adding some schema information (dimension and measure properties) ')\\n self.addDataCellProperty()\\n\\n # Add dimensions \\n self.graph.add((self.namespaces['tablink']['dimension'], RDF.type, self.namespaces['qb']['DimensionProperty']))\\n \\n #self.graph.add((self.namespaces['tablink']['label'], RDF.type, RDF['Property']))\",\n \"def clean_edges(self):\\n for from_node in self.all_nodes():\\n for to_node in self.all_nodes():\\n if from_node == to_node:\\n continue\\n dup = list(filter(lambda x: x.from_node == from_node and x.to_node == to_node, self.edges))\\n if len(dup) > 1:\\n for d in dup[1:]:\\n self.edges.remove(d)\",\n \"def __init__(self, nodes=[], edges=[], connections=[], directed=False, isNetwork=False):\\n Node.count=0\\n Edge.count=0\\n self.nodes = [n for n in nodes]\\n self.edges = [e for e in edges]\\n self.connections = [(a, b) for (a, b) in connections]\\n self.isDirected = directed\\n self.isNetwork = isNetwork\",\n \"def _CreateAdjacencyListGraph(self):\\n graph = dict()\\n for nodes in self.nodes:\\n graph[nodes[0]] = set()\\n for edges in self.edges:\\n graph[edges[0]].add(edges[1])\\n return graph\",\n \"def reset(self):\\n self._clusters = {}\\n self._clusters_val = {}\\n self._centroids = {}\\n self.store()\",\n \"def build_graph(self):\\n pass\",\n \"def clean_edges(self):\",\n \"def reset_weight_zero(self):\\n self.node_embedding = np.random.uniform(low=-0.5, high=0.5, size=(self.vocab_size, self.layer1_size)).astype(\\n np.float32)\\n self.context_embedding = np.zeros((self.vocab_size, self.layer1_size), dtype=np.float32)\\n\\n self.centroid = np.zeros((self.k, self.layer1_size), dtype=np.float32)\\n self.covariance_mat = np.zeros((self.k, self.layer1_size, self.layer1_size), dtype=np.float32)\\n self.inv_covariance_mat = np.zeros((self.k, self.layer1_size, self.layer1_size), dtype=np.float32)\\n self.pi = np.zeros((self.vocab_size, self.k), dtype=np.float32)\\n log.info(\\\"reset communities data| k: {}\\\".format(self.k))\",\n \"def init_adjacency(self, A):\\n A[A==0] = self.INFINITY\",\n \"def add_resets_edges(graph, start):\\n for node in graph.nodes:\\n neighbors = list(graph[node])\\n if neighbors == [node]:\\n graph.add_edge(node, start, label=\\\"RESET / \\\")\",\n \"def transition(self):\\n for node in self.net.nodes():\\n if node not in self.evidence:\\n self.update_node(node)\",\n \"def __init__(self):\\n\\t\\tself.edges = defaultdict(list)\\n\\t\\tself.weights = {}\",\n \"def __init__(self):\\n\\t\\tself.edges = defaultdict(list)\\n\\t\\tself.weights = {}\",\n \"def _generate_graph(self) -> None:\\n self.g_ = nx.random_partition_graph(list(self._community_sizes),\\n p_in=self.community_p_in,\\n p_out=self.community_p_out,\\n seed=self.seed)\\n\\n for _, nv in self.g_.nodes.data():\\n nv[\\\"infected\\\"] = 0\\n nv[\\\"immune\\\"] = False\\n nv[\\\"alive\\\"] = True\\n nv[\\\"_edges\\\"] = []\\n nv[\\\"isolated\\\"] = False\\n nv[\\\"mask\\\"] = 0.0\",\n \"def generate_full_adj(self):\\n edges = np.zeros(shape=(self.n_balls, self.n_balls))\\n row_idx = 0 # start filling adjacency mat from root node\\n col_idx = 1 # skip the root node and start from 2nd node\\n for l in range(self.nl):\\n for n in range(self.nn[l]):\\n edges[row_idx, col_idx:col_idx + self.nc[l]] = 1\\n # Increase counters after filling connections for a parent node\\n col_idx += self.nc[l]\\n row_idx += 1\\n return edges\",\n \"def update_node2edge(self):\\n self.node2edge = {e.child : e for e in self.edge}\\n childrenset = set(self.node2edge.keys())\\n rootset = set(e.parent for e in self.edge).difference(childrenset)\\n if len(rootset) > 1:\\n raise Warning(\\\"there should be a single root: \\\" + str(rootset))\\n if len(rootset) == 0:\\n raise Exception(\\\"there should be at least one root!\\\")\\n self.root = rootset.pop()\",\n \"def setUp(self):\\n self.complete = nx.Graph()\\n self.complete.add_edge(1, 2)\\n self.complete.add_edge(2, 3)\\n self.complete.add_edge(1, 3)\\n\\n self.small_tree = nx.Graph()\\n self.small_tree.add_edge(1, 3)\\n self.small_tree.add_edge(4, 3)\\n self.small_tree.add_edge(2, 3)\\n self.small_tree.add_edge(3, 5)\\n self.small_tree.add_edge(5, 6)\\n self.small_tree.add_edge(5, 7)\\n self.small_tree.add_edge(6, 7)\\n\\n self.deterministic_graph = nx.Graph()\\n self.deterministic_graph.add_edge(0, 1) # deg(0) = 1\\n\\n self.deterministic_graph.add_edge(1, 2) # deg(1) = 2\\n\\n self.deterministic_graph.add_edge(2, 3)\\n self.deterministic_graph.add_edge(2, 4) # deg(2) = 3\\n\\n self.deterministic_graph.add_edge(3, 4)\\n self.deterministic_graph.add_edge(3, 5)\\n self.deterministic_graph.add_edge(3, 6) # deg(3) = 4\\n\\n self.deterministic_graph.add_edge(4, 5)\\n self.deterministic_graph.add_edge(4, 6)\\n self.deterministic_graph.add_edge(4, 7) # deg(4) = 5\\n\\n self.deterministic_graph.add_edge(5, 6)\\n self.deterministic_graph.add_edge(5, 7)\\n self.deterministic_graph.add_edge(5, 8)\\n self.deterministic_graph.add_edge(5, 9) # deg(5) = 6\\n\\n self.deterministic_graph.add_edge(6, 7)\\n self.deterministic_graph.add_edge(6, 8)\\n self.deterministic_graph.add_edge(6, 9) # deg(6) = 6\\n\\n self.deterministic_graph.add_edge(7, 8)\\n self.deterministic_graph.add_edge(7, 9) # deg(7) = 5\\n\\n self.deterministic_graph.add_edge(8, 9) # deg(8) = 4\",\n \"def init_vertices(self):\\n self.vertices = []\\n for key in self.graph:\\n self.vertices.append(self.Vertex(key, self.graph[key]))\",\n \"def update(self):\\r\\n self.g = self.create_graph()\",\n \"def clear_nastran(self):\\n self.eid_map = {}\\n self.nid_map = {}\\n self.eid_to_nid_map = {}\\n self.element_ids = None\\n self.node_ids = None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65324295","0.63045096","0.6301467","0.6183333","0.6102479","0.6050887","0.6023047","0.5987564","0.5974988","0.58748007","0.5874257","0.58559334","0.5806248","0.5778933","0.5771964","0.5762822","0.57474977","0.57438475","0.57006","0.5695009","0.56947756","0.5658297","0.5654165","0.56452036","0.56430835","0.5635908","0.56318253","0.56318253","0.5621224","0.56173503","0.5595307","0.55855775","0.55817443","0.5577678","0.557441","0.554981","0.5540656","0.5532914","0.5531942","0.5516941","0.55155355","0.5508153","0.5497178","0.5497178","0.5497178","0.54649365","0.546345","0.54585266","0.54529124","0.5439693","0.5429664","0.5423405","0.5422416","0.54088396","0.54016113","0.5399264","0.5392605","0.5391486","0.5367117","0.5363376","0.5363359","0.5342555","0.53364915","0.53265774","0.5322334","0.53220356","0.5321241","0.53210264","0.5310078","0.5301045","0.5292643","0.52767885","0.52759117","0.5266936","0.5263736","0.5247962","0.5243319","0.5237699","0.52289337","0.52289337","0.5223753","0.52194643","0.521289","0.52058244","0.5203413","0.5202457","0.51980627","0.5195228","0.5193464","0.5192745","0.51904464","0.5189851","0.5189851","0.5188068","0.5186776","0.5186402","0.51830775","0.5180551","0.51776546","0.51723"],"string":"[\n \"0.65324295\",\n \"0.63045096\",\n \"0.6301467\",\n \"0.6183333\",\n \"0.6102479\",\n \"0.6050887\",\n \"0.6023047\",\n \"0.5987564\",\n \"0.5974988\",\n \"0.58748007\",\n \"0.5874257\",\n \"0.58559334\",\n \"0.5806248\",\n \"0.5778933\",\n \"0.5771964\",\n \"0.5762822\",\n \"0.57474977\",\n \"0.57438475\",\n \"0.57006\",\n \"0.5695009\",\n \"0.56947756\",\n \"0.5658297\",\n \"0.5654165\",\n \"0.56452036\",\n \"0.56430835\",\n \"0.5635908\",\n \"0.56318253\",\n \"0.56318253\",\n \"0.5621224\",\n \"0.56173503\",\n \"0.5595307\",\n \"0.55855775\",\n \"0.55817443\",\n \"0.5577678\",\n \"0.557441\",\n \"0.554981\",\n \"0.5540656\",\n \"0.5532914\",\n \"0.5531942\",\n \"0.5516941\",\n \"0.55155355\",\n \"0.5508153\",\n \"0.5497178\",\n \"0.5497178\",\n \"0.5497178\",\n \"0.54649365\",\n \"0.546345\",\n \"0.54585266\",\n \"0.54529124\",\n \"0.5439693\",\n \"0.5429664\",\n \"0.5423405\",\n \"0.5422416\",\n \"0.54088396\",\n \"0.54016113\",\n \"0.5399264\",\n \"0.5392605\",\n \"0.5391486\",\n \"0.5367117\",\n \"0.5363376\",\n \"0.5363359\",\n \"0.5342555\",\n \"0.53364915\",\n \"0.53265774\",\n \"0.5322334\",\n \"0.53220356\",\n \"0.5321241\",\n \"0.53210264\",\n \"0.5310078\",\n \"0.5301045\",\n \"0.5292643\",\n \"0.52767885\",\n \"0.52759117\",\n \"0.5266936\",\n \"0.5263736\",\n \"0.5247962\",\n \"0.5243319\",\n \"0.5237699\",\n \"0.52289337\",\n \"0.52289337\",\n \"0.5223753\",\n \"0.52194643\",\n \"0.521289\",\n \"0.52058244\",\n \"0.5203413\",\n \"0.5202457\",\n \"0.51980627\",\n \"0.5195228\",\n \"0.5193464\",\n \"0.5192745\",\n \"0.51904464\",\n \"0.5189851\",\n \"0.5189851\",\n \"0.5188068\",\n \"0.5186776\",\n \"0.5186402\",\n \"0.51830775\",\n \"0.5180551\",\n \"0.51776546\",\n \"0.51723\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7615647"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":945,"numItemsPerPage":100,"numTotalItems":95772,"offset":94500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTUwMTY4OSwianRpIjoiMzZlY2QzYTYtZTliYS00NDI0LWEzNzYtOTc2M2I0ZTliNzZiIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU1MDUyODksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.Jb4iqlAH0Gw122MVh9aYZH39nuz4YrRbTzG0uys1jnubPa26xfPXtXJNp3bUUjM591YCoKkNtjaEfzm-qceaBg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Ensure encoder produces expected encoded output. | def test_encoder(self):
from sosbeacon.utils import number_encode
number = 123
encoded = number_encode(number)
self.assertEqual(encoded, 'b6') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_encode(self):\n pass # TODO(tlarsen)",
"def _define_encoder(self):\n raise NotImplementedError",
"def test_encoders(encoder):\n assert encoding.decode(None, encoder) is None\n assert encoding.encode(None, encoder) is None\n\n assert b\"\" == encoding.decode(b\"\", encoder)\n\n a... | [
"0.7567242",
"0.70699394",
"0.69895875",
"0.69377136",
"0.68090206",
"0.6634779",
"0.65108114",
"0.64991826",
"0.64129996",
"0.6411713",
"0.64028895",
"0.63984114",
"0.63530314",
"0.6349346",
"0.6330712",
"0.6303407",
"0.6277781",
"0.6252162",
"0.6197097",
"0.61829704",
"0.61... | 0.6834381 | 4 |
Ensure decoded correctly decodes a known encoded number. | def test_decoder(self):
from sosbeacon.utils import number_decode
encoded = 'b6'
number = number_decode(encoded)
self.assertEqual(number, 123) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode(self, number: int) -> typing.Union[int, str]:\n return number",
"def test_decode_numbers(self, number, base, expected):\n self.assertEqual(positional.decode(number, base), expected)",
"def test_decoding_non_str_fails(self):\n self.assertRaises(DecodingError, base62.to_decimal, s... | [
"0.7170725",
"0.70083755",
"0.66928744",
"0.65111727",
"0.646559",
"0.6452943",
"0.63323385",
"0.63235164",
"0.62588257",
"0.6219759",
"0.61692274",
"0.61123574",
"0.61047447",
"0.6043373",
"0.6040491",
"0.5980549",
"0.5975069",
"0.59651697",
"0.59623265",
"0.5945968",
"0.591... | 0.78923905 | 0 |
Ensure decode(encode(number)) == number over a range of numbers. | def test_inverse(self):
from sosbeacon.utils import number_decode
from sosbeacon.utils import number_encode
for number in range(0, 500000, 339):
encoded = number_encode(number)
decoded = number_decode(encoded)
self.assertEqual(number, decoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def translate_num(number, lower_bound, upper_bound):\n try:\n value = int(number, 0)\n if value < lower_bound or value > upper_bound:\n raise translate_num_out_of_range(value, lower_bound, upper_bound)\n else:\n return value\n except:\n raise translate_num_er... | [
"0.6504324",
"0.6368809",
"0.6153909",
"0.612515",
"0.5986749",
"0.5916171",
"0.590891",
"0.58644295",
"0.581968",
"0.581036",
"0.5799809",
"0.57941693",
"0.579414",
"0.5778868",
"0.57601947",
"0.57461756",
"0.5717209",
"0.57090324",
"0.5706868",
"0.56949294",
"0.5694811",
... | 0.64608574 | 1 |
Ensure taskqueue.Queue.add is called exactly once. | def test_insert_batch(self, queue_mock):
from sosbeacon.utils import insert_tasks
tasks = []
for i in xrange(1, 10):
tasks.append(object())
added = insert_tasks(tasks, 'default')
self.assertEqual(added, 9) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_splits_once(self, queue_add_mock):\n from google.appengine.api import taskqueue\n from sosbeacon.utils import insert_tasks\n\n def side_effect(*args):\n if 2 in args[0]:\n raise taskqueue.TombstonedTaskError('uh oh')\n\n queue_add_mock.side_effect = si... | [
"0.6944164",
"0.65657383",
"0.6554443",
"0.62363005",
"0.6164665",
"0.6146997",
"0.608468",
"0.6042163",
"0.6031211",
"0.5978489",
"0.59758395",
"0.5960709",
"0.5934469",
"0.5904743",
"0.5876805",
"0.58512455",
"0.5846621",
"0.58196574",
"0.58075684",
"0.58046085",
"0.5796316... | 0.0 | -1 |
Ensure task batches are split and insertion is retried on TaskAlreadyExistsError. | def test_splits_once(self, queue_add_mock):
from google.appengine.api import taskqueue
from sosbeacon.utils import insert_tasks
def side_effect(*args):
if 2 in args[0]:
raise taskqueue.TombstonedTaskError('uh oh')
queue_add_mock.side_effect = side_effect
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_splits_on_taskexists(self, queue_add_mock):\n from google.appengine.api import taskqueue\n from sosbeacon.utils import insert_tasks\n\n queue_add_mock.side_effect = taskqueue.TaskAlreadyExistsError\n\n tasks = [i for i in xrange(0, 10)]\n added = insert_tasks(tasks, 'def... | [
"0.66691655",
"0.62592685",
"0.60708004",
"0.6027385",
"0.6026459",
"0.59589094",
"0.5712259",
"0.5664953",
"0.5624898",
"0.54451364",
"0.53221446",
"0.5284538",
"0.52741164",
"0.52515495",
"0.52346677",
"0.52338606",
"0.5233629",
"0.51976675",
"0.51887155",
"0.5188266",
"0.5... | 0.62067395 | 2 |
Ensure task batches are split and insertion is retried on TombstonedTaskError. | def test_splits_on_tombstoned(self, queue_add_mock):
from google.appengine.api import taskqueue
from sosbeacon.utils import insert_tasks
queue_add_mock.side_effect = taskqueue.TombstonedTaskError
tasks = [i for i in xrange(0, 7)]
added = insert_tasks(tasks, 'default')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_chunk_size_no_n_splits_provided(self):\n with self.assertRaises(ValueError):\n next(chunk_tasks([]))",
"def test_splits_once(self, queue_add_mock):\n from google.appengine.api import taskqueue\n from sosbeacon.utils import insert_tasks\n\n def side_effect(*args)... | [
"0.637449",
"0.62774134",
"0.60077596",
"0.5994124",
"0.5918459",
"0.57265055",
"0.56882507",
"0.56343436",
"0.558805",
"0.55751175",
"0.55216396",
"0.55209655",
"0.54577637",
"0.53927845",
"0.53148663",
"0.53103",
"0.5290059",
"0.5279151",
"0.5238429",
"0.5230983",
"0.522617... | 0.6903556 | 0 |
Ensure task batches are split and insertion is retried on TaskAlreadyExistsError. | def test_splits_on_taskexists(self, queue_add_mock):
from google.appengine.api import taskqueue
from sosbeacon.utils import insert_tasks
queue_add_mock.side_effect = taskqueue.TaskAlreadyExistsError
tasks = [i for i in xrange(0, 10)]
added = insert_tasks(tasks, 'default')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_splits_on_tombstoned(self, queue_add_mock):\n from google.appengine.api import taskqueue\n from sosbeacon.utils import insert_tasks\n\n queue_add_mock.side_effect = taskqueue.TombstonedTaskError\n\n tasks = [i for i in xrange(0, 7)]\n added = insert_tasks(tasks, 'default... | [
"0.62573755",
"0.620488",
"0.6070838",
"0.6027224",
"0.6025703",
"0.5959347",
"0.571253",
"0.5664554",
"0.5623779",
"0.5445145",
"0.53211784",
"0.5283602",
"0.52729964",
"0.52518106",
"0.5234075",
"0.5233979",
"0.52330583",
"0.51978356",
"0.51888645",
"0.518777",
"0.51753086"... | 0.6667976 | 0 |
Ensure a date with no hours / minutes is retuned as a date. | def test_date(self):
from sosbeacon.utils import format_datetime
date = datetime(year=2012, month=8, day=30)
encoded = format_datetime(date)
self.assertEqual('08/30/12', encoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insure_date(d):\n if isinstance(d, BeautifulDate):\n return date(year=d.year, month=d.month, day=d.day)\n else:\n return d",
"def fake_date_without_day(value):\n return date(year=value[0], month=value[1], day=1)",
"def ensure_date(value: Union[Date, Da... | [
"0.70018554",
"0.6946764",
"0.685827",
"0.6737637",
"0.6687125",
"0.66750145",
"0.65934587",
"0.64681107",
"0.64442796",
"0.6383153",
"0.6327889",
"0.6267114",
"0.62509316",
"0.6243825",
"0.61947256",
"0.61693084",
"0.6164787",
"0.61503243",
"0.6149337",
"0.6140435",
"0.61300... | 0.0 | -1 |
Ensure a date with hours and minutes is retuned as a datetime. | def test_date_with_time(self):
from sosbeacon.utils import format_datetime
date = datetime(year=2012, month=8, day=30, hour=7, minute=13)
encoded = format_datetime(date)
self.assertEqual('08/30/12 07:13', encoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ensure_datetime(ob: AnyDatetime) -> datetime.datetime:\n if isinstance(ob, datetime.datetime):\n return ob\n date = cast(datetime.date, ob)\n time = cast(datetime.time, ob)\n if isinstance(ob, datetime.date):\n time = datetime.time()\n if isinstance(ob, datetime.time):\n dat... | [
"0.7026758",
"0.65030086",
"0.64972097",
"0.64888424",
"0.6435325",
"0.64127433",
"0.6333002",
"0.62801087",
"0.6271071",
"0.62366825",
"0.62104833",
"0.6207101",
"0.61587965",
"0.6086302",
"0.6086026",
"0.60570335",
"0.6036885",
"0.60081786",
"0.5994509",
"0.5992334",
"0.598... | 0.64528507 | 4 |
Ensure a date with minutes but no hours is retuned as a datetime. | def test_date_with_zero_hours(self):
from sosbeacon.utils import format_datetime
date = datetime(year=2012, month=8, day=30, hour=0, minute=13)
encoded = format_datetime(date)
self.assertEqual('08/30/12 00:13', encoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_date_with_zero_minutes(self):\n from sosbeacon.utils import format_datetime\n\n date = datetime(year=2012, month=8, day=30, hour=19, minute=0)\n encoded = format_datetime(date)\n self.assertEqual('08/30/12 19:00', encoded)",
"def ensure_datetime(ob: AnyDatetime) -> datetime.d... | [
"0.694124",
"0.6576329",
"0.6497878",
"0.63341737",
"0.6285057",
"0.60327333",
"0.59235966",
"0.5857965",
"0.58377993",
"0.5830965",
"0.57997185",
"0.57975805",
"0.575214",
"0.5735291",
"0.5732054",
"0.57108104",
"0.5695891",
"0.5693779",
"0.5690543",
"0.5684678",
"0.56797",
... | 0.64184 | 3 |
Ensure a date with hours but no minutes is retuned as a datetime. | def test_date_with_zero_minutes(self):
from sosbeacon.utils import format_datetime
date = datetime(year=2012, month=8, day=30, hour=19, minute=0)
encoded = format_datetime(date)
self.assertEqual('08/30/12 19:00', encoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_date_with_zero_hours(self):\n from sosbeacon.utils import format_datetime\n\n date = datetime(year=2012, month=8, day=30, hour=0, minute=13)\n encoded = format_datetime(date)\n self.assertEqual('08/30/12 00:13', encoded)",
"def ensure_datetime(ob: AnyDatetime) -> datetime.dat... | [
"0.68381727",
"0.6782348",
"0.6598675",
"0.6525046",
"0.6244653",
"0.6082294",
"0.6023994",
"0.6016812",
"0.6003925",
"0.59741217",
"0.5968216",
"0.5952586",
"0.59485435",
"0.5870531",
"0.586283",
"0.5861185",
"0.58541614",
"0.58484566",
"0.58302844",
"0.5829411",
"0.5792652"... | 0.6744739 | 2 |
Ensure a missing date returns the empty string. | def test_non_input(self):
from sosbeacon.utils import format_datetime
encoded = format_datetime(None)
self.assertEqual('', encoded) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def strip_date(self, arg, line_number=0):\n try:\n dt = datetime.strptime(arg, \"%d %b %Y\")\n return dt\n except ValueError:\n raise ValueError(f\"US42 - Illegitimate date of {arg}. GEDCOM line: {line_number}\")\n else:\n return 'NA'",
"def test_b... | [
"0.66881824",
"0.6186882",
"0.61217904",
"0.61152357",
"0.60820913",
"0.6069122",
"0.6046784",
"0.60390896",
"0.60354966",
"0.602315",
"0.59958637",
"0.59915537",
"0.5989876",
"0.59784245",
"0.5972851",
"0.5956638",
"0.5951182",
"0.5942427",
"0.59409153",
"0.59297127",
"0.591... | 0.69573593 | 0 |
Ensure a missing lhs returns rhs. | def test_no_lhs(self):
from sosbeacon.utils import get_latest_datetime
lhs = None
rhs = object()
result = get_latest_datetime(lhs, rhs)
self.assertIs(rhs, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_rhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = object()\n rhs = None\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(lhs, result)",
"def not_equal(lhs, rhs):\n return _make.not_equal(lhs, rhs)",
"def isnone(cls, lhs, rhs):\n... | [
"0.6356365",
"0.56324995",
"0.5260674",
"0.5232673",
"0.51086223",
"0.5028781",
"0.5027332",
"0.49481612",
"0.48879817",
"0.4887651",
"0.48581392",
"0.4851787",
"0.48394328",
"0.48073387",
"0.48042664",
"0.47770894",
"0.47716808",
"0.4759484",
"0.47486883",
"0.47274226",
"0.4... | 0.6314329 | 1 |
Ensure a missing lhs returns rhs. | def test_no_rhs(self):
from sosbeacon.utils import get_latest_datetime
lhs = object()
rhs = None
result = get_latest_datetime(lhs, rhs)
self.assertIs(lhs, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_lhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = None\n rhs = object()\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(rhs, result)",
"def not_equal(lhs, rhs):\n return _make.not_equal(lhs, rhs)",
"def isnone(cls, lhs, rhs):\n... | [
"0.6314329",
"0.56324995",
"0.5260674",
"0.5232673",
"0.51086223",
"0.5028781",
"0.5027332",
"0.49481612",
"0.48879817",
"0.4887651",
"0.48581392",
"0.4851787",
"0.48394328",
"0.48073387",
"0.48042664",
"0.47770894",
"0.47716808",
"0.4759484",
"0.47486883",
"0.47274226",
"0.4... | 0.6356365 | 0 |
Ensure a missing lhs returns rhs. | def test_larger_lhs(self):
from sosbeacon.utils import get_latest_datetime
lhs = datetime(2012, 9, 20, 3, 45)
rhs = datetime(2012, 9, 20, 2, 45)
result = get_latest_datetime(lhs, rhs)
self.assertIs(lhs, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_rhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = object()\n rhs = None\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(lhs, result)",
"def test_no_lhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs =... | [
"0.6356365",
"0.6314329",
"0.56324995",
"0.5260674",
"0.5232673",
"0.51086223",
"0.5028781",
"0.5027332",
"0.49481612",
"0.48879817",
"0.4887651",
"0.48581392",
"0.4851787",
"0.48394328",
"0.48073387",
"0.48042664",
"0.47770894",
"0.47716808",
"0.4759484",
"0.47486883",
"0.47... | 0.0 | -1 |
Ensure a missing lhs returns rhs. | def test_larger_rhs(self):
from sosbeacon.utils import get_latest_datetime
lhs = datetime(2012, 9, 20, 2, 59)
rhs = datetime(2012, 9, 20, 3, 00)
result = get_latest_datetime(lhs, rhs)
self.assertIs(rhs, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_rhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = object()\n rhs = None\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(lhs, result)",
"def test_no_lhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs =... | [
"0.6356365",
"0.6314329",
"0.56324995",
"0.5260674",
"0.5232673",
"0.51086223",
"0.5028781",
"0.5027332",
"0.49481612",
"0.48879817",
"0.4887651",
"0.48581392",
"0.4851787",
"0.48394328",
"0.48073387",
"0.48042664",
"0.47770894",
"0.47716808",
"0.4759484",
"0.47486883",
"0.47... | 0.0 | -1 |
Ensure a missing lhs returns rhs. | def test_equal_inputs(self):
from sosbeacon.utils import get_latest_datetime
lhs = rhs = datetime(2012, 9, 20, 2, 59)
result = get_latest_datetime(lhs, rhs)
self.assertIs(rhs, result)
self.assertIs(lhs, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_no_rhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = object()\n rhs = None\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(lhs, result)",
"def test_no_lhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs =... | [
"0.6356365",
"0.6314329",
"0.56324995",
"0.5260674",
"0.5232673",
"0.51086223",
"0.5028781",
"0.5027332",
"0.49481612",
"0.48879817",
"0.4887651",
"0.48581392",
"0.4851787",
"0.48394328",
"0.48073387",
"0.48042664",
"0.47770894",
"0.47716808",
"0.4759484",
"0.47486883",
"0.47... | 0.0 | -1 |
Create a heatmap from a numpy array and two lists of labels. | def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel="", **kwargs):
if not ax:
ax = plt.gca()
# Plot the heatmap
im = ax.imshow(data, **kwargs)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def heatmap(data, row_labels, col_labels, ax=None,\r\n cbar_kw={}, cbarlabel=\"\", title = \"Default\", x_title=\" \",y_title=\" \",saveFile = None, **kwargs):",
"def heatmap(data, row_labels, col_labels, ax=None, cbar_kw={}, cbarlabel=\"\", **kwargs):\r\n\r\n if not ax:\r\n ax = plt.gca()\r... | [
"0.6765666",
"0.6746936",
"0.6712045",
"0.6677427",
"0.6677427",
"0.6677427",
"0.66198003",
"0.66069126",
"0.65382963",
"0.6534117",
"0.6513789",
"0.6497601",
"0.64536047",
"0.6449943",
"0.6434751",
"0.63585454",
"0.63225764",
"0.62491506",
"0.6228405",
"0.6213863",
"0.617980... | 0.6688963 | 3 |
A method to open and parse UniProt according to their keywords. Similar to what was done in Ata et al. 2018. | def parse_records(self):
for record in sp.parse(gzip.open(
"./human_uniprot_04_07_20.gz", 'rt')):
# print(record.taxonomy_id)
# if record.organism != "Homo sapiens":
# continue
# print(record.features[0])
# for comment in record.com... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def uniprot_txt_parser(uniprot_txt_lines):\n uniprot = {}\n entry_line = [i for i,l in enumerate(uniprot_txt_lines) if l[:2]=='ID']\n entry_line.append(len(uniprot_txt_lines))\n begin_end = [(begin,entry_line[i+1]) for i,begin in enumerate(entry_line[:-1])]\n for begin,end in begin_end:\n for... | [
"0.58220077",
"0.5802064",
"0.57448256",
"0.54425025",
"0.5314302",
"0.52784765",
"0.5223711",
"0.51647186",
"0.51590455",
"0.5118437",
"0.51034755",
"0.5095762",
"0.5017907",
"0.50099504",
"0.49973136",
"0.49962452",
"0.49901965",
"0.4973686",
"0.49708888",
"0.4963419",
"0.4... | 0.4876919 | 33 |
Input totally data frame then output daily data frame. | def get_increased_data(input_data_frame):
desc_data = input_data_frame.iloc[:, ::-1]
for index, col in desc_data.iteritems():
desc_data[index] = desc_data[index] - desc_data[index - 1]
if index == 2:
asc_data = desc_data.iloc[:, ::-1]
return asc_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_daily_files(dataframe, path, filename):\n\n days = dataframe.groupby('date_time_day')\n dataframe.groupby('date_time_day').size().reset_index(name='data points per day')\n\n for day in days.groups:\n print(day.date())\n output_path = path + filename + \"_\" + str(day.date()) + '.c... | [
"0.65267056",
"0.62918127",
"0.62888753",
"0.62433565",
"0.6169422",
"0.61276734",
"0.6091886",
"0.609138",
"0.60746336",
"0.6005586",
"0.6000989",
"0.59937394",
"0.59874874",
"0.59397405",
"0.59353983",
"0.5922037",
"0.5900342",
"0.589187",
"0.5853567",
"0.58485276",
"0.5825... | 0.0 | -1 |
Returns the ROS time in seconds | def get_time(cls):
now = rospy.Time.now()
return now.secs + now.nsecs*(10**-9) # time in seconds | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gettime():\n return libruss.russ_gettime()",
"def get_time(self):\n return self.get_timed() / 10.0",
"def _current_time_seconds(self):\n return int(round(time.time()))",
"def currentTimeSecs():\n return time.time()",
"def get_time(self) -> float:\n self.rocket.update()\n ... | [
"0.75012565",
"0.72696334",
"0.704129",
"0.704039",
"0.70382655",
"0.69805497",
"0.6978949",
"0.6969247",
"0.69403255",
"0.69107735",
"0.6896041",
"0.6878848",
"0.685984",
"0.67993206",
"0.67879426",
"0.6787236",
"0.67727417",
"0.6743631",
"0.6704049",
"0.6703564",
"0.6699629... | 0.7948383 | 0 |
Creates the log directories for the runs and saves initial run info | def instantiate_logs(self):
# Log file
timestamp = datetime.now().strftime("%Y-%m-%dT%H%M%S")
self.log_dir = os.path.join("experiment_logs", timestamp)
# Create Log directory if it does not exist
try:
os.makedirs(self.log_dir)
except OSError as exception:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setup_dir(self):\n if not os.path.exists(self._save_dir):\n logger.info(\"save_dir {} does not exist, \"\n \"creating it\".format(self._save_dir))\n os.makedirs(self._save_dir)\n\n # Log the run parameters.\n logger.info(\"Writing logs to {}\".... | [
"0.7466747",
"0.7157217",
"0.70655835",
"0.69699013",
"0.69102144",
"0.6875181",
"0.6774886",
"0.67428565",
"0.6716854",
"0.6689758",
"0.66799164",
"0.6649505",
"0.6647565",
"0.6583148",
"0.6562383",
"0.6534038",
"0.6531846",
"0.65123534",
"0.6502982",
"0.64945877",
"0.648349... | 0.7053942 | 3 |
Sends the goal vehicle pose to the simulator | def pub_goal_vehicle_pose(self):
header = Header()
header.stamp = rospy.Time.now()
position = Point(20.5, -10, -85)
# position = Point(20.5, -10, -85)
yaw = pi
# Converting yaw to quaternion
# See https://en.wikipedia.org/wiki/Conversion_between_quaternions_and... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_destination(self):\n\n print('send the target to the robot')\n move_base_action_goal=MoveBaseActionGoal()\n move_base_action_goal.goal.target_pose.header.frame_id=\"map\"\n move_base_action_goal.goal.target_pose.pose.orientation.w=1\n move_base_action_goal.goal.target_po... | [
"0.6900186",
"0.6846976",
"0.6781664",
"0.6722921",
"0.6500318",
"0.647756",
"0.641095",
"0.63947874",
"0.6390289",
"0.6380875",
"0.6371278",
"0.6319203",
"0.61569244",
"0.6111631",
"0.60957015",
"0.6094904",
"0.6075576",
"0.6063163",
"0.6058459",
"0.60555637",
"0.6015337",
... | 0.74411386 | 0 |
Sends a command to the ros service to update ocean currents based on vehicle position | def update_current(self):
velocity, horizontal_angle, vertical_angle = self.current_function()
self.set_current_velocity(velocity, horizontal_angle, vertical_angle) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self):\n self._odom_msg.header.stamp = rospy.Time.now()\n # query base state from robot and store in odom msg\n position, orientation, linear_velocity, angular_velocity = self._robot.get_base_state()\n [self._odom_msg.pose.pose.position.x,\n self._odom_msg.pose.pose.... | [
"0.64087474",
"0.61778766",
"0.60122633",
"0.5960585",
"0.5868954",
"0.58094513",
"0.5795152",
"0.5743932",
"0.56760687",
"0.56449115",
"0.5635973",
"0.5577554",
"0.5568342",
"0.5534826",
"0.5531178",
"0.55111843",
"0.5509245",
"0.5506559",
"0.5487788",
"0.5474574",
"0.546183... | 0.0 | -1 |
A simple timevarying current field in the x direction based on a sinusoid. | def sinusoid_current(self):
t = self.get_time() # time in seconds
offset_time = t - self.start_time - self.delay_time # time offset by start/delay time
# Don't start the currents until t > start + 10
if t < self.start_time + self.delay_time:
print("Waiting for delay time to... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, x):\r\n f = math.sin(self.phase_offset + self.angular_frequency*x)\r\n return self.amplitude_offset + self.amplitude*f",
"def tsx(self):\n\n self.x = self.sp\n self.set_zn(self.x)",
"def position(t, x, y):\n return x * exp(-t * y) * sin(2 * pi * t)",
"def update(s... | [
"0.6198477",
"0.6151556",
"0.6098946",
"0.5898149",
"0.5895894",
"0.5881547",
"0.5811019",
"0.57994246",
"0.57935405",
"0.5681556",
"0.5660104",
"0.5616797",
"0.5612879",
"0.56064534",
"0.55949235",
"0.5590392",
"0.5557532",
"0.5556586",
"0.55441815",
"0.5528075",
"0.552043",... | 0.627386 | 0 |
Generate prime values using sieve of Eratosthenes method. | def create_primes(threshold):
if threshold == 2:
return [2]
elif threshold < 2:
return []
numbers = list(range(3, threshold + 1, 2))
root_of_threshold = threshold**0.5
half = int((threshold + 1) / 2 - 1)
idx = 0
counter = 3
while counter <= root_of_threshold:
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def EratosthenesSieve(N):\n numbers = [True] * (N+1)\n max_p = int(math.sqrt(N))\n for p in (i for i in range(2, max_p+1) if numbers[i]):\n for q in range(p*p, N+1, p):\n numbers[q] = False\n return [i for i in range(2, N+1) if numbers[i]]",
"def EratosthenesSieve(N):\n numbers =... | [
"0.7907143",
"0.7902661",
"0.7900705",
"0.7870067",
"0.786426",
"0.7795188",
"0.77421093",
"0.7724749",
"0.76443654",
"0.7612922",
"0.758561",
"0.7580121",
"0.75798064",
"0.75734884",
"0.7526076",
"0.74892634",
"0.7484998",
"0.7456353",
"0.74515086",
"0.7430674",
"0.7415825",... | 0.0 | -1 |
Calculate the distance in km between two points given in decimal degrees. Uses Haversine formula, i.e. assuming perfectly spherical earth | def Distance(VCoords, SCoords):
#Convert to radians
lat1,lon1,lat2,lon2 = map(math.radians, VCoords+SCoords)
#Apply Haversine formula
dlon = lon2-lon1
dlat = lat2-lat1
a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
c = 2 * math.asin(math.sqrt(a))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def haversine(lat1, lon1, lat2, lon2):\n\t\t # convert decimal degrees to radians \n\t\t lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n\t\t # haversine formula \n\t\t dlon = lon2 - lon1 \n\t\t dlat = lat2 - lat1 \n\t\t a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2... | [
"0.7997606",
"0.79383963",
"0.7895904",
"0.78638697",
"0.7796029",
"0.77701026",
"0.77680063",
"0.77678233",
"0.77571",
"0.7750529",
"0.7741123",
"0.77320206",
"0.7727604",
"0.7691649",
"0.7687481",
"0.76833457",
"0.7681609",
"0.7681609",
"0.7670106",
"0.7646074",
"0.76412785... | 0.0 | -1 |
Sorts a list of tuples based on the first value in each. | def SortTupleList(TupleList):
SortedList = []
for i in range(0, len(TupleList)):
Pivot = len(SortedList) / 2
while TupleList[i] < SortedList(Pivot):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort_list_of_tuples(list):\n list.sort(key=lambda x: x[0])\n return list",
"def tupleListSort(tupleList):\n tupleList.sort(key=lambda y: y[0].lower())",
"def sort_fst(xs):\n return sorted(xs, key=lambda pair: pair[0])",
"def sort_prices(list_of_tuples):\n list_of_tuples.sort(key = get_pric... | [
"0.85222363",
"0.7436253",
"0.7205574",
"0.7185881",
"0.7109334",
"0.70277673",
"0.6577772",
"0.6568136",
"0.6405855",
"0.6328426",
"0.6323056",
"0.63180023",
"0.6280642",
"0.6245652",
"0.6225239",
"0.6207625",
"0.61590034",
"0.6151864",
"0.6146145",
"0.61375666",
"0.60629576... | 0.6534301 | 8 |
Check if uuid_to_test is a valid UUID. | def is_valid_uuid(uuid_to_test, version=4):
try:
uuid_obj = UUID(uuid_to_test, version=version)
except ValueError:
return False
return str(uuid_obj) == uuid_to_test | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_valid_uuid(uuid_to_test, version=4):\n\ttry:\n\t\tuuid_obj = UUID(uuid_to_test, version=version)\n\t\treturn True\n\texcept:\n\t\treturn False",
"def check_uuid(uuid):\n try:\n converted = UUID(uuid, version=4)\n except ValueError:\n return False\n\n return str(converted) == uuid",
... | [
"0.8684954",
"0.8333081",
"0.8155957",
"0.80367714",
"0.8028158",
"0.79428333",
"0.77374667",
"0.7734152",
"0.7734152",
"0.76239145",
"0.7593849",
"0.7555792",
"0.7149705",
"0.71090436",
"0.69526494",
"0.6922901",
"0.66879606",
"0.66867393",
"0.66619414",
"0.6655981",
"0.6640... | 0.8562131 | 1 |
return parsed file info file_name should be a string with full path to file | def file_info(file_name, file_pattern):
match = re.compile(file_pattern).match(file_name)
if match:
basepath = match.group('basepath')
sensor = match.group('sensor')
ax = match.group('ax')
freq = match.group('freq')
date = match.group('date')
return basepath, sens... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_file_info(directory, root_path, name):\n file_path = join(directory, name)\n rel_path = relpath(file_path, root_path)\n return {\n \"name\": name,\n \"path\": file_path,\n \"dir_name\": dirname(file_path),\n \"is_file\": isfile(file_path),\n \"is_dir\": isdi... | [
"0.70860726",
"0.7010818",
"0.6978658",
"0.69049",
"0.68632764",
"0.66971594",
"0.6577073",
"0.64977664",
"0.64931035",
"0.64348495",
"0.6431073",
"0.6293928",
"0.6288821",
"0.6240199",
"0.623606",
"0.61522275",
"0.61236763",
"0.6095241",
"0.607708",
"0.60727507",
"0.6071609"... | 0.71029544 | 0 |
Bug 1660259 Correct nav arialabel and label and description for theme buttons, part {index}. | def migrate(ctx):
ctx.add_transforms(
"browser/browser/newtab/onboarding.ftl",
"browser/browser/newtab/onboarding.ftl",
transforms_from("""
onboarding-multistage-theme-tooltip-automatic-2 =
.title = { COPY_PATTERN(from_path, "onboarding-multistage-theme-tooltip-automatic.title") }
onboar... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_button_label(self,index):\r\n self.l_debug(\"_get_button_label\",\"index=%d\" % (index))\r\n # TODO: Make sure it's a valid index?\r\n return self.parent.harmony_config['info']['functions'][index]['label']",
"def change_back_alphabet_button(event):\n img_alphabet_button_mouse... | [
"0.59079456",
"0.57021827",
"0.5611441",
"0.5596499",
"0.5464772",
"0.5462852",
"0.54272836",
"0.5413362",
"0.5381082",
"0.53032327",
"0.5299294",
"0.5295865",
"0.52915895",
"0.5270703",
"0.5253295",
"0.5223281",
"0.52090263",
"0.517131",
"0.5170982",
"0.5129802",
"0.512315",... | 0.0 | -1 |
Gets a list of machine proxy objects for machines registered on the server. | def list_services(self):
response = self._get()
services = []
for s in response["services"]:
services.append(_create_service_from_json(s, self._session, self._url_base, s["folderName"]))
return services | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _GetMachineList(self):\n machines = self._experiment.remote\n # All Label.remote is a sublist of experiment.remote.\n for l in self._experiment.labels:\n for r in l.remote:\n assert r in machines\n return machines",
"def get_machines(self):\n\n return self._machine_manager.get_... | [
"0.6875486",
"0.6869549",
"0.6537448",
"0.6433981",
"0.6399903",
"0.637255",
"0.6364685",
"0.6354067",
"0.63194156",
"0.6291957",
"0.62829745",
"0.6226571",
"0.6222153",
"0.6200053",
"0.6157794",
"0.6109501",
"0.6103711",
"0.60903883",
"0.6048954",
"0.60453343",
"0.59695596",... | 0.0 | -1 |
helper to return all possible splits this function returns all the possible splits on the given attribute on the provided dataframe | def get_possible_splits( df , attribute ):
ds = df.loc[:,attribute]
# First sort the values
ds = ds.sort_values().drop_duplicates()
# Compute averages of consecutive values
ds = ds.rolling(2).sum().divide(2)
splits = ds[1:].tolist()
# return the possible splits
return s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def splitBy(data, attribute_id):\n \n col = getColumn(data, attribute_id)\n values = set(col)\n split_data = [] \n for i in values:\n subset = [row for row in data if row[attribute_id] == i]\n split_data.append(subset)\n \n return split_data",
"def generate_splits( records, i... | [
"0.6871864",
"0.6392091",
"0.62676346",
"0.60429597",
"0.60380805",
"0.603773",
"0.5964825",
"0.5956346",
"0.5944079",
"0.5930761",
"0.58742964",
"0.58675",
"0.5851564",
"0.5841597",
"0.5820882",
"0.5810458",
"0.5741932",
"0.573894",
"0.57379395",
"0.5715522",
"0.57015127",
... | 0.81955785 | 0 |
helper function to evaluate gini value for the split This function gives Gini Value for the split generated by 'split' on 'attribute' in the given dataframe | def evaluate_split( df, attribute, split ):
mask = df[attribute] <= split
# split the dataset on the split attribute
dfl = df[mask]
dfr = df[~mask]
# calculate weighting factors for child
weighting_factor_left = float(dfl.shape[0])/df.shape[0]
weighting_factor_right = float(dfr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calcGiniSplitBySplitValue(self, data, structure, colIndex, splitValue):\n dataBellow = list(filter(lambda x: float(x[colIndex]) <= splitValue, data))\n dataAbove = list(filter(lambda x: float(x[colIndex]) > splitValue, data))\n giniSplit = (len(dataBellow) / len(data)) * self.calcDataGini(... | [
"0.6597128",
"0.6406698",
"0.62779886",
"0.6191937",
"0.567172",
"0.5599756",
"0.5579262",
"0.5560474",
"0.53938425",
"0.5372687",
"0.5327218",
"0.5243013",
"0.5231648",
"0.52305394",
"0.50356853",
"0.5020019",
"0.49910983",
"0.49304187",
"0.49144533",
"0.4894045",
"0.4893178... | 0.77684784 | 0 |
Write a decorator that prints UPPER_SLICE and LOWER_SLICE before and after calling the function (func) that is passed in ( is to preserve the original func's docstring) | def sandwich(func):
@functools.wraps(func)
def wrapped_decorator(*args, **kwargs):
print(UPPER_SLICE)
arguments= func(*args, **kwargs)
print(LOWER_SLICE)
return arguments
return wrapped_decorator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_wraps():\n print('func')",
"def trace(func):\n @wraps(func)\n def tracer(*args, **kwargs):\n name = func.__name__\n stack_size = int(len(inspect.stack(0)) / 2) # @wraps(func) is also increasing the size\n indent = stack_size*'\\t'\n print(f'{indent} > Entering \"{nam... | [
"0.59775233",
"0.59042966",
"0.58600795",
"0.58352566",
"0.58184",
"0.5748021",
"0.5739565",
"0.5706729",
"0.56139064",
"0.55796224",
"0.5578859",
"0.5534652",
"0.5532867",
"0.5523652",
"0.55230683",
"0.55105054",
"0.54960775",
"0.5491101",
"0.5476996",
"0.54521424",
"0.54212... | 0.8172087 | 0 |
Generates graph of connected peers Change to make this customisable | def generate_graph(self):
temp_graph = [[] for i in xrange(Parameters.num_peers)]
unconnected = set([i for i in xrange(Parameters.num_peers)])
while len(unconnected) > 1:
node1 = random.sample(unconnected, 1)[0]
unconnected.remove(node1)
node2 = random.sample(unconnected, 1)[0]
temp_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chain_graph(self) -> nx.DiGraph:\n edg_lst = [\n (f\"p{idx}\", f\"p{idx+1}\", self.d[f\"p{idx+1}\"]) for idx in range(self.n)\n ]\n chain_graph = nx.DiGraph()\n chain_graph.add_weighted_edges_from(edg_lst)\n return chain_graph",
"def chain_graph(self) -> nx.DiGra... | [
"0.62271756",
"0.61608636",
"0.5960585",
"0.58688587",
"0.5849908",
"0.5812597",
"0.57896066",
"0.57523227",
"0.5716475",
"0.57021713",
"0.56578314",
"0.56416863",
"0.56310755",
"0.56212884",
"0.56085145",
"0.5589877",
"0.5569081",
"0.5566458",
"0.55554277",
"0.5497438",
"0.5... | 0.6303654 | 0 |
Start thread for each peer | def start_peers(self):
for i in self.nodes:
i.start() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def peer_server(self):\n try:\n listener_thread = threading.Thread(target=self.peer_server_listener)\n listener_thread.setDaemon(True)\n\n operations_thread = threading.Thread(target=self.peer_server_host)\n operations_thread.setDaemon(True)\n\n listene... | [
"0.67120516",
"0.65352994",
"0.649779",
"0.64329314",
"0.6382283",
"0.62863195",
"0.6221034",
"0.62145627",
"0.6192615",
"0.6154389",
"0.61105305",
"0.60992175",
"0.6057519",
"0.6056828",
"0.60463715",
"0.60429364",
"0.6019063",
"0.5967418",
"0.59653294",
"0.595958",
"0.59562... | 0.7830526 | 0 |
Get the network delay between pid1 and pid2 | def get_delay(self, pid1, pid2, is_block):
is_slow = self.node_is_slow[pid1] or self.node_is_slow[pid2]
p = random.uniform(Parameters.p_min, Parameters.p_max)
c = Parameters.c_low if is_slow else Parameters.c_high
m = Parameters.m if is_block else 0
d = random.expovariate(c / Parameters.d)
retur... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getTimeDelay(*args):\n return args[0].TimeState.TimeDelay.time_delay",
"def get_delay(self, src, dst):\n try:\n fwd_delay = self.awareness.graph[src][dst]['lldpdelay']\n re_delay = self.awareness.graph[dst][src]['lldpdelay']\n src_latency = self.echo_latency[src]\n ... | [
"0.60461235",
"0.59016067",
"0.586062",
"0.56952107",
"0.55916893",
"0.55790615",
"0.55780035",
"0.5559182",
"0.554915",
"0.5509043",
"0.5487633",
"0.5454966",
"0.53850937",
"0.53481144",
"0.5299796",
"0.5299796",
"0.52713627",
"0.5267619",
"0.52288824",
"0.52207476",
"0.5209... | 0.75615823 | 0 |
Adds a meta file to database. | def add_meta_f_to_db(meta_f, p, dbi):
rism_attributes = sd.Water3DRISM.__dict__.keys()
extra_attributes = sd.Water3DRISMExtra.__dict__.keys()
with open(os.path.join(p, meta_f), 'rb') as f:
txt = f.readlines()
inchi_line = txt[0]
if inchi_line.startswith('InChI'):
print inchi_line
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __appendMetaData(self, filename):\n metadata = {'Model': 'LFM',\n 'Source': filename,\n 'Date processed': datetime.datetime.now(),\n 'Start date': self.startDate\n }\n \n self.data.append(key='meta',\n ... | [
"0.68102056",
"0.67017496",
"0.6549066",
"0.65284264",
"0.64970475",
"0.6281541",
"0.62121874",
"0.61612666",
"0.61489415",
"0.6141335",
"0.60017574",
"0.5996768",
"0.5930677",
"0.59127736",
"0.5901605",
"0.5892127",
"0.58494866",
"0.5829368",
"0.5819864",
"0.58169425",
"0.57... | 0.6100383 | 10 |
This is the main function of the program | def main():
global FPSCLOCK, DISPLAYSURF, BASICFONT
pygame.init()
FPSCLOCK = pygame.time.Clock()
# set up the window
BASICFONT = pygame.font.Font('freesansbold.ttf', 20)
DISPLAYSURF = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT), 0, 32)
pygame.display.set_caption('Othello')
DISPLAYSURF.fill(BGCOLOR)
#In... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main():",
"def main(... | [
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"0.9220979",
"... | 0.0 | -1 |
This is the method that draws the game board | def drawBoard():
#draw 64 Rectangles from (MARGINH,MARGINV) with CASESIZE sizes
for i in range(BOARDSIZE):
for j in range(BOARDSIZE):
pygame.draw.rect(DISPLAYSURF, BLACK, [MARGINH + (i)*CASESIZE, MARGINV + (j)*CASESIZE, CASESIZE, CASESIZE], 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drawBoard(self):\r\n \r\n for i in range(8):\r\n for j in range(8):\r\n if (i %2 == 0 and j % 2 == 0) or (i % 2 !=0 and j % 2 != 0):\r\n COLOR = COLOR1\r\n else: COLOR = COLOR2\r\n pygame.draw.rect(screen, COLOR, Rect(i*50... | [
"0.83789307",
"0.83610886",
"0.8352866",
"0.82823396",
"0.8200601",
"0.81451064",
"0.8119161",
"0.81146264",
"0.7879229",
"0.7845098",
"0.7784387",
"0.77669483",
"0.7705313",
"0.7694651",
"0.763012",
"0.7628809",
"0.7627039",
"0.76243454",
"0.76111424",
"0.75869983",
"0.75507... | 0.79026276 | 8 |
This method draws a piece of the right color at the right position. | def drawPiece(pos,color):
if color == 0:
color_piece = BGCOLOR
elif color == 1:
color_piece = BLACK
elif color == 2:
color_piece = WHITE
elif color == 3:
color_piece = LIGHTGREEN
#draws a circle of the right color on the board
pygame.draw.ellipse(DISPLAYSURF, color_piece, [MARGINH + (pos[0]-1)*CASESIZE+4,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _draw_square(self, left_x, top_y, side, color, fill):\n self.pen.up()\n self.pen.color(color)\n self.pen.goto(left_x, top_y)\n self.pen.down()\n self.pen.begin_fill()\n for _ in range(4):\n self.pen.forward(side)\n self.pen.right(90)\n self... | [
"0.6624936",
"0.65994567",
"0.65606534",
"0.6394199",
"0.63757133",
"0.6260754",
"0.6213263",
"0.62062967",
"0.6189885",
"0.6115182",
"0.60815096",
"0.60737467",
"0.60621893",
"0.6014855",
"0.6011095",
"0.59758043",
"0.5940978",
"0.5921333",
"0.59145665",
"0.5895593",
"0.5887... | 0.62434816 | 6 |
This function takes a mouse position and returns the position of the box the click is in or an empty tuple if it is not in the board | def isInBoard(posx, posy):
#is pos in the board
if posx >= MARGINH and posx <= MARGINH + (BOARDSIZE)*CASESIZE and posy >= MARGINV and posy <= MARGINV + (BOARDSIZE)*CASESIZE:
#transform it in case coordinates
casex = int((posx - MARGINH)/CASESIZE) + 1
casey = int((posy - MARGINV)/CASESIZE) + 1
return (casex,ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mouse_on_grid(self):\n if self.mousePos[0] < grid_pos[0] or self.mousePos[1] < grid_pos[1]:\n return None\n if self.mousePos[0] > grid_pos[0] + grid_size or self.mousePos[1] > grid_pos[1] + grid_size:\n return None\n temp = (self.mousePos[0] - grid_pos[0]\n ... | [
"0.69800913",
"0.69175345",
"0.6895727",
"0.6853113",
"0.6841856",
"0.68141514",
"0.671112",
"0.6637405",
"0.66198343",
"0.65802467",
"0.6531164",
"0.64707327",
"0.64676",
"0.64114225",
"0.63978434",
"0.6394438",
"0.6353023",
"0.6344379",
"0.6328014",
"0.6309418",
"0.6291824"... | 0.58239156 | 51 |
This function makes the move on the state_board | def make(self,state_board):
state_board[self.column][self.line] = self.couleur #place the piece
drawPiece((self.column,self.line),self.couleur) #draws it on the board
for pos in self.flips: #flips all the pieces in flips
state_board[pos[0]][pos[1]] = self.couleur
drawPiece(pos,self.couleur) #draws it on the... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self, state):\n raise NotImplementedError(\"Need to implement this method\")",
"def move(self, board):\n # first, make your turn:\n currentState = board[self.x,self.y]\n turnDir = self.rule[(currentState + 1) % len(self.rule)]\n self.turn( int(turnDir) )\n # nex... | [
"0.7646966",
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"0.6925819",
"0.6908902",
"0.68981934",
"0.6890... | 0.6485864 | 73 |
This function defines the inital board of the game | def init_board():
# Generates a table 10*10 of 0s with -1 around and the initial state
# of the board with 2 whites and 2 blacks in the middle
table = [[0 if i != 0 and i != 9 else -1 for i in range(10)] if j != 0 and j != 9 else [-1 for i in range(10)] for j in range(10)] #leaves a -1 line around the whole table of... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initBoard(self):\n pass",
"def initialize_board(self):\n self.board = np.zeros(shape=(BOARD_SIZE, BOARD_SIZE), dtype=np.int) # another way of defining board: [[for x in range(cm.BOARD_SIZE)] for x in range(cm.BOARD_SIZE)]\n center = int(BOARD_SIZE / 2)\n self.board[center-1][cen... | [
"0.84164816",
"0.81662166",
"0.7968856",
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"0.77081436",
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"0.738848",
"0.73879623",
"0.73621136",
"0.73618233",
"0.734... | 0.83401465 | 1 |
This function finds possibilities for a player on a board | def possible(state_board,turn):
legal_moves = [] # list of legal moves as Move objects
for i in range(1,9):
for j in range(1,9):
if state_board[i][j] == 0:
if flipper([i,j],turn,state_board) != []:
# if there are flipped pieces, it appends this move to
# the legal moves and draws it in light greens... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def possibilities(board):\n return board[np.where(board == 0)]",
"def _check_winning_combinations(board, player):\n winning_combinations = (\n ((0, 0), (0, 1), (0, 2)),\n ((1, 0), (1, 1), (1, 2)),\n ((2, 0), (2, 1), (2, 2)),\n ((0, 0), (1, 0), (2, 0)),\n ((0, 1), (1, 1), ... | [
"0.70995724",
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"0.6524956",
"0.65147173",
"0.6503424",
"0.6475118",
"0.64386654"... | 0.6201891 | 58 |
This function finds the flipped pieces and returns them | def flipper(pos, coul, state_board):
tflips = []
for i in range(-1,2): # -1 to 1
for j in range(-1,2): #-1 to 1
for k in range(1,9): # 1 to 8
if state_board[pos[0]+i*k][pos[1]+j*k] == 0 or state_board[pos[0]+i*k][pos[1]+j*k] == -1: # if the case is empty or out of bounds
break;
elif state_board[pos[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_flip_piece():\n board = Board(640, 640, 8)\n board.start_game()\n board.gm.flip_pieces = [(3, 3)]\n current_color = board.game_pieces[3][3].color\n board.flip_pieces()\n assert board.game_pieces[3][3].color != current_color\n \n board.gm.flip_pieces = [(3, 4)]\n current_color = b... | [
"0.6014114",
"0.58825684",
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"0.5230201",
"0.5228546",
"0.52233607",
"0.5223231",
"0.5212... | 0.64342684 | 0 |
This function tests a board for a winner and returns a value between 0 and 5 | def test_winner(state_board):
res = 3 #default value is tie game
ptsb = 0 #points for the black
ptsw = 0 #points for the white
#looks in the board if there is an empty case while
# counting the number of points for each player
for i in state_board:
for j in i:
if j == 0:
res = 0
elif j == 1:
pts... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(board):\n winner = 0\n for player in [1, 2]:\n if row_win(board, player) or col_win(board, player) or diag_win(board, player):\n winner = player\n \n if np.all(board != 0) and winner == 0:\n winner = -1\n return winner",
"def utility(board):\n win =... | [
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"0.7508619",
"0.75054234",
"0.7480221",
"0.7479761",
"0.747914",... | 0.76951003 | 3 |
This function counts the points and returns | def count_points(p1,p2):
if p1 > p2:
drawWinner(1)
return 1
elif p2 > p1:
drawWinner(2)
return 2
else:
drawWinner(3)
return 3 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Points_Counting(self):\n return len(self.__traectory_list)",
"def nr_points(self):\n return len(self.x)",
"def count():",
"def countPoints(self,sumation):\n if sumation == 21:\n points = 7\n elif sumation == 20:\n points = 5\n elif sumation == 19:\... | [
"0.74205756",
"0.71399736",
"0.71250033",
"0.70320505",
"0.6990405",
"0.69521356",
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"0.64867896",
"0.64867896",
"0.64867896",
"0.6444945",
"0.64027125",
"... | 0.7081302 | 3 |
This method says who is the winner on top of the board | def drawWinner(result):
# determines who is the winner from the result
if result == 1:
text = "Black player is the winner !"
elif result == 2:
text = "White player is the winner !"
else:
text = "Tie Game !"
#draws the text as in a surface
winner_surf = BASICFONT.render(text, True, BLACK)
winner_rect = w... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def winner(board):\n\t#For X\n\tiswinnerX = winnerForPlayer(board, X)\n\tiswinnerO = winnerForPlayer(board, O)\n\n\tif iswinnerX:\n\t\treturn X\n\tif iswinnerO:\n\t\treturn O\n\n\treturn None",
"def determine_winner1(self): \r\n sorted_player_rank = self._rank()\r\n print(f\"sorted player rank: ... | [
"0.7595406",
"0.7561998",
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"0.7231121",
"0.72244394",
"0.7210622",
"0.72083884",
"0.71932673",
"0.7186... | 0.0 | -1 |
Extract ngram feature for single dataStr | def __processSingleInstance(self, dataStr):
rsltList = [self.__depdData.GetDependName(d) \
for d in self.__depdData.SplitDenpStr(dataStr)]
return rsltList | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract(self, document):\n f_num = len(self.feature_list)\n feature_vector = np.zeros((f_num,))\n words = document.split()\n for i in xrange(len(words)):\n for n in self.ns:\n ngram = self.try_get_ngram(words, n, i)\n if ngram and ngram in se... | [
"0.6531941",
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"0.6000876",
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"0.56565773",
"0.5656345",
"0.5622813",
"0.5619041",
"0.5607017",
"0.5584... | 0.0 | -1 |
Extract POS ngram feature from tree dict | def ExtractFeatureOnCorpus(self, dataDict):
wd = WritingData.GetInstance()
# Step 1. First pass scan to get sorted vocab
vocab = {} # POS vocab
classSet = set()
classColStr = "Nationality"
numWrt = 0
for wrtId in dataDict.keys():
# wrtId (int)
classVal = wd.GetValueByWid(wrtId, classColStr) # cl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_tree(tree):\n return find_noun(tree)\n\n # noun_phrase = re.match(\"NP|WHNP\", tree.parent().label())\n # noun = re.match(\"NN.*\", tree.label())\n # return noun_phrase and noun",
"def feat_dict(pos_feat,text):\n dict = {}\n bigrams = ngrams(word_tokenize(text),2)\n ... | [
"0.6383279",
"0.6254126",
"0.61910975",
"0.6125874",
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"0.53711605",
"0.53593254",
"0.53128034",
"0.5308846",
"0.530849... | 0.52241606 | 29 |
Context processor to fetch community stats from Django people and Django packages. This caches the resulting dictionary to lower the chance of overwhelming those services. | def community_stats(request):
stats = cache.get(STATS_CACHE_KEY, None)
if not stats:
stats = fetch(PEOPLE_STATS_URL)
packages_data = fetch(PACKAGES_STATS_URL)
if 'meta' in packages_data:
stats.update({'packages': packages_data['meta']['total_count']})
stats = {'comm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def each_context(self, request):\n script_name = request.META['SCRIPT_NAME']\n site_url = script_name if self.site_url == '/' and script_name else self.site_url\n # 把用户的groupID传给template\n # if Group.objects.filter(id = request.user.id):\n # group_context = Group.objects.get(... | [
"0.55988616",
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"0.5080095",
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"0.5069672",
"0.5065913",
"0.5061... | 0.7520238 | 0 |
Returns True if both submodels have same length | def _validate_submodels(self, type_promax, type_ms):
return type_promax in self._submodels and \
type_ms in self._submodels and \
len(self._submodels[type_promax]) > 0 and \
len(self._submodels[type_promax]) == len(self._submodels[type_ms]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __len__(self):\n assert len(self.sent1) == len(self.sent2)\n return len(self.sent1)",
"def __eq__(self, other):\n return isinstance(other, type(self)) and self.size == other.size",
"def equal_size(self, other):\n if not isinstance(other, Matrix):\n raise ValueError(\"... | [
"0.62707317",
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"0.58497226",
"0.58374107",
"0.5794245",
"0.57578903",
"0.575601",
... | 0.71253115 | 0 |
Validates type, returning ("normalized_type", submodel_fields) | def _validate_type(self, tp: str, name: str = None):
if tp is None:
return None, None
fields = None
if tp.startswith('{'):
# Submodel defined in JSON
fields = parse_json_model(tp, modelname=name)
if not fields:
return None, None
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_model_field_types(self):\n self.assertTrue(isinstance(self.UserInfo.have_siblings, str))\n self.assertTrue(isinstance(self.UserInfo.known_env_exposures, str))\n self.assertTrue(isinstance(self.UserInfo.known_genetic_mutations, str))\n self.assertTrue(isinstance(self.UserInfo.ag... | [
"0.6012281",
"0.5627871",
"0.56062263",
"0.5541995",
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"0.52599204",
"0.5195668",
"0.51644295",
"0.51417065",
"0.5140979",
"0.513462... | 0.6659755 | 0 |
Yields pairs from an iterable. | def pairs(iterable):
previous = None
for item in iterable:
current = item
if previous is not None:
yield previous, current
previous = current | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_pairs(iterable):\n if isinstance(iterable, Mapping):\n iterable = iterable.items()\n return iter(iterable)",
"def pairs(lst):\r\n\tfor i in range(1, len(lst), 2):\r\n\t\tyield lst[i-1], lst[i]",
"def pairwise(iterable):\r\n a = iter(iterable)\r\n return izip(a, a)",
"def pairs(lst... | [
"0.81223637",
"0.7461575",
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"0.6843346",
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"0.6824935",
"0.6753879",
... | 0.77604663 | 1 |
Used to visualize the follow poing on purepursuit | def viewFollowPoint(self,follow_point_msg):
marker = Marker()
marker.header.frame_id = self.veh_name
marker.ns = self.veh_name + "/follow_point"
marker.id = 0
marker.action = Marker.ADD
marker.type = Marker.SPHERE
marker.lifetime = rospy.Duration.from_sec(5.0)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def head_hearteyes():\n print (hair_longer())\n print (eye_heart())\n print (nose_rightwards())\n print (mouth_smile())\n print (chin_curvy())",
"def bonus_food(self):\n self.penup()\n self.shape(\"turtle\")\n self.color(\"red\")\n self.x_cordinates ... | [
"0.56292534",
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"0.52410096",
"0.52402276",
"0.5233314",
"0.5229365",
"0.52061... | 0.4950288 | 72 |
Returns the current date. That means every time the project is deployed, the datestamp will update. Returns a formatted date object, ala "Friday Feb. 20" | def last_update(blank):
today = date.today()
return today.strftime('%A %B %d') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_date():\n return datetime.datetime.today().strftime(constants.DATE_FORMAT)",
"def get_date_today() -> str:\n return datetime.now().strftime(\"%Y-%m-%d\")",
"def get_current_date(fmt=\"%Y-%m-%d\"):\n return datetime.datetime.now().strftime(fmt)",
"def get_date():\n now = da... | [
"0.7824893",
"0.7507471",
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"0.70509845",
"0.7046391",
"0.702825",
"0.6980741",
"0.69473976",
"0.694374... | 0.6653718 | 41 |
What's the current date and time? | def timestamp(blank):
today = datetime.today()
return today.strftime("%A %B %d, %-I:%M %p") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_time():\n return datetime.now()",
"def get_current_time():\n return datetime.datetime.now()",
"def current_time():\n now = datetime.now().strftime(\"%Y/%m/%d %H:%M:%S.%f\")\n return now",
"def current_time():\n return time.time()",
"def get_current_datetime ( ) :\... | [
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"0.7701877",
"0.7679058",
"0.76773113",
"0.7676556",
"0.7654265",
"0.765412",... | 0.0 | -1 |
Take a number such as 62 and return 62nd. 63, 63rd etc. | def ordinal_filter(value):
digit = value % 10
if 10 < value < 20:
o = 'th'
elif digit is 1:
o = 'st'
elif digit is 2:
o = 'nd'
elif digit is 3:
o = 'rd'
else:
o = 'th'
return '%d%s' % (value, o) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def last_digit(n):\n\n return n % 10",
"def get_dig_num(num, n = 1):\n digit = num//10**n%10 # this is the n-th digit, 0-indexed\n return digit",
"def digit(number: int, n: int) -> int:\n return number // 10 ** n % 10",
"def last_n_digits(num, n):\n return num%(10**n)",
"def get_nth_digi... | [
"0.7141755",
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"0.6578584",
"0.6549075",
"0.6525275",
"0.6459882",
"0.64534855",
"0.64322025",
"0.64104617",
"0.6395445",
"0.63594806",
"0.63561016",
"0.6331798",
"0.6282339",
"0.62483346",
"0.6241493",
"0.624059... | 0.0 | -1 |
Class constructor receives parameters to connect to the networkAPI. | def __init__(self, networkapi_url, user, password, user_ldap=None):
super(
RoteiroEquipamento,
self).__init__(
networkapi_url,
user,
password,
user_ldap) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, address):\r\n self.loop = asyncio.new_event_loop()\r\n self.device_address = address\r\n\r\n self.__api_address = \"http://\" + address if not address.startswith('http://') else address\r\n _LOGGER.debug(\"Api address: %s\", self.__api_address)\r\n self.model_r... | [
"0.6986114",
"0.6901052",
"0.6891296",
"0.6836612",
"0.6833463",
"0.6784958",
"0.67357445",
"0.671517",
"0.66976726",
"0.66885394",
"0.66739595",
"0.66732055",
"0.6663089",
"0.6645663",
"0.66445404",
"0.6629692",
"0.66234744",
"0.662254",
"0.66040313",
"0.66040313",
"0.657282... | 0.0 | -1 |
Sets the initial state of the device. | def _initialize_data(self):
self.reset_count = 0
self._idn_no_firmware = "KEPCO,BOP 50-20,E1234,"
self._firmware = 2.6
self._init_data() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initial_state(self, initial_state: QuantumCircuit) -> None:\n self._invalidate()\n self._initial_state = initial_state",
"def initialize(self):\n self.currState = self.startState",
"def resetDeviceStates(self):",
"def set_initial_state(self, initial_states, userdata):\n raise ... | [
"0.7504116",
"0.690324",
"0.6809412",
"0.67910904",
"0.67115295",
"0.6696538",
"0.6672575",
"0.6632381",
"0.6580465",
"0.6563067",
"0.6559918",
"0.64950496",
"0.64455366",
"0.642056",
"0.64086676",
"0.6338755",
"0.6338366",
"0.6336666",
"0.63347834",
"0.6327715",
"0.629293",
... | 0.0 | -1 |
Reset the device, reinitialising the data. | def reset(self):
self.reset_count += 1
self._init_data() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def device_reset(self):\n\t\tlogger.info('Device Reset')\n\t\tself.spi.writebytes([0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff])\n\t\tprint(DELIMITER*'-')",
"def resetDevice(self):\n reset_pkt = [START_BYTE_1, START_BYTE_2, RESET_MTYPE, 0x00, HEADER_SIZE + RESET_DATA_SIZE]\n reset_pkt.extend(RES... | [
"0.758682",
"0.75735646",
"0.7518953",
"0.7389302",
"0.73468316",
"0.71213037",
"0.70714784",
"0.69749707",
"0.6920218",
"0.6890552",
"0.68882835",
"0.683326",
"0.6819545",
"0.68126434",
"0.6779973",
"0.67533934",
"0.6750631",
"0.67285883",
"0.6715532",
"0.67061126",
"0.67061... | 0.75327283 | 2 |
Add all run parsers. | def add_subparser(
subparsers: SubParsersAction, parents: List[argparse.ArgumentParser]
) -> None:
run_parser = subparsers.add_parser(
"run",
parents=parents,
conflict_handler="resolve",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
help="Starts a Rasa server wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_command_parsers(self) -> None:\n for command in self.commands:\n self.get_command_parser(command)",
"def run_parse(self):\n # Data set already has source file names from load_inputs\n parsedset = {}\n parsedset['data_set'] = []\n for log in self.input_fil... | [
"0.6505622",
"0.641105",
"0.62048805",
"0.601413",
"0.60013145",
"0.589798",
"0.5883723",
"0.585354",
"0.5844148",
"0.58043873",
"0.564003",
"0.5558875",
"0.55376494",
"0.54931134",
"0.5423032",
"0.53750455",
"0.5364569",
"0.5346068",
"0.53338164",
"0.5296625",
"0.5261934",
... | 0.59972507 | 5 |
Entrypoint for `rasa run`. | def run(args: argparse.Namespace) -> None:
import rasa
args.endpoints = rasa.cli.utils.get_validated_path(
args.endpoints, "endpoints", DEFAULT_ENDPOINTS_PATH, True
)
args.credentials = rasa.cli.utils.get_validated_path(
args.credentials, "credentials", DEFAULT_CREDENTIALS_PATH, True
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_rasa_server(run_event):\n print('START RASA SERVER')\n if os.getenv('RASA_ACTIONS_URL') and len(\n os.getenv('RASA_ACTIONS_URL')) > 0:\n # ensure rasa endpoints file matches RASA_ACTIONS_URL env var\n endpoints_file = open(\n os.path.join(\n os.pat... | [
"0.75828344",
"0.6596486",
"0.6327994",
"0.6202171",
"0.6101385",
"0.6101385",
"0.6101385",
"0.60364085",
"0.6035912",
"0.603584",
"0.603292",
"0.6017248",
"0.599784",
"0.5949028",
"0.5909721",
"0.5909721",
"0.5909721",
"0.5909721",
"0.5909721",
"0.5909721",
"0.5909721",
"0... | 0.6522449 | 2 |
return ngrams as single spaceseparated strings with their occurences | def get(self, n):
parts = [' '.join(g) for g in ngrams(self.words, n)]
return get_occurences(parts) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _ngrams(self, string_):\n def find_ngrams(input_list, n):\n return zip(*[input_list[i:] for i in range(n)])\n\n ngrams = []\n tokens = string_.split()\n\n for size in range(1, self._ngram_range + 1):\n tuples = find_ngrams(tokens, size)\n concatenate... | [
"0.78278804",
"0.76701784",
"0.76432234",
"0.7412483",
"0.7388992",
"0.7383285",
"0.7343278",
"0.7261323",
"0.725827",
"0.7243522",
"0.72036326",
"0.7154389",
"0.7147983",
"0.71211857",
"0.71205693",
"0.70832944",
"0.70656013",
"0.70451486",
"0.70400804",
"0.6991556",
"0.6975... | 0.64976317 | 52 |
return most used noun_phrases | def get_phrases(self, first=10):
return get_occurences(self.lemmatized_phrases)[:first] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def most_words(self, n):\n return big_tags",
"def frequent_nouns_counts(self, tokens):\n\n unigram_fd = nltk.FreqDist(tokens)\n pos = POS()\n common_unigrams = unigram_fd.most_common(\n int(self.top_pct * len(unigram_fd)))\n\n nouns = [pair for pair in common_unigram... | [
"0.6768063",
"0.6764637",
"0.6511837",
"0.635036",
"0.63493806",
"0.6346821",
"0.6346821",
"0.63431996",
"0.6316743",
"0.63167274",
"0.6286206",
"0.62472194",
"0.62286603",
"0.6213437",
"0.62020063",
"0.6191109",
"0.61899865",
"0.61860347",
"0.6180162",
"0.6180162",
"0.617625... | 0.6017455 | 35 |
return most used words | def get_words(self, first=10):
return get_occurences(self.lemmatized_words)[:first] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_frequent_words(words, most_frequent): \n \n # common_words = Counter(sorted(words))\n # print common_words\n common_words = Counter(sorted(words)).most_common(most_frequent)\n print (common_words )\n most_common_words = [w for w, w_count in common_words]\n return most_common_words"... | [
"0.7717855",
"0.7710902",
"0.7526036",
"0.7515229",
"0.7457172",
"0.7383764",
"0.71464837",
"0.71408725",
"0.71277374",
"0.7127556",
"0.7100289",
"0.70912224",
"0.70876014",
"0.7072795",
"0.70574975",
"0.70387983",
"0.6993421",
"0.6988404",
"0.69723606",
"0.6950494",
"0.69486... | 0.0 | -1 |
lemmatize words in noun_phrases, exclude phrases with `STOPWORDS` | def lemmatized_phrases(self):
phrases = [set(lower_words(TextBlob(p).words.lemmatize()))
for p in self.blob.noun_phrases]
return [' '.join(p) for p in phrases if not STOPWORDS.intersection(p)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lemmatize_verbs(self):\n lemmas = []\n # lemmas = \"\"\n for word in self.words:\n lemma = wn.lemmatize(word, pos='v')\n lemmas.append(lemma)\n # lemmas += f\"{lemma} \"\n self.words = lemmas\n return self",
"def lemmatize_nouns(self, words):\n\t\tlemmatizer = WordNetLemma... | [
"0.73897773",
"0.73393244",
"0.72585356",
"0.7244815",
"0.71327686",
"0.69146174",
"0.68672264",
"0.68209785",
"0.6816726",
"0.6812039",
"0.6789802",
"0.6779173",
"0.67476636",
"0.6746864",
"0.6731405",
"0.6731262",
"0.67183787",
"0.6701651",
"0.66856176",
"0.66856176",
"0.66... | 0.8088282 | 0 |
downloading and renaming columns replace space with '_' | def download_datasets_csv(url):
# dataset = pd.read_csv(url, sep='\t')
dataset = pd.read_csv(url, sep=",")
dataset.columns = dataset.columns.str.replace(" ", "_")
return dataset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tidy_cols(my_csv):\n return [re.sub(\" \", \"_\", col.lower()) for col in my_csv.columns]",
"def _clean_up_table_column_names(loop_dict):\n \n # Make the column names all lowercase\n # and remove any underscores from the beginning\n for key in loop_dict.keys():\n rename_dict = { x:re.su... | [
"0.5891028",
"0.582513",
"0.5810645",
"0.5772185",
"0.5601228",
"0.558993",
"0.55504906",
"0.55083317",
"0.547578",
"0.5473249",
"0.54711485",
"0.54481345",
"0.5434032",
"0.54124486",
"0.53992754",
"0.5395076",
"0.5377607",
"0.532118",
"0.53113216",
"0.52825606",
"0.5271922",... | 0.586284 | 1 |
download and save historical + new dataset in complte dataset | def save_today_up_to_date_local_copy(url_hist, url_new):
df_historical = download_datasets_csv(url_hist)
df_new = download_datasets_csv(url_new)
# creating complete dataset
complete_dataset = df_historical.append(df_new, ignore_index=True)
# saving dataset
today = date.today().strftime("%d_%m_%Y... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_data_and_save():\n url = 'https://github.com/djay/covidthailand/wiki/combined.csv'\n s=requests.get(url).content\n global df\n global last_updated\n df=pd.read_csv(io.StringIO(s.decode('utf-8')), parse_dates= ['Date'])\n df.to_parquet(file_name, compression='UNCOMPRESSED')\n df.to_csv('jaydata.... | [
"0.7490032",
"0.70159507",
"0.6900648",
"0.673739",
"0.6722774",
"0.66758853",
"0.65556735",
"0.6517671",
"0.6412614",
"0.63694435",
"0.633438",
"0.6323673",
"0.63144374",
"0.62915635",
"0.62536067",
"0.6218456",
"0.6188079",
"0.61649686",
"0.61501503",
"0.61264765",
"0.61185... | 0.73869425 | 1 |
replacing data in Google Cloud Table | def replace_data_in_gbq_table(project_id, table_id, complete_dataset):
complete_dataset.to_gbq(
destination_table=table_id,
project_id=project_id,
credentials=credentials,
if_exists="replace",
)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replace_data(\n table_id: str = typer.Option(..., help=\"The id of the table\"),\n test: bool = typer.Option(False, help=\"Whether is a test or not\")\n) -> None:\n base_path = Path.cwd().parent\n\n tbl = bd.Table(dataset_id=\"br_cgu_servidores_executivo_federal\", table_id=table_id)\n output_pa... | [
"0.6006506",
"0.60053116",
"0.5916676",
"0.58872443",
"0.58701134",
"0.5751219",
"0.5727902",
"0.57181513",
"0.5715651",
"0.56847066",
"0.5664049",
"0.56344426",
"0.5623332",
"0.55554026",
"0.5548725",
"0.54871124",
"0.5472866",
"0.5461745",
"0.54568017",
"0.54271203",
"0.542... | 0.6709368 | 0 |
Get output for a finished job with id. | def getOutput(self):
self.checkBeforeGet()
# Get first job of the list
if not self.dontCheckSpaceLeft and not has_freespace(self.outDir, 10*1024): # First check for more than 10 Mb
msg = "You have LESS than 10 MB of free space on your working dir\n"
msg +="Please make som... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def job_output(self, job_id):\n\n url = self.base_url + \"/ml-service/phoenix-ml/output/findBy?jobId={0}\".format(job_id)\n headers = {\"ApiKey\": self.api_key}\n response = requests.get(url=url, headers=headers)\n return response.json()",
"def completed_job_info(self, jobid=None, out... | [
"0.748623",
"0.73491913",
"0.6980665",
"0.6841408",
"0.6526476",
"0.64631826",
"0.63375413",
"0.6295551",
"0.6289767",
"0.62885624",
"0.622371",
"0.61111414",
"0.60730827",
"0.59569275",
"0.59468514",
"0.59456295",
"0.5942398",
"0.5934416",
"0.59119844",
"0.5900385",
"0.58976... | 0.0 | -1 |
Parses the FJR produced by job in order to retrieve the WrapperExitCode and ExeExitCode. Updates the BossDB with these values. | def parseFinalReport(self, input):
from ProdCommon.FwkJobRep.ReportParser import readJobReport
codeValue = {}
jreports = readJobReport(input)
if len(jreports) <= 0 :
codeValue["applicationReturnCode"] = str(50115)
codeValue["wrapperReturnCode"] = str(50... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wsParseFJR(self):\n txt = '\\n#Written by cms_cmssw::wsParseFJR\\n'\n txt += 'echo \">>> Parse FrameworkJobReport crab_fjr.xml\"\\n'\n txt += 'if [ -s $RUNTIME_AREA/crab_fjr_$NJob.xml ]; then\\n'\n txt += ' if [ -s $RUNTIME_AREA/parseCrabFjr.py ]; then\\n'\n txt += ' ... | [
"0.64432395",
"0.5260613",
"0.52343196",
"0.52212924",
"0.519622",
"0.5182771",
"0.51703864",
"0.51282525",
"0.5100127",
"0.5092974",
"0.50409675",
"0.503647",
"0.5012631",
"0.50123405",
"0.49673423",
"0.49517173",
"0.49453866",
"0.49341914",
"0.49084947",
"0.49002942",
"0.48... | 0.71490014 | 0 |
Move output of job already retrieved into the correct backup directory | def moveOutput(self,id, max_id,path,file):
Dir_Base=path +'Submission_'
for i in range(1, max_id):
if not os.path.isdir( Dir_Base + str(i) + '/'):
cmd=('mkdir '+ Dir_Base + str(i) + '/ >& /dev/null')
cmd_out = runCommand(cmd)
commo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transfer_job_to_single_archive(wcl, saveinfo, dest, task_label):\n\n if miscutils.fwdebug_check(3, \"PFWRUNJOB_DEBUG\"):\n miscutils.fwdebug_print(\"TRANSFER JOB TO ARCHIVE SECTION\")\n archive_info = wcl['%s_archive_info' % dest.lower()]\n tstats = None\n if 'transfer_stats' in wcl:\n ... | [
"0.63789195",
"0.62199014",
"0.614267",
"0.6113023",
"0.60661507",
"0.6045541",
"0.5890307",
"0.5839137",
"0.5829081",
"0.58237654",
"0.57699376",
"0.57484376",
"0.5743507",
"0.57335913",
"0.570491",
"0.56820357",
"0.5673654",
"0.5669085",
"0.5664019",
"0.5630723",
"0.5622250... | 0.60767967 | 4 |
Split out the data set into its inputs and its labels. | def _split_inputs_outputs(self, data):
inputs = []
outputs = []
for point in data:
inputs.append(point[0])
outputs.append(point[1])
return np.array(inputs), np.array(outputs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_data(self):\n if not self.load_data:\n raise AttributeError('Preprocessor has not loaded any data.')\n \n # 3 - Find example counts for each set\n self.n_examples = self.data[0].shape[0]\n self.n_train = int(self.n_examples * self.train_ratio)\n self.n... | [
"0.68070143",
"0.6750992",
"0.6724709",
"0.6647016",
"0.66121227",
"0.65744203",
"0.65072405",
"0.64910185",
"0.6469287",
"0.6430547",
"0.6300627",
"0.62866193",
"0.62826365",
"0.62524253",
"0.6250792",
"0.6244052",
"0.6212815",
"0.6197",
"0.6156624",
"0.6151179",
"0.61149824... | 0.6575571 | 5 |
Method to classify a test data set and return the score in terms of accuracy. | def score(self, test_data):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score(self, test_data):\n\n\t\tins, outs = self._split_inputs_outputs(test_data)\n\n\t\t# One hot encode the input/labels\n\t\tencoder = LabelEncoder()\n\t\tencoder.fit(outs)\n\t\tenc_labels = encoder.transform(outs)\n\t\tenc_labels = np_utils.to_categorical(enc_labels)\n\n\t\t_, score = self.model.evaluate(in... | [
"0.7827106",
"0.7716559",
"0.76527333",
"0.7484252",
"0.7440777",
"0.74163395",
"0.73581105",
"0.73344773",
"0.7304312",
"0.7296065",
"0.7292504",
"0.7276412",
"0.7261127",
"0.7254433",
"0.72333145",
"0.7222515",
"0.7206312",
"0.7166098",
"0.7164757",
"0.71625596",
"0.7161631... | 0.0 | -1 |
Fit the knn model. | def _fit(self, data):
train_in, train_labels = self._split_inputs_outputs(data)
clf = KNeighborsClassifier(n_neighbors=self.k)
clf.fit(train_in, train_labels)
return clf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self):\n \n self.knn_model = KNeighborsRegressor(n_neighbors=self.k,\n weights='distance')\n self.knn_model.fit(self.X, self.y)",
"def fit(self, Xtrain, ytrain):\n self.Xtrain = Xtrain\n self.ytrain = ytrain\n\n max_nn ... | [
"0.82824767",
"0.7297423",
"0.7242588",
"0.7187846",
"0.69953674",
"0.69056904",
"0.689061",
"0.6822247",
"0.6819599",
"0.67986625",
"0.67709565",
"0.66812265",
"0.66537255",
"0.66494197",
"0.66305053",
"0.66141015",
"0.6599681",
"0.65994424",
"0.6562805",
"0.654726",
"0.6524... | 0.7031433 | 4 |
Return the accuracy attained by the knn on the test data set. | def score_one(self, test_data):
test_in, test_labels = self._split_inputs_outputs(test_data)
correct = 0
total = 0
for i, test_input in enumerate(test_in):
prediction = self.model.predict(test_input.reshape(1,-1))
if prediction[0] == test_labels[i]:
correct+=1
total+=1
return float(correct)/tot... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAccuracy(self):\n\t\tcorrect = (self.testing[self.classLabel]==self.bestLabel).sum()\n\t\tself.accuracy = (correct/float(len(self.testing))) * 100.0",
"def get_accuracy(self) -> float:\n self.network.load_data()\n self.network.train()\n\n n = len(self.network.y_test)\n correct ... | [
"0.81783843",
"0.807551",
"0.80026567",
"0.8001903",
"0.78935206",
"0.77839744",
"0.7777315",
"0.7738132",
"0.7703403",
"0.76944774",
"0.7688138",
"0.76269644",
"0.7613137",
"0.7607058",
"0.75859165",
"0.757926",
"0.7487052",
"0.74865884",
"0.7449901",
"0.7419433",
"0.7382690... | 0.0 | -1 |
Use 10fold CV to produce an average score | def score(self):
splits = 10
score = 0
kf = KFold(n_splits=splits, shuffle=True)
kf.get_n_splits(self.data)
for train_ind, test_ind in kf.split(self.data):
train = [self.data[ind] for ind in train_ind]
test = [self.data[ind] for ind in test_ind]
self.model = self._fit(train)
temp_score = self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cv_score(clf, x, y, score_func):\r\n result = 0\r\n nfold = 5\r\n for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times\r\n clf.fit(x[train], y[train]) # fit\r\n result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data\r\n ... | [
"0.7508548",
"0.68642884",
"0.686009",
"0.6607522",
"0.65247214",
"0.65219957",
"0.65063673",
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"0.65024954",
"0.6391796",
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"0.61726385",
"0.6169891",
"0.6133121",
"0.6118932",
"0.6067403",
"0.6057782",
... | 0.63403803 | 12 |
Fit the decision tree model. | def _fit(self, data):
train_in, train_labels = self._split_inputs_outputs(data)
clf = DecisionTreeClassifier(min_samples_leaf=0.05)
clf.fit(train_in, train_labels)
return clf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_decision_tree(model, x_train, y_train):\r\n model.fit(x_train, y_train)\r\n score = model.score(x_train, y_train)\r\n importance = model.feature_importances_\r\n return score, importance",
"def fit(self, X, y):\n #Initialize the tree with the given data\n tree = kd(X)\n s... | [
"0.7569848",
"0.74153537",
"0.72756153",
"0.72105074",
"0.6821379",
"0.67818767",
"0.662454",
"0.6596738",
"0.65849745",
"0.65849745",
"0.65598154",
"0.65543187",
"0.6544427",
"0.6542975",
"0.6537915",
"0.65235364",
"0.650521",
"0.65000254",
"0.6415684",
"0.64080507",
"0.6333... | 0.7340412 | 2 |
Return the accuracy attained by the knn on the test data set. | def score_one(self, test_data):
test_in, test_labels = self._split_inputs_outputs(test_data)
correct = 0
total = 0
for i, test_input in enumerate(test_in):
prediction = self.model.predict(test_input.reshape(1,-1))
if prediction[0] == test_labels[i]:
correct+=1
total+=1
return float(correct)/tot... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAccuracy(self):\n\t\tcorrect = (self.testing[self.classLabel]==self.bestLabel).sum()\n\t\tself.accuracy = (correct/float(len(self.testing))) * 100.0",
"def get_accuracy(self) -> float:\n self.network.load_data()\n self.network.train()\n\n n = len(self.network.y_test)\n correct ... | [
"0.8180346",
"0.80784214",
"0.8005831",
"0.80041337",
"0.7895488",
"0.77844036",
"0.77788",
"0.7739961",
"0.77059036",
"0.7696882",
"0.7691345",
"0.76276654",
"0.76155436",
"0.76090294",
"0.7584759",
"0.75794256",
"0.74891686",
"0.748881",
"0.7452785",
"0.7422463",
"0.7385018... | 0.0 | -1 |
Use 10fold CV to produce a score | def score(self):
splits = 10
score = 0
kf = KFold(n_splits=splits, shuffle=True)
kf.get_n_splits(self.data)
for train_ind, test_ind in kf.split(self.data):
train = [self.data[ind] for ind in train_ind]
test = [self.data[ind] for ind in test_ind]
self.model = self._fit(train)
temp_score = self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cv_score(clf, x, y, score_func):\r\n result = 0\r\n nfold = 5\r\n for train, test in KFold(y.size, nfold): # split data into train/test groups, 5 times\r\n clf.fit(x[train], y[train]) # fit\r\n result += score_func(clf, x[test], y[test]) # evaluate score function on held-out data\r\n ... | [
"0.7361523",
"0.6822073",
"0.67983013",
"0.67981595",
"0.679089",
"0.6743333",
"0.661887",
"0.6546391",
"0.6534486",
"0.64706314",
"0.6468548",
"0.6424231",
"0.6401285",
"0.6376128",
"0.6340506",
"0.6315139",
"0.6280791",
"0.6203428",
"0.61945695",
"0.61933863",
"0.6171417",
... | 0.6448152 | 11 |
Fit the logistic regression model. | def _fit(self):
clf = LogisticRegression()
clf.fit(inputs, labels)
return clf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_reg(x_train, y_train):\n\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\n log_reg_classifier.fit(x_train, y_train)\n return log_reg_classifier\n\n # log_reg_classifier.fit(x_train, y_train)",
"def _fit(self, _X, _y):\n\n self.model = linear_model.LogisticRegre... | [
"0.7822972",
"0.7692005",
"0.7548392",
"0.7395676",
"0.7204495",
"0.7157888",
"0.71509266",
"0.70614314",
"0.70379573",
"0.69379884",
"0.69329995",
"0.68996924",
"0.6820621",
"0.68176067",
"0.6782893",
"0.67820376",
"0.6755673",
"0.6706559",
"0.67048633",
"0.6691573",
"0.6682... | 0.7538954 | 3 |
Return the score obtained on the test data set | def score(self, test_data):
ins, outs = self._split_inputs_outputs(test_data)
return self.model.score(ins, outs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def score(self, test_data):\n\n\t\tpass",
"def score(self, test_data):\n\n\t\tins, outs = self._split_inputs_outputs(test_data)\n\n\t\t# One hot encode the input/labels\n\t\tencoder = LabelEncoder()\n\t\tencoder.fit(outs)\n\t\tenc_labels = encoder.transform(outs)\n\t\tenc_labels = np_utils.to_categorical(enc_lab... | [
"0.86668295",
"0.7818388",
"0.77717274",
"0.77294904",
"0.76660264",
"0.76660264",
"0.76569784",
"0.73830205",
"0.7382572",
"0.7316532",
"0.7287335",
"0.72611284",
"0.7224763",
"0.7224763",
"0.7218049",
"0.7218049",
"0.7199401",
"0.7164144",
"0.71476907",
"0.71476907",
"0.714... | 0.8328699 | 1 |
Train the neural network using Adam optimizer | def train(self):
input_size = len(self.inputs[0])
output_size = len(set(self.labels))
hidden_size_1 = 15
hidden_size_2 = 15
# One hot encode the labels
encoder = LabelEncoder()
encoder.fit(self.labels)
enc_labels = encoder.transform(self.labels)
enc_labels = np_utils.to_categorical(enc_labels)
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def trainNet():",
"def train():\n rng = random.PRNGKey(0)\n\n # Get Zachary's karate club graph dataset.\n node_feats, node_labels, sources, targets = get_karate_club_data()\n\n # Create model and optimizer.\n _, initial_params = GNN.init(\n rng, node_x=node_feats, edge_x=None, sources=sources, targets... | [
"0.71472293",
"0.70600724",
"0.70026565",
"0.69949657",
"0.6988527",
"0.6967045",
"0.6955149",
"0.6940348",
"0.694033",
"0.68917704",
"0.6864978",
"0.68499064",
"0.6846929",
"0.6829985",
"0.6814839",
"0.68050236",
"0.68016535",
"0.6799331",
"0.6787858",
"0.67825234",
"0.67695... | 0.0 | -1 |
Return the accuracy attained by the neural network on the test set | def score(self, test_data):
ins, outs = self._split_inputs_outputs(test_data)
# One hot encode the input/labels
encoder = LabelEncoder()
encoder.fit(outs)
enc_labels = encoder.transform(outs)
enc_labels = np_utils.to_categorical(enc_labels)
_, score = self.model.evaluate(ins, enc_labels, verbose=2)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_accuracy(self) -> float:\n self.network.load_data()\n self.network.train()\n\n n = len(self.network.y_test)\n correct = 0\n for i in range(n):\n # Predict by running forward pass through the neural network\n pred = self.network.predict(self.network.x... | [
"0.8605905",
"0.815202",
"0.8070769",
"0.8068605",
"0.8035914",
"0.80330867",
"0.7938776",
"0.7906472",
"0.7895872",
"0.78683805",
"0.78303057",
"0.77969366",
"0.7765335",
"0.7745827",
"0.7736295",
"0.77029103",
"0.7656342",
"0.76442033",
"0.7615893",
"0.7611401",
"0.76058733... | 0.0 | -1 |
Python model creator fro tensor implementation in java Args | def __init__(self, dim=20, nIter=5, lamb=0.05, alph=40,
user_features=["user"], item_features=["item"]):
self.setParams(dim,nIter, lamb, alph)
self.user_features = {}
self.item_features = {}
self.factors = {}
self.user_column_names = user_features
self.i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_model():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('--DISC_LR', type=float, default=1e-4)\r\n parser.add_argument('--GEN_LR', type=float, default=1e-3)\r\n parser.add_argument('--GEN_BETA1', type=float, default=0.9)\r\n parser.add_argument('--GEN_BETA2', type=float, default=0.9... | [
"0.67856354",
"0.6725397",
"0.6558513",
"0.65427756",
"0.6461063",
"0.64487284",
"0.63853717",
"0.6380155",
"0.6334473",
"0.63143593",
"0.62861764",
"0.6284722",
"0.6238022",
"0.62292343",
"0.6205176",
"0.6196909",
"0.6196012",
"0.61855704",
"0.6158863",
"0.61315936",
"0.6114... | 0.0 | -1 |
Return parameter details for dim, nIter, lamb and alph | def paramDetails(cls):
return {
'dim': (10, 20, 2, 20),
'nIter': (1, 10, 2, 5),
'lamb': (.1, 1., .1, .05),
'alph': (30, 50, 5, 40)
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setParams(self,dim=20, nIter=5, lamb=0.05, alph=40):\n self._dim = dim\n self._nIter = nIter\n self._lamb = lamb\n self._alph = alph",
"def show_parameters(self):\n with np.printoptions(precision=3, suppress=True):\n print('number of wind phase = {}'.format(self.... | [
"0.66055083",
"0.65783185",
"0.6249512",
"0.60537255",
"0.6009132",
"0.60000795",
"0.599074",
"0.5983164",
"0.59602505",
"0.59599835",
"0.59563273",
"0.5954735",
"0.5952508",
"0.59498894",
"0.59248936",
"0.5918452",
"0.5881164",
"0.58748",
"0.5865431",
"0.5844908",
"0.5821393... | 0.74654466 | 0 |
Java Float Matrix is a 1D array writen column after column. Numpy reads row after row, therefore, we need a conversion. | def _float_matrix2numpy(self, java_float_matrix):
columns_input = java_float_matrix.toArray()
split = lambda lst, sz: [numpy.fromiter(lst[i:i+sz],dtype=numpy.float)
for i in range(0, len(lst), sz)]
cols = split(columns_input, java_float_matrix.rows)
matri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def matrixToFloatMatrix(matrix):\n\n float_matrix = [matrix(i, j) for i in xrange(4) for j in xrange(4)]\n\n outMatrix = OpenMaya.MFloatMatrix()\n OpenMaya.MScriptUtil.createFloatMatrixFromList(float_matrix , outMatrix)\n\n return outMatrix",
"def itemsToFloat(self):\n returnva... | [
"0.7274915",
"0.66002345",
"0.6478091",
"0.62763405",
"0.59943956",
"0.58819616",
"0.58508307",
"0.58382297",
"0.58107084",
"0.5799",
"0.57688916",
"0.57163143",
"0.5654517",
"0.56448877",
"0.56354064",
"0.5616841",
"0.5608916",
"0.55774224",
"0.5566227",
"0.5564168",
"0.5546... | 0.7982614 | 0 |
Set the parameters for the TensorCoFi | def setParams(self,dim=20, nIter=5, lamb=0.05, alph=40):
self._dim = dim
self._nIter = nIter
self._lamb = lamb
self._alph = alph | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_parameters(self, **kwargs):\n self.__multi_layer_perceptron.set_params(**kwargs)",
"def set_parameters(self,params):\n K3Supervisor.set_parameters(self,params)\n self.gtg.set_parameters(self.parameters)\n self.avoidobstacles.set_parameters(self.parameters)\n self.wall.s... | [
"0.7162809",
"0.6975917",
"0.69572663",
"0.69175005",
"0.67739886",
"0.6729018",
"0.67094076",
"0.66425633",
"0.6621363",
"0.65588087",
"0.6556437",
"0.6530796",
"0.6530384",
"0.6530384",
"0.6520774",
"0.65118974",
"0.6503824",
"0.64620966",
"0.6427614",
"0.64271015",
"0.6417... | 0.58768946 | 71 |
Instantiate this submodule with the given UI. | def __init__(self, UI):
super(COTInjectConfig, self).__init__(UI)
self._config_file = None
self._secondary_config_file = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, ui: UI):\n super().__init__(ui)",
"def __init__(self):\r\n super().__init__()\r\n self.init_ui()",
"def init_ui(self):\n raise NotImplementedError",
"def init_ui(self):\n raise NotImplementedError",
"def init_ui(self):\n raise NotImplementedError... | [
"0.71629465",
"0.6812497",
"0.65096986",
"0.65096986",
"0.6493672",
"0.6486345",
"0.63511",
"0.63335776",
"0.62438047",
"0.62001234",
"0.6143851",
"0.6113456",
"0.60962325",
"0.60550797",
"0.6031183",
"0.60106504",
"0.5976868",
"0.5973087",
"0.59472775",
"0.59101176",
"0.5889... | 0.0 | -1 |
Do the actual work of this submodule. | def run(self):
super(COTInjectConfig, self).run()
vm = self.vm
platform = vm.platform
# Find the disk drive where the config should be injected
# First, look for any previously-injected config disk to overwrite:
if platform.BOOTSTRAP_DISK_TYPE == 'cdrom':
(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n obj = UnityFilesystem()\n obj.perform_module_operation()",
"def __gitSubmodulesSync(self):\n self.vcs.gitSubmoduleSync(self.project.getProjectPath())",
"def do_workload(self):\n module_manager = self._core.get_module_manager()\n module = module_manager.get_module_by_nam... | [
"0.66442853",
"0.62968755",
"0.6259206",
"0.624318",
"0.6181432",
"0.61801285",
"0.6152305",
"0.6148519",
"0.61186683",
"0.61186683",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",
"0.6116955",... | 0.0 | -1 |
Add subparser for the CLI of this submodule. | def create_subparser(self, parent, storage):
p = parent.add_parser(
'inject-config',
help="Inject a configuration file into an OVF package",
usage=self.UI.fill_usage("inject-config", [
"PACKAGE -c CONFIG_FILE [-o OUTPUT]",
"PACKAGE -s SECONDARY... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extend_cli(self, subparser):",
"def add(cls, subparsers):\n subparser = subparsers.add_parser(\n name=cls.__tool_name__(),\n description=cls.__get_description__())\n\n cls.__add_arguments__(subparser)\n subparser.set_defaults(func=cls.from_args)\n return subp... | [
"0.8356323",
"0.7613267",
"0.7532559",
"0.75132465",
"0.74843043",
"0.7461825",
"0.7458073",
"0.7289405",
"0.71724516",
"0.7165431",
"0.7135265",
"0.7031047",
"0.6956534",
"0.6943993",
"0.69056773",
"0.688262",
"0.68790203",
"0.6860334",
"0.6815078",
"0.67114484",
"0.67108387... | 0.5897082 | 100 |
Check that all protected branches are a single index unicity of name version must be increased `force` option ignored | def check_pkg_consistency():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def commit_check(ctx):\n result = ctx.run(f\"{VENV_PREFIX} cz check --rev-range master..\", warn=True)\n if result.exited == 3: # NO_COMMIT_FOUND\n exit(0)\n else:\n exit(result.exited)",
"def validate(cfg: defs.Config) -> List[str]: # noqa: C901\n res: List[str] = []\n\n def check... | [
"0.5562095",
"0.5386104",
"0.5354058",
"0.53346205",
"0.5217734",
"0.5204411",
"0.51927465",
"0.5189931",
"0.5149217",
"0.5106516",
"0.5092081",
"0.5082172",
"0.50748366",
"0.5031404",
"0.49861875",
"0.4984854",
"0.4967",
"0.49571994",
"0.49304572",
"0.49161586",
"0.48917702"... | 0.0 | -1 |
Lists all the blobs in the bucket. | def gcs_list_blobs(bucket_name):
storage_client = client #storage.client()
bucket = storage_client.get_bucket(bucket_name)
blobs = bucket.list_blobs()
blob_list = []
for blob in blobs:
blob_list.append(blob.name)
return blob_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_blobs(bucket_name):\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n return blobs",
"def list_blobs(bucket_name):\n # bucket_name = \"your-bucket-name\"\n\n storage_client = ... | [
"0.81295836",
"0.80809796",
"0.8063129",
"0.80104107",
"0.79991764",
"0.7891442",
"0.771796",
"0.7614527",
"0.757374",
"0.7504958",
"0.74911094",
"0.74169266",
"0.73137933",
"0.72736335",
"0.7269783",
"0.72240984",
"0.7204592",
"0.7182615",
"0.7155751",
"0.71367675",
"0.71151... | 0.7789013 | 6 |
Downloads a blob from the bucket. | def gcs_download_blob(bucket_name, source_blob_name, destination_file_name):
storage_client = client #storage.Client()
bucket = storage_client.get_bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_blob(bucket_name, source_blob_name):\n\n storage_client = storage.Client()\n bucket = storage_client.bucket(bucket_name)\n blob = bucket.blob(source_blob_name)\n\n return blob",
"def blob_download(blob_url):\n blob = storage.Object.from_url(blob_url)\n blobc = blob.download()\n ... | [
"0.8135551",
"0.8126195",
"0.7890022",
"0.78339165",
"0.7721649",
"0.77189684",
"0.7649907",
"0.7573001",
"0.7502306",
"0.74736196",
"0.74572617",
"0.7345375",
"0.73441243",
"0.72784907",
"0.7213689",
"0.7128273",
"0.7065729",
"0.6937288",
"0.6928252",
"0.6894445",
"0.6828534... | 0.70821893 | 16 |
Current user will place order | def post(self, request, format=None):
inventory_id = request.data.get('inventory_id', None)
user_id = None
if hasattr(request, 'user'):
user_id = request.user.id
if inventory_id is not None and user_id is not None:
try:
inventory_item = self.nv_m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def place_order(self):\n\n order_total = self.get_order_total()\n\n if self.person.can_afford(order_total):\n print 'This person is stinkin rich!'\n else:\n print \"No soup for you!\"",
"def test_order_by_user(self):\n self.fill_session_cart()\n self.clien... | [
"0.70376885",
"0.6521009",
"0.6342922",
"0.6326408",
"0.6183782",
"0.6176169",
"0.6154756",
"0.606741",
"0.60295796",
"0.60066897",
"0.60049874",
"0.60049874",
"0.60049874",
"0.5994649",
"0.59906405",
"0.59855217",
"0.59850776",
"0.5975906",
"0.5965155",
"0.59345704",
"0.5929... | 0.0 | -1 |
Given a ManagedProcess protobuf instance, create the corresponding ProcessNode and insert it into the graph. | def add_node(self, managed_process_pb):
node = ProcessNode(managed_process_pb)
if node.name not in self.nodes:
self.nodes[node.name] = node
self.logger.info('Created node for [{}]'.format(node.name))
else:
self.logger.error(
'Detected request ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_node_process(name, properties):\n process = NodeProcess(name)\n DB.session.add(process)\n DB.session.commit()\n for p in properties:\n prop = NodeProcessProperty(process.id, p[0], p[1], p[2])\n DB.session.add(prop)\n DB.session.commit()\n return process",
"def add_proce... | [
"0.6773554",
"0.64495486",
"0.60057384",
"0.5933061",
"0.5871152",
"0.58102006",
"0.5744983",
"0.57229316",
"0.5719331",
"0.56770027",
"0.5667292",
"0.5577523",
"0.5570289",
"0.55650806",
"0.55393213",
"0.55308384",
"0.5441535",
"0.5393277",
"0.5389408",
"0.5335206",
"0.52840... | 0.7839599 | 0 |
Reset all adjacency information and rebuild it after adding nodes. This implements a collection of sanity checks (making sure every subscribed topic has a publisher, etc.) which, if they all pass, mean we can safely / sanely construct the dependency graph. | def build(self):
self.logger.info('Rebuilding adjacency information')
self.edges = collections.defaultdict(list)
topic_to_publisher = collections.defaultdict(list)
topic_to_subscribers = collections.defaultdict(list)
node_to_missing_deps = collections.defaultdict(list)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _reset_state(self):\n # Directed graph, (u, v) => v depends on u. u, v are pairs of (rule_name, rule_dir_abs)\n # Used for generating Topological Sort\n self._rule_to_dependency_graph_adjlist = {}\n self._topologically_sorted_build_rule_names = []\n\n # List of (dependency_na... | [
"0.65324295",
"0.63045096",
"0.6301467",
"0.6183333",
"0.6102479",
"0.6050887",
"0.6023047",
"0.5987564",
"0.5974988",
"0.58748007",
"0.5874257",
"0.58559334",
"0.5806248",
"0.5778933",
"0.5771964",
"0.5762822",
"0.57474977",
"0.57438475",
"0.57006",
"0.5695009",
"0.56947756"... | 0.7615647 | 0 |
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